chiron-ai

From Prompt to Product: Turning HPU Intent into CPU Execution

Thu, 26 Mar 2026 13:32:36 GMT

BLUF: The real value of generative AI is not in the answers it gives but in the durable capability it helps you build.

From HPU intent to CPU Execution (Image by J Eselgroth w/GenAI)

A model spent eight minutes trying to transcribe a video. It failed. Five minutes later, a simple app solved the problem permanently.

That moment stayed with me. Not because the model failed. Because it exposed how most people use generative AI. They treat it like a vending machine. Insert a prompt. Receive an answer. Move on. That pattern feels productive. It is also misleading. Real leverage is not consuming intelligence on demand. Real leverage is building something that keeps working after the model stops.

The Shift Beneath the Surface

A few weeks ago, I wrote about the new physics of productivity . Work now moves across three layers. The HPU, or Human Processing Unit, is where judgment lives. You frame the problem. You weigh tradeoffs. You set intent. The GPU, or Generative Processing Unit, is where creation happens. Code gets written. Patterns emerge. Options multiply. The CPU, or Classical Processing Unit, is where execution scales. Systems run. Workflows fire. Outputs repeat at low cost.

HPU, GPU, CPU, where should you route the work to? (Image by J Eselgroth w/Gen AI)

Most people move from HPU to GPU and stop. They think the answer is the outcome. It is not. The outcome is what runs after the answer.

Where Most People Stop

The pattern is familiar. Open a model. Type a prompt. Get a response. Close the tab. Every time the task returns, you repeat the cycle. Every failure costs time, tokens, and attention. That is not a workflow. That is a dependency loop.

The real opportunity sits one layer deeper. Use the HPU to frame the problem. Use the GPU to generate the artifact. Then push the result into the CPU layer. Let it run on its own. No more round trips. No more token burn. The generative layer is not the destination. It is the bridge.

"The generative layer is not the destination. It is the bridge.

A Real Example Under Pressure

I saw this play out after finishing a six-week course. I recorded each session using Google Meet. For most classes, I turned on captions. I never turned on transcripts. That turned out to be fine. The caption layer gave the models enough to work with later. But for two sessions, I forgot to enable captions entirely. That small miss created a real problem.

I needed transcripts from every recording. The first few were easy. I dropped the MP4 files into ChatGPT. It found the caption layer and returned clean text. Fast. Almost invisible. That is usually when people stop thinking about the process.

Then I hit the two files without captions. The model started searching for tools. It tried different approaches. It worked long enough to build false hope. Then it failed. I tried another model. Same result. One-hour video files are heavy. The task had become a full speech-to-text problem. The path through the model was slow, unreliable, and burning through tokens with nothing to show for it.

I was also running out of time. A family trip was hours away. Urgency sharpens your judgment fast. The wrong workflow becomes painfully obvious when the clock is moving. I started searching the internet for a solution. Some tools wanted five or ten dollars. Others were free. That kind of free always raises a question. If you are not paying, your data is the product. I did not want course recordings sitting on a random server. I did not want to create an account with a service I would never use again. None of it felt right.

So I changed the question. I stopped looking for a service. I asked for a tool. I prompted for a simple local application. Drag and drop an MP4. Extract the audio. Generate a transcript. Run it in Docker. Keep everything on my machine.

In minutes, I had a Python script, a small UI, and a working runtime. I dropped in the two problem files. The app processed them. I got the transcripts. Problem solved. Reusable.

Screenshots of my new video to transcript app

From Answer to Asset

That experience crystallized something important. Generative AI is not just an answer engine. It is a capability factory. Instead of paying for intelligence every time, you pay once. You use it to build a mechanism that keeps doing the job. You use the GPU to create something that operates at the CPU layer. That is a fundamentally different move. You are not generating answers. You are generating infrastructure.

The Economic Shift

Tokens are not free. Latency is not free. Failed attempts are definitely not free. There is also a quieter cost. Sensitive data flowing into tools you do not control. People treat these costs as minor. Each interaction feels small. But small repeated costs compound into workflow drag. They become friction. They become the tax you pay for never moving past the prompt. And most people do not even realize they are paying it.

The next productivity shift is not about better prompting. It is about knowing when to stop prompting and start building. Some tasks belong in the generative layer. Brainstorming lives there. Early drafting lives there. Exploration lives there. But repeatable tasks are different. Time-sensitive tasks are different. The moment a task shows up twice, ask whether it deserves to become a tool.

Tailorability at the Tactical Level

This connects to an idea I explored in delivering tailorability to the tactical level . For years, we designed technology around central platforms. Long implementation cycles. Vendor roadmaps. Capability was something you procured, not something you built at the edge. That logic is weakening. The person closest to the problem can now shape the solution directly. Governance still matters. Architecture still matters. But the distance between need and capability is collapsing. The edge is getting smarter, faster.

Right now, today, we are experimenting and in some cases developing production in the safe tailorability segment (Image by J Eselgroth w/GenAI)

The Trap: Tools Are Not Systems

There is a trap here worth naming. A one-off app is not a system. Solving a single problem is useful. Supporting a team or a business requires more. You need data models. Storage. Telemetry. Access controls. Transaction history. You need time-series data to support prediction and learning. Machine learning does not run on intuition. It runs on structured, historical data. If you never capture that data, you cannot improve over time. Generative tools can get you surprisingly far on the first build. But serious operating capability still requires thoughtful design.

"Machine learning does not run on intuition. It runs on structured, historical data.

Systems thinking matters more now, not less. Creation speed has increased. The need for intent and structure has not. When building is easy, knowing what should exist becomes the harder skill. Without that discipline, you end up with clever tools and no capability stack.

What Changes Next

The real shift is behavioral. Once you build your first tool, your instincts change. You stop hunting for solutions. You stop comparing subscriptions. You stop waiting for someone else to ship the feature you need. You start asking a different question. Could I just build this? And once you ask that question once and the answer is yes, you never go back to the old pattern.

That question changes everything. It moves you from consumer to builder. From passenger to operator. From someone who waits for capability to someone who generates it.

We are not entering an age of better prompts. We are entering an age of self-generated capability. The individuals and organizations that win will not be the best prompters. They will be the ones who move fastest from HPU intent, through GPU creation, into CPU execution. The model is not the product. What you build with it is.

Better Decisions Don’t Start with More Data

Tue, 10 Mar 2026 14:23:00 GMT

BLUF | Insight comes from the intersection of the right data and multiple perspectives, not from either alone.

More data does not produce better decisions. Better questions do.

That distinction matters. The federal government has invested heavily in the machinery of data-driven decision making. Agencies have hired Chief Data Officers. The Evidence Act of 2018 set new expectations for how data supports policy. Open data plans, performance reviews, customer feedback loops, and AI governance frameworks now operate across dozens of agencies.

The infrastructure is real. The results are uneven.

Teams accumulate dashboards, reports, models, and metrics. They still struggle to answer basic operational questions. Volume rises. Decision confidence does not.

Recent federal guidance signals a shift. OMB’s 2025 Phase 2 Evidence Act guidance treats data maturity as a governance challenge, not a collection target. It calls for systematic approaches to data access, inventories, quality, and usability [1]. OMB’s 2025 memo on broadening participation states that meaningful engagement is foundational to informed government decision-making [2]. OMB Circular A-11 Section 260 defines data-driven reviews as a management routine proven to produce better results [3].

The pattern is clear. Washington is moving past “get more data” toward “govern data well, broaden perspective, and connect both to outcomes.”

That is exactly what the Data Driven Matrix describes.

The Two Axes That Shape Insight

The matrix rests on two dimensions: data maturity and perspective.

The vertical axis represents data maturity. At the bottom sits the data an organization currently holds. Moving upward represents data that could exist but has not been collected, integrated, or made usable. Maturity is not volume. It is structure, quality, accessibility, and fitness for purpose. OMB’s Evidence Act guidance reinforces this. It emphasizes timeliness, completeness, consistency, accuracy, and availability as markers of real data readiness [1].

The horizontal axis represents perspective. Every analysis passes through a lens. That lens is shaped by training, role, incentives, and institutional habit. Teams with similar backgrounds ask similar questions. They validate similar assumptions. The result is analytical tunnel vision that feels rigorous because everyone agrees.

OMB’s participation guidance names the antidote directly. Broader engagement gives agencies greater insight into the lived experiences and perspectives of communities [2]. That is not a soft add-on. It is how the questions improve.

Better perspective sharpens the questions. Better questions reveal what data actually matters. The intersection of these two axes produces four organizational states.

Figure 1: The Data Driven Matrix

Street-Level Confidence

Few insights, more bias

Most teams start here. They use the data they have. They rely on the perspectives already in the room. The work is sincere. The scope is narrow.

It is like standing on one street corner and explaining how the whole city works. You notice what passes in front of you. You build a theory. Your assumptions harden faster than your understanding.

This quadrant produces confidence before it produces clarity.

Everything Everywhere All at Once

More data, fewer answers

When confidence cracks, teams reach for more data. New systems connect. Dashboards multiply. Warehouses expand. The environment grows dense and expensive.

If perspective has not changed, additional data amplifies noise. Collection outpaces interpretation. The organization becomes better at gathering inputs without becoming better at using them.

Federal guidance points to the corrective. A-11 Section 260 does not call for reviewing everything. It focuses on a limited set of performance priorities, using recent evidence to diagnose problems and adjust course [3]. The lesson: disciplined focus beats maximal visibility.

This is where most “data-driven” efforts stall.

Let’s Ask Around

More insights, less bias

The breakthrough comes from broadening perspective, not broadening collection.

Operators explain how the process actually works. Policy experts surface legal constraints. Customers describe what the system feels like from the receiving end. Cross-functional partners challenge definitions that had gone unquestioned.

The result is not more opinions. It is better framing.

OMB’s participation memo reinforces this move. Public input shapes priorities and leads to more responsive policies and programs [2]. A-11 Section 280 makes a parallel case. It treats customer feedback as central to service improvement and calls for actionable data that informs design decisions [3].

This quadrant reveals something important. Many decision problems are not informational. They are interpretive. Fix the frame, and the data search sharpens.

What Matters

Moving Across the Matrix

Most organizations start on the left. They work with limited data and limited perspective, or they compensate by adding data without changing how they think. Progress comes from moving diagonally. Expand perspective. Improve data quality and relevance together. Structured decision processes make that movement deliberate. The FORGED framework sequences the work:

• Frame clarifies the decision and its context.

• Organize establishes trusted data, definitions, and boundaries.

• Refine separates signal from noise.

• Generate produces decision-ready artifacts.

• Engage tests interpretation with stakeholders.

• Deploy embeds insight into operational workflows.

The sequence matters. It does not begin with “gather everything.” It begins with clarity. Then structure. Then interpretation. Then validation. Then action.

Figure 2: The FORGED Process Mapped to the Data Driven Matrix

The same logic now appears in federal AI guidance. OMB’s 2025 AI memo directs agencies to pair innovation with governance and public trust. It calls for risk management on high-impact AI and feedback from end users and the public [4]. Even in the newest policy domain, the message holds: better tools still require better judgment and broader perspective.

Why This Matters

In government, bad decisions are never abstract. They become delayed services. Misallocated funding. Weak oversight. Missed mission outcomes.

The real challenge is not becoming more data driven. It is becoming more decision capable.

Data without perspective produces noise. Perspective without evidence produces untethered intuition. Durable decision advantage comes from combining both on purpose.

That is the journey the Data Driven Matrix maps. From Street-Level Confidence to What Matters. Not through more data alone, but through better questions, broader perspective, and evidence that is usable when decisions must be made.

To see where your organization sits on the Data Driven Matrix, try the self-assessment at [link] .

References

[1] Office of Management and Budget, M-25-05: Phase 2 Implementation of the Foundations for Evidence-Based Policymaking Act of 2018 — Open Government Data Access and Management Guidance, January 2025.

[2] Office of Management and Budget, M-25-07: Broadening Participation and Engagement, January 2025.

[3] Office of Management and Budget, OMB Circular A-11: Preparation, Submission, and Execution of the Budget, August 2025. (Sections 260, 280, 290, and Part 6.)

[4] Office of Management and Budget, M-25-21: Accelerating Federal Use of AI through Innovation, Governance, and Public Trust, February 2025.

The New Physics of Productivity

Tue, 24 Feb 2026 14:12:03 GMT

BLUF | Productivity is no longer about who works hardest. It is about where work goes, and whether that decision is deliberate.

Figure 1: The future of work is not a workforce reduction. It is a workforce elevation. One person governs what thousands of machines execute. That is not a smaller role. It is a bigger one. Image by GenAI wJ Eselgroth

We Are Asking the Wrong Question

Most organizations frame this as an AI problem. It is not.

It is a routing problem. Work lands where it has always landed, not where it should land. People absorb tasks that machines could handle. Machines run processes that humans should govern. AI tools get deployed without a clear answer to the most basic operational question: who, or what, should be doing this?

The data confirms the gap. The World Economic Forum reports that 47% of enterprise tasks are performed mainly by humans, 22% mainly by technology, and 30% by a combination of both.[1] Most leaders look at those numbers and see a workforce story. The more useful read: nearly a third of work is already in a hybrid execution state, and organizations are managing that reality without a routing architecture.

The question is no longer whether machines participate in work. They already do. The question is whether that participation is designed or accidental.

Three Layers. One Decision.

For decades, organizations had two execution options. Automation or humans. The structure was simple.

The first option was the CPU layer, the Central Processing Unit of organizational work. Rule-based. Deterministic. Reliable within fixed parameters. Payroll calculations, file routing, workflow triggers, system integrations. The CPU layer does exactly what it is told. It does not interpret, adapt, or reason. That is its strength and its limit.

The second option was the HPU layer, the Human Performance Unit. Judgment. Accountability. Ambiguity resolution. Humans handled everything the CPU layer could not. That included high-stakes decisions. It also included an enormous volume of routine cognitive work that defaulted to people simply because no other option existed. Summarizing reports. Triaging requests. Drafting first-pass communications. Comparing policies. Work that consumed capacity without requiring genuine judgment.

That constraint no longer holds.

A third layer now exists between the CPU and HPU. This is the GPU layer, the Graphics Processing Unit of organizational reasoning. Not the chip in a data center. The operational analogy: a system built to run massive parallel interpretation at scale. Generative AI, predictive models, classification engines, large-scale language inference. These systems handle probabilistic reasoning, pattern recognition, language synthesis, and decision support. They are not rule-followers. They are not humans. They are a new execution class.

Three layers. One architectural decision for every task your organization runs:

CPU Layer (Central Processing Unit): Deterministic execution. Structured inputs. Fixed rules. High reliability, zero interpretation. Payroll, scheduling, system integrations, automated workflows.

GPU Layer (Graphics Processing Unit): Machine reasoning. Probabilistic inference. Pattern recognition. Language and context interpretation. Drafting, summarization, triage, classification, anomaly detection, first-pass analysis.

HPU Layer (Human Performance Unit): Human judgment. Ambiguity resolution. Ethical tradeoffs. Strategic accountability. Decisions that carry consequence, require unique context, or demand clear ownership.

F igure 2: The Gravity Stack visualizes the core routing principle. Tasks enter the system and fall toward the lowest cognition layer that can safely execute them. Most land in the CPU layer at the base. Image by GenAI wJ Eselgroth

Interpretive and pattern-recognition work settles in the GPU layer. Only the decisions that carry real consequence, ambiguity, or accountability rise to the HPU tier at the top. The physics are simple. The organizational discipline to design around them is not. Source: Chiron AI.

The terminology is not cosmetic. It forces a specific operational question. For any given task, which layer owns it? That question, asked consistently, is the difference between a productivity strategy and a technology spending plan.

Work Has a Gravitational Pull

There is a simple law operating underneath all of this.

Tasks migrate toward the lowest cognition layer that can safely execute them. CPU execution is the cheapest. GPU execution is the next cheapest. HPU execution is the most expensive. That cost gradient creates a persistent gravitational pull. Over time, work flows downstack as automation improves, AI reasoning matures, and organizational trust in machine outputs grows.

The problem is that most organizations let this happen passively. Work migrates when someone builds a workaround. When an employee discovers a tool on their own. When a vendor sells a point solution into one team. There is no routing architecture. There is no deliberate design.

Organizations that take routing seriously ask a different question before any process is staffed. Which layer should handle this? What is blocking it from getting there? Is the obstacle technical, economic, regulatory, or cultural? That question, asked upstream, changes the economics of every workflow downstream.

"Organizational effectiveness is not defined by how much AI an organization owns. It is defined by how consistently work is intercepted and routed before it reaches a human desk."

The chart below shows how this migration has evolved and where the trajectory points by 2030.

Figure 3: Work Routing Across Cognition Layers (1995-2030). Illustrative model based on published automation and AI adoption trends. Image by J Eselgroth

Humans Are Not Being Replaced. They Are Being Elevated.

This is the part most leaders misread.

As CPU and GPU layers absorb more execution, HPU involvement shrinks in volume. It grows in consequence. Humans make fewer decisions. Each decision they make carries more weight.

Microsoft and LinkedIn's 2024 Work Trend Index found that 75% of knowledge workers now use AI at work.[2] Even so, the average time savings among regular users is 30 to 60 minutes per day. AI is present across most workflows. It is embedded deeply in very few. The GPU layer is expanding. It has not yet captured the cognitive middle. That migration is still in progress.

The signal for leaders is not headcount reduction. It is role elevation. HPU decisions are becoming rarer, more expensive per unit, and more strategically consequential. The humans you retain need to operate at a higher level. Building toward that is the actual workforce strategy.

Why Adoption Lags Capability

Technical feasibility is not the governing constraint. Economics is.

MIT's research on automation cost-effectiveness found that even when AI can technically execute a task, the cost of integration, data preparation, process redesign, and governance often makes human execution still the rational choice.[3] The gap between what AI can do and what organizations actually deploy is not a technology gap. It is an economic and operational one.

McKinsey estimates that generative AI could technically automate 60 to 70% of employee time. Actual adoption scenarios put 50% automation of work activity somewhere between 2030 and 2060.[4] The physics are real. The timeline is constrained by data quality, regulatory exposure, organizational readiness, and the unglamorous work of process redesign.

AI experimentation moves fast. Operational embedding moves slowly. Both are true at the same time. Planning around only one of them produces the wrong strategy.

The Question Leaders Should Be Asking

Most transformation initiatives start with the wrong prompt: "Where can we deploy AI?"

The more useful prompt: "Which cognition layer should own this task, and what is preventing it from getting there?"

That reframe changes everything. It moves the conversation from tools to architecture. From adoption rates to routing decisions. From generic AI strategy to specific operational design.

For each significant workflow, the routing audit covers four questions. What is the cognitive complexity of each task inside this process? What is the cost of executing it at each layer? What regulatory or governance constraints shape the options? What would it take to move it one layer down?

Modern leadership increasingly resembles traffic control. The goal is not to maximize activity inside any one layer. It is to ensure work reaches the most efficient reliable execution point. That is the job.

Where the Distribution Is Heading

The future state is not a uniform redistribution across three layers.

It looks more like a dual distribution. Large volumes of structured, repeatable work concentrating in the CPU layer at minimal cost. A rapidly expanding GPU layer handling the interpretive, language-heavy, pattern-recognition middle. A narrower but more consequential HPU layer where humans govern decisions that carry real organizational weight.

Task volume and cognitive authority move in opposite directions. The CPU layer dominates on volume. The HPU layer dominates on consequence. The GPU layer is the dynamic middle where the most significant transformation is currently happening and where the most competitive advantage is being built or missed.

Figure 4: Dual Distribution Model of Organizational Wor k Routing. Task volume concentrates in deterministic automation. Decision authority concentrates in human judgment. The GPU layer spans the interpretive middle where AI delivers the highest leverage. Image by GenAI wJ Eselgroth

The organizations that outperform will not be those with the most AI licenses or the largest technology budgets. They will be the ones that most deliberately answer the routing question for every significant workflow they operate.

For most of management history, productivity meant getting people to work faster.

In the next decade, it will mean deciding which work should never reach people at all.

That decision does not make itself.

References

[1] World Economic Forum. Future of Jobs Report 2025. "Jobs Outlook: Task-Level Distribution." Geneva: WEF, 2025. weforum.org/publications/the-future-of-jobs-report-2025/

[2] Microsoft and LinkedIn. Work Trend Index Annual Report 2024. "AI at Work: What the Data Says." Redmond: Microsoft, 2024. news.microsoft.com/source/2024/05/08/microsoft-and-linkedin-release-the-2024-work-trend-index-on-the-state-of-ai-at-work/

[3] Svanberg, M., Li, W., et al. "Beyond AI Exposure: Which Tasks are Cost-Effective to Automate with Computer Vision?" SSRN, 2024. papers.ssrn.com/sol3/papers.cfm?abstract_id=5233833

[4] McKinsey Global Institute. "The Economic Potential of Generative AI: The Next Productivity Frontier." McKinsey and Company, 2023. mckinsey.com/capabilities/tech-and-ai/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier

[5] World Economic Forum. Future of Jobs Report 2023. Geneva: WEF, 2023. weforum.org/publications/the-future-of-jobs-report-2023/

Sticker Shock is Costing You Billions

Tue, 10 Feb 2026 20:17:07 GMT

BLUF: The most expensive decision is not the one with the big price tag. It is the one that looked safe and cost you years.

What's the real cost of deciding only by the price tag? (Image by J Eselgroth with Gen AI)

A few years ago, I published a series exploring the tension between cost and capability in organizational decision-making. The core question still holds. But the conversation has matured. So has the framework.

Here is what has not changed: leaders still flinch at the price tag.

The Flinch

Eight years ago, a leader rejected a $250 million upgrade across 50,000 assets. The number was too big. The organization chose a safer path instead. Today, they are no closer to solving the original problem. The opportunity cost is likely in the billions.

Inspired by True events, pitching an idea and being denied due to sticker shock (Image by J Eselgroth with Gen AI)

This pattern plays out everywhere. Public sector. Private sector. Every week.

In a similar, yet different scenario, a leader chose the “easy” option over a slightly more "expensive" one. The team then spent years burning hours and sinking costs trying to reach the same outcome. They never got there.

The problem is not budget discipline. Budget discipline is healthy. The problem is mistaking the sticker price for the total cost. And mistaking a narrow solution for a real capability.

Two Axes, One Diagnostic

Most cost conversations measure the wrong things. They focus on acquisition price and feature count. Neither tells you what the investment actually delivers over its lifecycle.

A better diagnostic uses two axes.

The vertical axis measures Cost , Total Ownership Burden. Not just the purchase price. The full weight: retooling the workforce, integration surprises, process disruption, consumption blind spots, and the opportunity cost of delayed deployment. Every dollar you do not see on the invoice still shows up in the mission.
The horizontal axis measures Capability , Force-Wide Utility. How many problems does this capability solve? How broadly can the organization adopt it? How fast does it reach value? A tool serving one team is a tool. A capability serving the enterprise is an investment.

Plot these two axes and you get four quadrants. Each one tells a different story.

The new 2 by 2 Cost vs Capability Matrix (Image by J Eselgroth)

The Four Quadrants

The Sticker Shock Trap. Safe to buy. Expensive to own.

Low price. Narrow utility. The organization chose it because the number felt responsible. Training takes longer than expected. Adoption stalls. Integration requires workarounds nobody budgeted for. The hidden costs compound quietly. The safe choice becomes the most expensive one.

Bespoke Complexity. Built for one. Sustained by many.

High cost. Still narrow. These are the expensive one-offs built for a specific niche. They cannot scale. They require specialized training and constant maintenance. Shadow IT lives here. So do most legacy platforms no one can sunset.

Strategic Genesis. Expensive today. Essential tomorrow.

High cost. High potential. This is the R&D space. Experimental, multifaceted, and risky. Every organization needs some investment here. But the goal is never to stay. The goal is to mature these capabilities and push them toward broader adoption.

Force-Wide Utility. Easy to adopt. Hard to outgrow.

Lower total ownership burden. High versatility. This is the sweet spot. These capabilities are easy to adopt. Near zero-touch deployment. Fuel efficient. Data efficient. Energy efficient. They solve problems across the enterprise, not just for one team. Economies of scale live here. So does real return on investment.

Capabilities naturally evolve across these quadrants. Simon Wardley mapped this evolution from genesis to commodity. The same logic applies here. The leader’s job is not just to buy. It is to accelerate the migration path from experimental to essential.

The Conversation Worth Having

When a leader asks "why does this cost so much," they are asking a budget question. The answer must be a readiness statement. One that is outcome focsed.

From Sticker Shock to a Capabilities Based Decision (Image by J Eselgroth with Gen AI)

Organizations have to stop buying artifacts and start investing in outcome-generating capacity. This requires radical transparency about the Total Ownership Burden. Look past the sticker price and see the invisible tail.

Does this require months of retraining, or is it near-zero-touch familiarization? What is the hidden cost in power, data, and sustainment nobody scoped in the original pitch? If the safer option takes three years to reach the force and the versatile one takes three months, the "savings" are a decision failure.

In a world accelerating every day, the real cost is measured in mission outcomes the organization failed to achieve while waiting for the "safe" system to orient.

Bring this framework to your next review. Plot the options. Compare Total Ownership Burden against Force-Wide Utility. Stop measuring technical milestones. Start mapping readiness timelines. Make the invisible costs visible.

This is where Decision Intelligence earns its weight. Plotting options on a 2x2 is a starting point. Understanding the second, third, and fourth-order effects of each option is where real clarity lives. A structured action-to-outcomes approach connects the sticker price to the total ownership burden, surfaces the hidden dependencies, and traces every investment decision to its downstream impact on people, process, and mission readiness. The framework exists. The question is whether your organization is willing to use it.

The goal is not to spend more. The goal is to invest with clarity.

From Reactive to Augmented: Building Decision Capability

Tue, 03 Feb 2026 14:47:59 GMT

BLUF | The Decision Capability Matrix shows where you stand. This article shows how to move. The 5Ps provide the foundation. The 5Cs of Intelligent Transformation provide the path to Augmented Intelligence.

From Diagnosis to Action

In the previous article, Mapping Decision Capability: Where Does Your Agency Stand? , we introduced the Decision Capability Matrix. The matrix plots organizations across two dimensions: Human Governance and AI Capability. Most federal agencies find themselves in one of three suboptimal quadrants: Reactive, Intuition-Led, or Automation-Led.

The destination is the upper-right quadrant: Augmented Intelligence . This is where human judgment and machine intelligence work together within clear governance structures. But knowing the destination is not the same as knowing how to get there.

The journey requires two things: a solid foundation and a deliberate transformation method.

The Foundation: 5Ps of Digital Transformation

Before organizations can pursue Intelligent Transformation, they need a solid digital foundation. This is where the 5Ps come in. The 5Ps, People, Policy, Process, Partners, and Platforms, represent the essential infrastructure for any digital initiative.

The 5P | Digital Transformation Foundation
People | Skills, roles, change management, and workforce readiness
Policy | Governance, compliance, security, and decision rights
Process | Workflows, automation candidates, and operational efficiency
Partners | Internal collaboration and external expertise
Platforms | Technology infrastructure, data systems, and integration

Organizations that neglect the 5Ps will struggle to advance. If your platforms cannot integrate data, your AI investments will underperform. If your policies do not address AI governance, you will face compliance gaps. If your people lack the skills to work with AI, adoption will stall. The 5Ps are not optional. They are the prerequisite.

However, the 5Ps alone are not sufficient to reach Augmented Intelligence. They establish the foundation for digital transformation, but the journey to Intelligent Transformation requires something more.

The Path Forward: 5Cs of Intelligent Transformation

The 5Cs build on top of the 5Ps. Where the 5Ps establish digital infrastructure, the 5Cs enable intelligent decision-making. The 5Cs of Intelligent Transformation are:

The 5C's Table, the what, and the path (Image by J Eselgroth)

Cognition | Cognition establishes clarity about what the organization is trying to achieve and how decisions will be made. It defines outcomes, decision rights, and guardrails. It aligns AI capabilities with strategic objectives. Without cognition, organizations deploy technology without purpose.

The building blocks of an Intelligent Enterprise from 5Ps through the 5Cs (Image by J Eselgroth with GenAI)

Capability | Capability builds the human skills required to work with AI effectively. This is not just technical training. Leaders must understand how to set intent, interpret outputs, and override when necessary. The workforce must develop judgment about when to trust machine recommendations and when to question them.

Culture | Culture shapes the behaviors and incentives that make data-driven decision making stick. Technology adoption fails when it conflicts with how people are rewarded. Culture change requires visible leadership commitment, aligned incentives, and patience. It is the hardest C to change and the most important to sustain.

Connectivity | Connectivity integrates data sources, platforms, processes, and workflows. Interoperability enables automation. Connected systems allow insights to flow to where decisions happen. Without connectivity, intelligence stays trapped in silos.

Continuity | Continuity establishes the governance, risk management, and resilience structures that sustain progress. It embeds ethics, security, and continuous improvement into decision processes. Without continuity, organizations backslide. Early gains erode. The effort hump reappears.

Gartner's recent Magic Quadrant for Decision Intelligence Platforms reinforces continuity as essential. Carlie Idoine, Vice President Analyst at Gartner, states: "Global competition, geopolitical disruptions, and growing customer demands are putting unprecedented pressure on organizations to clarify and accelerate their decision making. Enterprises can no longer afford decisions that lack transparency or accountability." Decision stewardship, the practice of governing decisions with the same rigor as data, is what Gartner calls the "big deal" for 2026.

The Benefits of Achieving Augmented Intelligence

Organizations that achieve Augmented Intelligence gain decision advantage. In competitive or adversarial environments, this advantage is existential.

Win-Win Prioritization. When decision-makers have both human judgment and AI-generated insights, prioritization becomes collaborative rather than political. Data surfaces the largest issues. Human judgment weighs context and consequences. Stakeholders align around evidence rather than hierarchy.

Focused Budgeting. Augmented Intelligence makes resource allocation more precise. Leaders can see which investments will generate the greatest mission impact. Subjective arguments give way to informed debates about priorities and trade-offs.

Improved Conflict Resolution. Large initiatives generate disagreements. When challenges arise, Augmented Intelligence helps decision-makers focus on the situation rather than the personalities. Being more objective and less subjective helps teams understand issues, evaluate options, and anticipate consequences.

Decision Advantage and the OODA Loop. Most importantly, organizations with Augmented Intelligence complete the OODA loop, Observe, Orient, Decide, Act, faster than competitors or adversaries can reach their second O. This is the speed of relevance. Clearer understanding of second, third, and fourth-order effects improves decision quality. Faster cycle time improves decision speed. The combination delivers decision advantage: better decisions, made faster, with greater confidence in outcomes.

Gartner quantifies this advantage. They predict that by 2030, explicitly modeled business decisions will be five times more trusted and 80% faster than ungoverned decisions. This reflects what happens when organizations move from opaque recommendation engines to transparent, governed decision architectures. The benefits compound: trust enables adoption, adoption enables scale, scale enables impact.

From Matrix to Mission

The Decision Capability Matrix (Below) shows where an organization stands. The 5Cs show how to move. But movement requires context about where the organization is going.

The Decision Capability Matrix, achieving Augmented Intelligence (Image by J Eselgroth)

The Digital Efficiency Matrix (Below) provides that context. It maps organizational maturity across efficiency and digital capability, from Laggards through Implementers to Leaders . Augmented Intelligence is what Leaders do . It is the decision-making capability that distinguishes organizations at the top of the maturity curve.

The Digital Efficiency Matrix from Laggards to Leaders of the Intelligent Enterprise (Image by J Eselgroth)

The destination is the Intelligent Enterprise: an organization where intelligence, both human and artificial, is embedded in every process, decision, and interaction. Decision Intelligence provides the orchestration. The 5Cs provide the method. Augmented Intelligence provides the capability.

The Path Forward

The current policy environment demands action. EO 14179 and M-25-21 push agencies to accelerate AI adoption. M-25-22 shapes how agencies acquire AI. M-26-04 sets expectations for transparency and accountability. The pressure to move fast is real.

But speed without direction is dangerous. Agencies that deploy AI without Decision Intelligence will struggle to demonstrate the public trust these policies require. They will accumulate tools without outcomes. They will automate processes without improving decisions.

Agencies that deploy AI without Decision Intelligence will struggle to demonstrate the public trust these policies require.

The Decision Capability Matrix provides the map. The 5Cs provide the method. Augmented Intelligence provides the destination. Digital transformation was the beginning. Intelligent Transformation is the bridge. The Intelligent Enterprise is where we are headed.

The agencies that build this capability will lead. The agencies that do not will struggle to keep pace with a world that moves faster every day. The choice is not whether to pursue Augmented Intelligence. The choice is whether to pursue it deliberately or stumble toward it haphazardly.

The 5Cs offer a deliberate path.

References

Eselgroth, J. (2026). Mapping Decision Capability: Where Does Your Agency Stand? Chiron AI.
Pidsley, D. (2026). Decision Intelligence Platforms. Gartner Magic Quadrant.
Eselgroth, J. (2025). The Unending Quest for Efficiency: Navigating Beyond Digital Transformation. Chiron AI. https://www.chironai.io/the-unending-quest-for-efficiency-navigating-beyond-digital-transformation
Eselgroth, J. (2025). Delivering Tailorability to the Tactical Level in the Age of AI. Chiron AI. https://www.chironai.io/delivering-tailorability-to-the-tactical-level-in-the-age-of-ai
Eselgroth, J. (2025). Decision Intelligence: The Missing Piece in AI Orchestration? Chiron AI. https://www.chironai.io/decision-intelligence-the-missing-piece-in-ai-orchestration

Mapping Decision Capability: Where Does Your Agency Stand?

Tue, 27 Jan 2026 14:42:08 GMT

BLUF | Federal agencies are racing to adopt AI, but most lack the decision capability to use it effectively. The Decision Capability Matrix reveals where organizations actually stand, and why technology alone is not enough.

What Path are you choosing when making decisions? (Image by J Eselgroth w/GenAI)

The Landscape Has Shifted

Federal AI policy has pivoted sharply. Executive Order 14179, signed in January 2025, established a national policy of American AI "dominance" and revoked prior AI directives. OMB Memorandum M-25-21 replaced earlier guidance with a framework emphasizing innovation, streamlined governance, and reduced compliance burden. M-26-04 added requirements for "truth-seeking" and "ideological neutrality" in federal AI procurement.

The policy intent is clear: accelerate AI adoption, remove barriers, and favor American-made solutions. Yet acceleration without decision discipline creates new risks. Agencies that deploy AI without clear decision rights, accountability structures, and outcome alignment will struggle to demonstrate the public trust these memoranda require.

The Foundations for Evidence-Based Policymaking Act established the mandate for data-driven government. The current framework establishes the urgency. What remains missing for many agencies is the operational capability to connect AI investments to mission outcomes.

That capability is Decision Intelligence .

Decision Intelligence: The Orchestration Layer

Decision Intelligence is not a technology or a maturity model. As defined by Gartner, it is "a practical discipline that advances decision making by explicitly understanding and engineering how decisions are made and how outcomes are evaluated, managed, and improved via feedback." Dr. Lorien Pratt, a pioneer in this field, frames Decision Intelligence as the action-to-outcome mechanics connecting data, technology, and human insight.

This is no longer an emerging concept. In January 2026, Gartner published its first Magic Quadrant for Decision Intelligence Platforms, marking the transition from emerging technology to a recognized market category. As David Pidsley, Gartner's Decision Intelligence Leader, noted: organizations are moving beyond the "data-driven" dogma toward a genuinely "decision-centric" vision for operations.

Rita Sallam, Gartner's Distinguished VP Analyst and Chief of Research for Data and Analytics, captures the challenge precisely: "Data and analytics leaders aren't short of insight—they're short of control over how insight turns into action. As AI scales, the real challenge is no longer producing better analysis, but governing, measuring, and improving the decisions that analysis feeds."

In the context of AI orchestration, Decision Intelligence serves as the glue that holds everything together. It addresses the human element, what I call the "squishy things" like subjectivity and intuition, that are often overlooked in AI implementations. When faced with finding a needle in a haystack, Decision Intelligence helps us "burn the hay", eliminating irrelevant data and focusing efforts on the most valuable information.

Decision Intelligence operates at the intersection of data science, behavioral science, and organizational design. It answers questions that data alone cannot: Who has authority to decide? What information is required? What are the consequences of being wrong? How fast must we move? What feedback loops exist to improve over time?

Without Decision Intelligence, organizations collect data but do not use it to its fullest potential. They build models but don’t completely trust them. They create dashboards that are rarely checked. Decision Intelligence is the discipline that closes the gap between information and action.

Decision Intelligence is the bridge to improved outcomes (made by J Eselgroth & GenAI)

The Decision Capability Matrix

Federal leaders make decisions based on information from two sources: human judgment shaped by experience, expertise, and context; and machine intelligence shaped by data, algorithms, and computational power. The relationship between these sources determines an organization's decision-making capability.

This can be mapped across two dimensions. The first is Human Governance : the extent to which human judgment, accountability structures, ethics, and guardrails guide decisions. The second is AI Capability : the extent to which artificial intelligence, including automation, prediction, generation, and autonomous systems, supports or executes decisions.

The Decision Capability Matrix (Image by J Eselgroth)

Understanding the Quadrants

Reactive (Low Human Governance, Low AI Capability)

Organizations in this quadrant operate on hope. Decisions are ad hoc, crisis-driven, or simply not made. Without focus from either human leadership or machine intelligence, outcomes depend on luck. Leaders face budget overruns, costly rework, and cycles wasted on irrelevant tasks. Disagreements about priorities consume more energy than execution.

Some agencies have also arrived here through a different path: they purchased AI tools without governance frameworks. They have capability without direction.

Random acts of AI produce random results.

Intuition-Led (High Human Governance, Low AI Capability)

These organizations have built something valuable: clear accountability, strong professional judgment, and leaders whose experience has guided sound decisions for years. This is not a weakness. It is a foundation.

The opportunity is scale. Human attention is finite. When every significant decision flows through senior leaders, the organization cannot move faster than its most experienced people can think. This is not a criticism of those leaders. It is a recognition that their expertise is a bottleneck precisely because it is so trusted.

Organizations cannot move faster than its most experienced people can think

Many leaders in these organizations approach AI with healthy skepticism. They have seen technology fail before. They have watched implementations promise transformation and deliver disruption. That skepticism is earned. It also reflects something important: these leaders understand that decisions carry consequences, and they take that responsibility seriously.

The path forward is not to replace that judgment. It is to amplify it. Augmented Intelligence extends the reach of experienced leaders, allowing their expertise to inform more decisions, faster, without sacrificing the governance that makes those decisions trustworthy.

Automation-Led (Low Human Governance, High AI Capability)

Here organizations have invested heavily in AI and automation. Dashboards proliferate. Models run. But governance has not kept pace. Decisions emerge from black boxes. Accountability is unclear. When the algorithm is wrong, no one knows why or how to fix it.

This quadrant creates compliance risk. M-25-21 requires AI governance boards, risk management for high-impact AI, and documented AI strategies. M-26-04 mandates vendor documentation through model and system cards. Organizations that cannot demonstrate accountability and transparency will face increasing scrutiny. Checkbox AI, where tools are deployed to satisfy mandates rather than improve decisions, lives here.

The stakes are quantifiable. Gartner predicts that by 2027, 25% of ungoverned decisions using large language models will cause financial or reputational loss due to human biases, insufficient critical thinking, and AI sycophancy.

Governance is not optional.

Augmented Intelligence (High Human Governance, High AI Capability)

The upper-right quadrant represents the destination. Human judgment and machine intelligence work together within clear governance structures. Leaders set intent and guardrails. AI handles pattern recognition, prediction, and routine decisions. Humans handle exceptions, ethics, and strategy.

This is not about humans versus machines. It is about humans with machines. The combination outperforms either alone. Research consistently shows that human-AI teams achieve better outcomes than pure automation or pure human judgment. The key is proper integration, and Decision Intelligence provides the orchestration layer that makes integration work.

It is about humans with machines. The combination outperforms either alone.

What Comes Next

Knowing where you stand is the first step. The Decision Capability Matrix provides the diagnostic. But diagnosis without treatment is incomplete.

In the next article, From Reactive to Augmented: Building Decision Capability, we explore how to move from any quadrant toward Augmented Intelligence. The journey requires both a solid digital foundation, what I call the 5Ps, and a deliberate transformation method, the 5Cs of Intelligent Transformation.

The question is not whether your agency will pursue AI. The question is whether you will build the decision capability to make AI work.

References

Gartner Glossary: Decision Intelligence. https://www.gartner.com/en/information-technology/glossary/decision-intelligence
Pidsley, D. (2026). Decision Intelligence Platforms. Gartner Magic Quadrant. https://www.linkedin.com/pulse/decision-intelligence-platforms-david-pidsley-gelne/
Eselgroth, J. (2025). Decision Intelligence: The Missing Piece in AI Orchestration? Chiron AI. https://www.chironai.io/decision-intelligence-the-missing-piece-in-ai-orchestration

From Systems-of-Systems to Capabilities-of-Capabilities

Tue, 13 Jan 2026 15:20:17 GMT

BLUF: Systems engineering built the tools. Capabilities win the outcomes. Decision Intelligence orchestrates both.

From System to Capability (Image by J Eselgroth w/Gen AI)

The Integration Trap

Systems engineering changed how we build complex things. It gave us architecture, modularity, and integration discipline. It helped us land spacecraft and connect global networks. But it also created a blind spot.

We became so focused on systems we forgot what they exist to produce.

Programs fail not because systems break. They fail because no one asked whether the system could actually deliver an outcome. A fighter jet without pilots, maintenance crews, fuel logistics, and training pipelines is not a capability. It is an expensive artifact.

The GAO has tracked this pattern for decades. In its 2025 assessment, the agency reported that major defense acquisition programs now take almost 12 years from start to deliver even an initial capability. DOD plans to invest $2.4 trillion across 106 of its costliest programs. Most pass every systems engineering gate. They still fail to deliver operational capability on time.

This pattern extends beyond defense. Seventy percent of digital transformation initiatives fail to meet their objectives. According to a RAND study, 80 percent of AI projects fail. BCG researchers found success depends 70 percent on people and ways of working. Only 30 percent depends on technology.

The problem is not bad engineering. The problem is scoping the wrong unit.

Capability Is the True Delivery Unit

A capability is the practical ability to achieve a defined outcome under real constraints. It emerges from the coordinated use of equipment, people, training, and supporting elements. Systems are tools. Capability is what those tools make possible.

Think about an F-16 and F-35. They are not just two aircraft. They represent two points on a capability axis. Each requires different pilots, different maintenance, different doctrine, and different logistics. The aircraft alone delivers nothing. Capability emerges when the system combines with trained operators and support.

The same logic applies to a pistol and a sniper rifle. The weapon matters. The shooter matters more. A trained sniper with the right rifle, range data, and spotting support produces precision engagement. A novice with the same rifle produces nothing useful.

Example Capabilities (Image by J Eselgroth w/Gen AI)

Consider a data scientist. The capability is not the analytics platform. It is a trained analyst, clean data pipelines, governed data sources, compute infrastructure, and business context working together. The outcome is actionable insight. The platform alone produces dashboards no one trusts.

Or consider an accountant. The capability is not the ERP system. It is a credentialed professional, documented processes, internal controls, reporting standards, and audit trails. The outcome is reliable financial statements. The software alone produces numbers without integrity.

Organizations do not field systems. They field capabilities.

Why Systems-of-Systems Falls Short

Systems-of-systems thinking improved technical composition. It enabled interoperability and modular design. These are genuine advances. But the model remains anchored in technical artifacts.

Real delivery depends on more than artifacts. It depends on people who know how to operate them. It depends on training pipelines producing those people. It depends on consumables arriving where they are needed. It depends on doctrine guiding employment. It depends on leadership making sound decisions under uncertainty.

None of these elements live inside a systems architecture. They live around it. Systems-of-systems thinking does not have a place for them.

This is the blind spot. Programs optimize the technical layer. They underinvest in the human and organizational layers. Then leaders wonder why fielded systems fail to produce outcomes.

Capabilities-of-Capabilities as the Reframe

Shifting to a capabilities-of-capabilities lens changes everything. The unit of analysis becomes outcome-generating capacity. The question becomes "what can we achieve?" not "what can we integrate?"

This reframe treats capabilities as composable, measurable, and improvable. A mission outcome often requires multiple capabilities acting in concert. Air superiority requires strike, surveillance, refueling, and command capabilities working together. Each one depends on systems, people, training, and logistics.

Composing capabilities is a decision problem. Prioritizing them is a decision problem. Sequencing investments across them is a decision problem. This is where decision intelligence enters the picture.

Leaders operating at this level ask different questions. What capability am I buying? What capability am I retiring? What capability gap creates the most risk? These questions drive smarter investments than "what system do we need?"

Implications for Leaders

Adopting a capability lens changes procurement, planning, and measurement. Procurement shifts from buying systems to buying outcome-generating capacity. Planning shifts from integration schedules to readiness timelines. Measurement shifts from technical milestones to operational effectiveness.

This is not a rejection of systems engineering. It is an elevation. Systems engineering remains essential for building the tools. Capability thinking ensures those tools produce outcomes.

The leaders who grasp this distinction will outperform those who do not. They will spend less chasing systems that never deliver. They will invest more in the people, training, and logistics enabling real capability.

The Takeaway

Stop asking what system you need and start asking what capability you are building.

Sources:

GAO-25-107569, "Weapon Systems Annual Assessment," June 2025
Gartner/BCG analysis on digital transformation failure rates, 2025
RAND Corporation / SHRM, "Prioritize Human Factors: The Hidden Key to AI Project Success," October 2024

Act Before They Orient

Tue, 06 Jan 2026 04:22:20 GMT

BLUF: Decision advantage means completing your Observe-Orient-Decide-Act cycle before your competitor finishes their second O. The tools exist. The question is whether you're using them.

Complete your OODA Loop before your competitor (Image by J Eselgroth with Gen AI)

Decisions are only as good as your ability to make them at the speed of need.

The OODA loop, developed by military strategist John Boyd, remains one of the most durable frameworks for understanding decision advantage. Observe, Orient, Decide, Act. Simple in concept. Brutal in execution. Boyd's insight was that the competitor who cycles through OODA faster doesn't just react quicker. They force the other side into perpetual confusion. If you complete your loop before your adversary finishes orienting, they're always guessing. You've seized the initiative. They're responding to a reality that no longer exists.

This isn't doctrine for fighter pilots. It's the operating logic of 2026.

The Real Failure Mode

Here's what most organizations get wrong: they assume the information-gathering process is fixed. That synthesis takes as long as it takes. That the path from raw data to decision-ready insight can't be meaningfully compressed.

It can. Technology is the nitrous oxide.

GPUs, CPUs, AI, and intelligent automation exist to collapse the loop. Not to generate more reports. Not to build prettier dashboards. To crash the time between a question and an answer. Between signal and synthesis. Between context and decision.

Years ago, I sat with a brigadier general who framed the challenge perfectly. "Jim, when a decision lands on my desk, I have to make it. That's why I have this star. But the reality? I might have 30% of the information I need. And typically zero understanding of the second, third, and fourth-order effects of that decision." He paused. "Wouldn't it be something if I could get to 50%? And maybe 10% visibility into the downstream consequences? That would be phenomenal."

That's the gap. Not late decisions. Decisions made on schedule but with degraded context. The insight arrived too late, or never arrived at all, so intuition filled the void.

When your OODA loop can't keep pace with decision deadlines, you don't postpone the decision. You make it anyway, with partial signal, stale context, or gut instinct standing in for synthesis. That's where decision advantage is won or lost. Not in the quality of the decision itself, but in whether relevant insight was present when the decision had to be made.

Speed doesn't delay action. It determines whether insight shows up in time.

Why OODA Feels Slow

The loop isn't broken. It's stretched.

Observe takes too long because data exists but signal doesn't. Orient requires too much human stitching. Decide waits on synthesis. Act inherits the accumulated delay. Every phase is elongated by manual tasks, analog handoffs, and processes that are digital but not intelligent.

I've seen this firsthand. In 2018, I contributed to a 93-slide executive briefing for a four-star general. My piece was two or three slides. But watching the full effort unfold was revealing. So I ran the numbers.

Total preparation cost: $241,881. Total preparation time: 772 hours, nearly five full work months. Fourteen meetings and dry runs. And the developer-to-bureaucracy ratio? 1:3. For every hour spent creating content, three hours were spent reviewing, vetting, or updating it.

The deck accomplished its goal. It informed leadership. But it was fundamentally a rearview-mirror exercise. Awareness, not action. Insight into what had happened, not what should happen next. Enormous energy devoted to building artifacts of understanding, with little investment in converting those artifacts into decisions.

That's the stretch. Not broken. Bloated.

The Decision Supply Chain

OODA describes the cycle. The Decision Supply Chain describes the infrastructure that makes the cycle fast or slow.

Think of it this way: knowledge alone is inert. A body of knowledge, whether policies, doctrine, market data, or operational history, only becomes useful when it's contextualized. Context transforms raw knowledge into decision-driven data. Decision-driven data enables action. Action produces outcomes. Outcomes feed back into knowledge.

This flow is identical whether you're running a business or executing a mission. Different contexts. Same system.

The supply chain exists whether you design it or not. If you don't own it, friction will. And as General Gordon Sullivan wrote in Hope Is Not a Method, hope is precisely that: not a method. When you don't deliberately design your decision supply chain, you're not just accepting friction. You're placing blind hope where architecture should be.

Most organizations have fragmented supply chains. Data lives in silos. Context is reconstructed manually for every decision. Synthesis depends on individual analysts with institutional knowledge locked in their heads. The result is a loop that stretches with every handoff.

Compressing the Decision Supply Chain is how you compress OODA.

From Violinist to Conductor

Today's leadership model still depends on hands-on synthesis. Leaders trust analysts. Analysts gather and prepare information. Leaders interpret it. Even with dashboards and automation, too much of the violin is still being played by hand.

The shift underway is fundamental. Leaders are moving from players to conductors.

In the emerging model, agents, models, and contextualized bodies of knowledge perform the synthesis. Leaders set intent, constraints, and direction. They don't compile decisions. They conduct them. The orchestra plays. The conductor shapes the performance.

This is augmented intelligence in practice. Machines excel at pattern recognition, correlation, and execution at scale. They compress Observe and Orient into a continuous, living system where context carries forward automatically. Humans remain responsible for intent, judgment, strategy, and accountability. You can accelerate every letter of the OODA loop with technology, but you cannot outsource responsibility.

Technology as a superpower means collapsing time, not decorating delay.

One Process. This Week.

The tools exist. Power Automate. Appian. UiPath. Platform-native AI. If you're in the DoD, you have access to GenAI. The question isn't access. It's adoption.

So here's the challenge for 2026: What is one process you can automate this week to buy back decision time?

Not a transformation initiative. Not a multi-year roadmap. One process. One automation. One hour reclaimed and redirected toward a higher-order decision.

That's how you start finishing your OODA loop first.

Decision advantage isn't about better answers. It's about better timing. Act before they orient, and you're no longer reacting. You're dictating the tempo.

From Cuts to Clarity: What Efficiency Actually Requires

Tue, 30 Dec 2025 14:44:04 GMT

BLUF: Efficiency without architecture is just speed, and speed without understanding is how you break things that matter.

Cutting blindly or cutting with understanding (Image by J Eselgroth with GenAI)

Merriam-Webster defines efficiency as "effective operation as measured by a comparison of production with cost."[1] Oxford frames it as "doing something well with no waste of time or money."[2] My family put it more simply over dinner: accomplishing a task in the fewest steps using the least effort.

All three definitions share a common thread, and it has nothing to do with headcount. Efficiency is about process. It is about how people, policies, processes, partners, and platforms work together to produce an outcome. These five elements, the 5Ps, exist in every organization and every contract. When they are aligned, workflows and value compounds. When one is removed without understanding how it connects to the others, the system does not simply shrink. It fractures in ways that are difficult to predict and expensive to repair.

The 5P's and their relationship to delivering outcomes (image by J Eselgroth with Gen AI)

This is where the distinction between cutting and improving becomes critical. Removing a contract is not the same as improving efficiency. Cutting staff is not the same as optimizing a process. If you do not understand how a contract interfaces with the larger organization, you cannot know what you are actually doing when you cancel it. The 5Ps are not line items. They are load-bearing walls.

The DOGE Approach

In 2025, the Department of Government Efficiency launched a rapid campaign of contract cancellations and workforce reductions across federal agencies. The scale was unprecedented. Contracts worth billions were terminated. Entire program offices went dark. Thousands of federal employees and contractors found themselves suddenly without work, many with little warning and less explanation.

I was among them. Twice.

Within a span of months, I lost two executive roles, both directly traceable to DOGE-driven contract cancellations at agencies my employers supported. The first came 45 days into a new position as Chief Technology and AI Officer. The second arrived around month five as SVP of Technology and Innovation at a larger firm. In both cases, the message from leadership was the same: the cuts upstream had eliminated the revenue that funded my role.

If you worked in or around federal contracting this year, some version of this story is probably familiar. You may have lived it yourself, watched colleagues pack their desks, or felt the ripple effects in your own organization. The stated goal was efficiency: reduce spending, eliminate waste, and move fast. The execution was blunt force applied at scale.

The Missing Variable

I hold no grudge toward either company that let me go. They made hard calls under real pressure, and I understand why. I am even somewhat empathetic about what DOGE was trying to accomplish. Driving change at scale, under time constraints, with incomplete information is brutally difficult.

Research confirms what most leaders already know intuitively. Cognitive constraints shape how we make decisions under uncertainty, and models of decision-making should account for the pervasive limitations of human cognition.[3] When stress compounds that uncertainty, people default to simpler heuristics and faster action, often at the expense of nuance.[4] The interplay between stress and decision-making is complex, influenced by timing, context, and individual differences.

The problem is not will. The problem is architecture.

When the system is too complex to model, you cut and see what happens. It may feel like the only viable option. But that is a symptom, not a strategy. Imagine if DOGE had access to a tool that could answer a simple but powerful question: where is the best utilization of our taxpayer dollars? A system capable of modeling how actions connect to outcomes. How a contract cancellation ripples through the 5Ps of an agency. How strategy and reality align, or fail to.

Such a tool might have enabled more precision. It might have surfaced alternatives that no one had considered. It might have changed what got cut and what got protected. Without that architecture, rapid action becomes the only path forward.

But here is the opportunity hidden in that constraint: we can build it. I know because I have done it before.

I Was Doing Decision Intelligence Before I Knew It

In 2015, the Air Force handed me a version of the same problem.

We had just stood up the Air Force Installation and Mission Support Center, and the commander wanted to centrally manage all small arms firing ranges across the enterprise. This had never been done in USAF history. The question seemed simple enough: if we had a dollar to spend, where should we spend it? But then came the harder questions. Do we have a prioritized list? If we spend that dollar, what are the second, third, and fourth-order effects of that decision?

We did not have answers. So we built them.

We started with Security Forces data, then expanded to Civil Engineering, Finance, Human Capital, and beyond. We deconstructed the problem by focusing on the stakeholders affected by range outcomes. We expanded perspectives and added data sources. When the AFIMSC Commander saw early results, she asked about range capacity, which unlocked an entirely new layer of context we had not anticipated. Each question led to better questions, and each answer revealed more of the system.

By May 2018, we delivered a 30-year, $954 million strategy for right-sizing every firing range across the globe. It was the first asset management approach of its kind in the Air Force. I did not call it Decision Intelligence at the time, but that is exactly what it was: connecting actions to outcomes, modeling consequences, and putting leaders in a position to make informed choices before committing resources.

From 1 Question to improving an asset class for years to come (Image by J Eselgroth with Gen AI)

Efficiency Redefined

Last year, I introduced the Digital Efficiency Matrix, a 3x3 framework that maps digital maturity against operational efficiency.[5] The insight was straightforward but important: the destination is not "digital." The destination is intelligent. An intelligent enterprise does not just digitize its processes. It understands them. It models them. It simulates outcomes and anticipates consequences before committing resources.

The path from today to that destination is Intelligent Transformation, and the engine that powers it is Decision Intelligence.

But reaching that destination requires more than technology. It requires disciplined attention to the 5Ps: People, Policy, Process, Partners, and Platforms. Each must be assessed. Each must align. Technology alone cannot carry you there. The 5Ps are the connective tissue that determines whether a transformation holds together or falls apart under pressure.

Decision Intelligence gives leaders visibility into consequences before they decide. It makes complexity manageable by surfacing the relationships between actions and outcomes. It does not replace accountability. It accelerates the ability to exercise accountability wisely, with evidence rather than intuition alone.

Why Chiron AI

Both layoffs gave me the same gift: clarity.

I had always wanted to build Chiron AI, but I did not know when or how to make the leap. This year answered that question for me. Each role gave me R&D momentum and sharpened my thinking about what leaders actually need. Each disruption removed another reason to wait. Sometimes the path forward only becomes visible when the path you were on disappears.

Our mission is simple: turn strategy into reality. We are building the apparatus that lets decision-makers ingest information, understand context, and model consequences at the speed of relevance. In the age of generative AI, we cannot outsource accountability. But we can equip leaders with the tools to make decisions that stick, decisions grounded in an understanding of what will happen next.

Looking Ahead

Next year, we move from concept to capability. The work is underway, and I am not ready to say more just yet. But the direction is clear, and the urgency is real.

Efficiency is not speed alone. It is precision. It is foresight. It is understanding what you are doing before you do it, and documenting why you made the choice you made.

As you enter 2026, consider the decisions you will face. What complexity will demand clarity? What choices would benefit from modeling and simulation before you commit? What second and third-order effects are you currently unable to see?

The tools are within reach. Sometimes the architecture starts with a whiteboard and the right people in the room. Sometimes it starts with a conversation about what you wish you understood before you acted. The question is not whether you have the resources to begin. The question is whether you will.

Sources

[1] Merriam-Webster. "Efficiency." Merriam-Webster.com Dictionary. https://www.merriam-webster.com/dictionary/efficiency

[2] Oxford University Press. "Efficiency." Oxford Learner's Dictionaries. https://www.oxfordlearnersdictionaries.com/definition/american_english/efficiency

[3] Lebiere, C., & Anderson, J. R. (2011). Cognitive constraints on decision making under uncertainty. Frontiers in Psychology, 2, 305. https://doi.org/10.3389/fpsyg.2011.00305

[4] Sarmiento, L. F., Lopes da Cunha, P., Tabares, S., Tafet, G., & Gouveia Jr, A. (2024). Decision-making under stress: A psychological and neurobiological integrative model. Brain, Behavior, & Immunity - Health, 38, 100766. https://doi.org/10.1016/j.bbih.2024.100766

[5] Eselgroth, J. (2024). The Unending Quest for Efficiency: Navigating Beyond Digital Transformation. Chiron AI. https://www.chironai.io/the-unending-quest-for-efficiency-navigating-beyond-digital-transformation

In the Speed of Now, Ready Beats Rushing

Wed, 17 Dec 2025 05:01:30 GMT

BLUF: Organizations fail at speed because they confuse urgency with readiness, the solution is clarifying ownership, embedding context, and practicing judgment before crisis forces action.

Most organizations think speed means moving faster. It doesn't.

Speed means being ready to decide before urgency forces the issue. Only 37% of organizations make decisions that are both high quality and fast. The gap isn't capability. It's readiness. Organizations confuse urgency with preparation. They demand speed without removing friction.

The cost shows up in the numbers. Lost productivity from context switching costs the global economy $450 billion annually. But the real expense isn't the switching itself. It's the momentum that never builds.

Decision readiness fixes this. Organizations that are 30% faster at addressing inefficiency always have access to the data they need. Not more data. The right data. At the right time. In the right hands.

What Decision Readiness Actually Means

Smooth is fast. Rushing is sloppy.

High-performing teams look fast because they did the work before urgency hit. They clarified roles. They built trust. They practiced deciding together. When urgency arrives, it doesn't break them. It activates them.

This happens through preparation, repetition, and trust. When these exist, speed becomes automatic. Teams don't waste time reorienting. They don't lose context. They don't wait for permission.

The difference matters. Rushing means cutting corners and skipping judgment. Speed means the work was already done. One creates chaos. The other creates momentum.

Why Most Organizations Fail

Organizations confuse speed with pressure. They demand faster decisions without removing the friction slowing everything down. The phrase "let me get back to you" becomes the default response. Not because people lack knowledge. Because systems aren't built for the way work actually flows.

I called a colleague once while deep in productive flow. I needed help solving something concrete. Their response was reasonable. "Let me get back to you." They did, eventually. But by the time they responded, the moment had passed. My momentum was gone.

Nothing malicious happened. Life intervened. Another call came in. Another meeting ran long. Context slipped away. That's how most delays happen. Not through neglect. Through cascading interruption.

Workers toggle roughly 1,200 times each day. Each toggle costs time. Each interruption breaks flow. Each delay compounds.

This pattern reveals the deeper problem. Leaders fear being wrong more than being slow. They build cultures where hesitation feels safer than action. Where political calculation trumps strategic logic.

That fear spreads. People stop experimenting. They stop learning. They stop deciding. Innovation doesn't die from bad ideas. It dies from delayed ones.

What Decision-Ready Systems Require

Decision readiness requires three things working together.

First, clear ownership before urgency arrives. When everyone knows who owns what, decisions happen at the point of knowledge. Companies with empowered teams report 25% faster time-to-market for new products. Empowerment isn't about authority. It's about accountability.

Second, embedded context that travels with the work. Organizations that move 30% faster always have access to the data they need. Not buried in systems. Not trapped in inboxes. Present, available, and clear.

Third, psychological safety around judgment. Not knowing is acceptable. Being unprepared is not. Admitting uncertainty builds trust. Deferring judgment indefinitely erodes it.

I worked for a leader once who always said no first. Not to be difficult. To force thinking. If someone came back a second time, prepared, they got help. Every time. The goal wasn't denial. It was decision ownership.

Where AI Fits

AI doesn't solve decision problems. It exposes them. It amplifies good systems and accelerates bad ones.

Used well, it sharpens thinking and surfaces gaps. Used poorly, it becomes the excuse. When decisions fail, tools get blamed first. Accountability disappears. Learning stops.

The real question isn't what AI can do. It's what we're prepared to decide. What stays human. What gets delegated. Who owns the outcome.

The System Design Problem

This isn't a technology problem. It's a system design problem.

Organizations still run on episodic work models. Ask. Analyze. Align. Respond later. But modern work is continuous. Momentum matters. Flow matters. Continuity matters.

The organizations that thrive now don't move faster. They remove friction earlier. They clarify decisions before urgency arrives. They practice judgment, not just analysis. They design for flow, not heroics.

They decide before the moment demands it.

Preparation is the only form of speed that scales.

References

McKinsey & Company. "Effective decision making in the age of urgency." April 2019. https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/decision-making-in-the-age-of-urgency
Spekit. "The Effects of Context Switching are Costing You Big Time." September 2024. https://www.spekit.com/blog/the-effects-of-context-switching-are-costing-you-big-time
Orgvue. "New research finds link between faster decision-making and a greater share of profit." December 2020. https://www.orgvue.com/news/new-research-finds-link-between-faster-decision-making-and-a-greater-share-of-profit/
Orbii. "The Impact of Slow Decision Processes on Organizational Success." September 2024. https://orbii.fr/en/posts/impact-of-slow-decisions-culture/

Compressing Learning Time: How Leaders Turn AI into an Advantage

Tue, 09 Dec 2025 19:08:37 GMT

BLUF | We gain time, clarity, and capability when we treat generative AI as a learning superpower instead of another tool.

Have you ever had to brief a senior leader on a complex problem but spent most of your time just orienting them to the landscape? Or maybe you've been the leader walking into a meeting and needed to get smart fast before you enter the room. No time to read it all. No space to absorb it. You just need the picture.

A simple visual can change that. A clean infographic can compress the learning curve in seconds. Research backs this up: hear a piece of information, and three days later you'll remember 10% of it. Add a picture and you'll remember 65%.¹ Generative AI now gives every leader the ability to create these visuals on demand. Not as a replacement for required reading. Not as a shortcut around accountability. But as a fast 10 to 20 percent head start. A starter pistol. A way to see the terrain before you move through it.

AI as a learning superpower

Most leaders don't struggle with information scarcity. They struggle with absorption. New software. New policy. New mission requirement. The learning curve expands. The available hours shrink.

AI doesn't replace learning. It removes the drag. It accelerates the slowest part of the process: orientation. That shift is the superpower.

But expectations matter. AI won't give you mastery. It gives you 10 to 20 percent. Just enough to see the shape of the problem. Just enough to build a 35,000-foot view in ten seconds. You still need to read the policy. You still need to make the decision. But you start with context instead of confusion. That saves hours. Across a workforce, it saves months.

NotebookLM is one example of this pattern. Not the only one. But clear enough to make the capability real.

A practical workflow for learning at speed

I needed to understand Tableau Next fast. Instead of consuming ten videos end-to-end, I uploaded them into NotebookLM. It highlighted the shift toward agentic workflows and a unified workspace. It framed the evolution in a way that matched how I learn.

Infographic of Tableau Next using notebookLM

Was it perfect? No. Was it enough to get moving? Absolutely. The real learning came from experimentation. But the orientation came from AI.

I tried the same approach with the Air Force knowledge-management AFI. NotebookLM extracted the underlying physics: people, processes, technology, and the lifecycle of knowledge as an operational asset.

Infographic of Knowledge Management (KM) Air Force Manual (AFMAN) 33-396 using notebookLM

It won't replace reading the AFI. But it gives leaders the shared mental model they need before making decisions tied to that policy. It creates alignment before the meeting starts.

I tested the pattern again with publicly available CATM regulations from my earlier career field. NotebookLM distilled qualification cycles, maintenance tiers, and readiness expectations into a single frame.

Infographic of Combat Arms Training and Maintenance (CATM) Department of the Air Force (DAF) 31-131 DAFI/DAFMAN using notebookLM

Directionally right. Imperfect. Immediately useful.

This is the pattern. Gather material. Load it. Ask for outputs that match your learning style. Then do the real work with more clarity and less friction.

Why this matters for leaders

Time is the true denominator. The faster you orient, the faster you decide and act.

The evidence is clear. A University of Minnesota and 3M study found that presentations with visual aids were 43% more persuasive than those without.² Visuals improve attention, comprehension, retention, and action. Leaders who use them move teams faster.

This approach doesn't outsource accountability. It strengthens it. Leaders reclaim the hours lost to blind starts. They build digital, data, AI, and functional literacy. They walk into conversations knowing the terrain. Teams onboard faster. Stakeholders align sooner. Decision cycles tighten.

Most leaders think the barrier is access. The real barrier is imagination. Most organizations already allow AI use with public data. Many provide approved internal environments for controlled content. The question isn't "Can I use this?" It's "What could this unlock if I did?" As an old boss put it well: if you never ask the question, the answer is always no.

AI gives leaders a way to create orientation on demand. Instead of waiting for perfect training products, they generate a tailored starting point in minutes. They move from consuming to shaping. They build clarity before conflict and understanding before action.

Guardrails and good practice

NotebookLM is one illustration. Ask Sage, Gamma, Gemini, and many others offer similar capability. The principle is constant across tools: turn raw material into structured insight.

Respect organizational boundaries. Use public data unless you're operating in an approved environment. The rules matter. They protect missions, teams, and trust.

Aim for good enough. Not perfection. This is orientation, not adjudication. It gives you the first slice of clarity so your judgment can handle the rest.

The goal is simple. Get time back. Reduce friction. Improve clarity. Build capability. Equip yourself and your teams with faster mental models so you can invest your energy where it counts.

Leaders who learn at speed lead with advantage.

Sources

¹ Medina, J. (2014). Brain Rules: 12 Principles for Surviving and Thriving at Work, Home, and School. Pear Press. https://brainrules.net/vision

² University of Minnesota / 3M Corporation study, as cited in: Huang, S., Martin, L.J., et al. (2020). "Maximizing impact with infographics." Canadian Pharmacists Journal. https://pmc.ncbi.nlm.nih.gov/articles/PMC7605071/

Delivering Tailorability to the Tactical Level in the Age of AI

Tue, 02 Dec 2025 14:47:17 GMT

BLUF | Shadow IT is not a compliance problem. It is a design failure. The Intelligent Enterprise is where we need to go. Intelligent Transformation is how we get there. Solving tailorability at the tactical level is what we build along the way.

The opportunity to close the gap, delivering Tailorability (Image by J Eselgroth with Gen AI)

Have you ever started a task and realized the business system does not meet your needs? So you open a spreadsheet [or some other tool]. You craft a solution with the tools you have, not the tools you need.

Maybe it becomes an Access database. Maybe you sidestep corporate IT and purchase software because you needed to solve a problem yesterday. The official process moves so slowly that workarounds become the only path forward.

Does this sound familiar? This is not rebellion. It is survival.

Innovation Without Integration

Honorio Padrón III, captured the pattern perfectly in his recent LinkedIn article, The Real AI Bubble: “The result was innovation without integration.” He was describing the flood of AI investment that never connected to enterprise architecture. That line applies to the last decade of enterprise technology with uncomfortable precision.

We bought the platforms. We built the dashboards. According to Auvik Networks, 30-40% of enterprise IT spend still flows into Shadow IT. Zluri also reports that the average organization uses more than 270 SaaS tools. More than half were never approved.

You can call it noncompliance. I see it as unmet need.

I have written before about the 5Ps of Intelligent Transformation: People, Policy, Process, Partners, and Platforms. Shadow IT sends signals across all five. People improvise because platforms cannot flex. Processes fracture because policy cannot adapt. When you see Shadow IT, you are seeing the 5Ps under stress.

The 5Ps under stress (Image by J Eselgroth with Gen AI)

Then came generative AI. People hoped it would close the gap. Instead, it exposed it. MIT found that 95% of enterprise gen AI pilots delivered no measurable value. Not because the ideas were bad. Because the work was isolated. Innovation without integration, all over again.

The Architectural Pivot

Honorio raises this tension with the shift toward the Intelligent Enterprise. That destination is right. Reaching it requires Intelligent Transformation. This is an intentional journey that rebuilds architecture, capability, and culture together.

AI factories represent a critical milestone. Think of them as the physics layer of the enterprise finally consolidated. Data, identity, security, governance, compute. All in one coherent environment. They give intelligence a place to live, evolve, and scale.

But here is the part we do not talk about enough. AI factories may fix the backbone. But they do not fix the last mile.

Walk into a claims unit, a field office, a military unit, or a distribution center. Core platforms give you transactions. Dashboards give you trends. Real action sits in the exceptions. The judgment calls. The tiny workflow steps no product manager ever designed.

If Intelligent Transformation is the journey and the Intelligent Enterprise is the destination, then tailorability at the tactical level is what we build to complete the trip.

Safe Tailorability Changes Everything

Tailorability is the ability to deliver capability that stays malleable at the point of need. It resolves the tension between economies of scale and the reality that one size never fits all. Standardize the foundation. Adapt the experience. This applies to digital systems and physical operations alike.

Inside an AI factory, tailorability becomes safe. Not the free-for-all of Shadow IT. Not the rigidity of a monolithic system. Something in between.

Imagine a team lead who notices friction in a weekly process. Today, she can file a ticket and wait six months. Or she can solve it herself with an unauthorized tool. Inside an AI factory, she has a third option. She describes the friction in plain language. The environment generates a micro-app in minutes, not months.

Before and After delivering Tailorability to the tactical level (Image by J Eselgroth with Gen AI)

This is the shift from violinist to conductor. She sets intent and lets intelligent systems perform the heavy lift. Craftsmanship moves from execution to orchestration.

Because it runs inside the AI factory, it inherits everything the enterprise requires. Security. Data permissions. Audit trails. It feels like Shadow IT in speed. It behaves like enterprise IT in safety. That is the breakthrough.

When this works, people reduce the amount of tools through the side door, and maybe even stop altogether. They build inside the system. Every micro-app feeds back into the Business Body of Knowledge. Tribal knowledge becomes documented and learnable. The gap closes through a legitimate path, not forced compliance.

The Path Forward

You do not need a fully built AI factory to start. But remember the super-serum principle: AI amplifies what already exists. Sloppy processes scale sloppiness. Strong foundations scale excellence.

Start by mapping real workflows. Capture exceptions and judgment calls. Create a safe sandbox for tailorability. Standardize data, identity, and permissions. Pair technologists with operators. Measure what happens outside the system.

For decades, enterprises have cycled through the same pattern. Build a system. Watch people work around it. Crack down. Repeat. Each cycle hits the same effort hump. The steep climb before benefits appear.

Flattening the curve through Intelligent Transformation (Image by J Eselgroth with Gen AI)

Intelligent Transformation flattens that curve. Safe tailorability eliminates the friction that creates the second bump. Constraints become compounding returns instead of fixed trade-offs.

The path to the Intelligent Enterprise runs through the last mile. The last mile finally has a chance to keep up.

References

Honorio Padrón III, “The Real AI Bubble: Inside Zero-Latency Decision™ Capitalism and the Extinction of Many of the Existing 80,000 AI Startups” https://www.linkedin.com/pulse/real-ai-bubble-inside-zero-latency-decision-many-80000-padr%C3%B3n-iii-tqo0e/
Auvik Networks, “Shadow IT Stats” https://www.auvik.com/franklyit/blog/shadow-it-stats/
Zluri, “Shadow IT Statistics: Key Facts to Learn in 2024” https://www.zluri.com/blog/shadow-it-statistics-key-facts-to-learn-in-2024/
MIT (reported via AI Magazine), “Why 95% of Enterprise AI Investments Fail to Deliver” https://aimagazine.com/news/mit-why-95-of-enterprise-ai-investments-fail-to-deliver

Efficient by Design: Why Teams Who Eat Their Own Dog Food Win

Wed, 19 Nov 2025 21:45:25 GMT

BLUF | Real efficiency happens when leaders design operations with intent, align people and systems, and turn practice into progress.

Image of efficiency theater compared efficiency in action (image by J Eselgroth with Gen AI)

Why Efficiency Still Feels Out of Reach

The most effective organizations aren’t efficient by accident. They are efficient by design. They build systems that fit the way people actually work. They align data, process, and purpose so decisions flow instead of stall. Whether it’s a growing business trying to scale or a public program under pressure to deliver more with less, the goal is the same: remove friction and make every dollar count.

This year, 53 percent of leaders say productivity must increase, yet 80 percent of the global workforce say they lack the time or energy to do their work (Source: Microsoft Work Trend Index 2025 ). The demand for more output is clear, but the capacity to deliver it is stretched thin.

Too often, efficiency becomes a slogan instead of a strategy. Leaders invest in tools, launch transformation projects, and celebrate new dashboards. Then, six months later, the same bottlenecks reappear under new names.

True efficiency is not something you buy. It is something you build. It is a design choice, not a by-product.

The Efficiency Gap

Every organization measures efficiency. Few improve it. Dashboards and KPIs make it look like progress is happening, yet inside the system, work still crawls through approvals, duplicate efforts, and manual processes. It is the same story in both small businesses and large enterprises: everyone tracks activity, but few improve outcomes.

This disconnect— the efficiency gap —represents the space between performance metrics and operational reality. For small and midsize businesses, that gap eats into margins. For government and enterprise programs, it slows results and erodes trust.

Closing the gap means rethinking how we define progress. Efficiency is not about doing more work faster. It is about designing smarter systems making the right work easier to do.

The Efficiency Boom That Isn’t

Search interest for “operational efficiency” ( Google Trends - Picture below) is higher today than at any point since Google began tracking it. Everyone wants to be efficient. Yet the rise in curiosity has not produced a rise in capability.

Image of the Dog Food Index, a 2 by 2 matrix showing how practicing and talking relate (image by J Eselgroth)

In the lower right sits Efficiency Theater, the loudest voices with the least discipline. In the upper left, the Quiet Operators, teams that don’t market efficiency but embody it. And in the upper right, a rare group that walks the talk:

Amazon, where every team was required to expose its services through APIs long before “digital transformation” was trendy ( Bezos API Mandate )).
Atlassian, which runs its own operations on Jira and Confluence ( How Atlassian Uses Confluence )).
Microsoft, which acts as “customer zero” for Copilot ( Microsoft Customer Zero )).
Notion, which manages Notion on Notion ( How Notion Uses Notion ).

These organizations prove efficiency from the inside out. They don’t just measure ROI; they generate it through design. They improve by testing on themselves first. Their products work because their operations demand it. The real takeaway: efficiency isn’t about what you say, it’s about how you operate when no one is watching. That’s also called integrity.

From Burden to Advantage

For many leaders, especially in small and midsize organizations, the back office can feel like a necessary evil. Compliance, reporting, and operational overhead consume time that could be spent creating value. But what if efficiency became an advantage instead of an obligation?

Efficiency can shift from burden to advantage. Augmented intelligence makes this possible. Automation handles repetitive tasks. RPA clears routine work. Generative AI speeds documentation and insight. Predictive analytics guides decisions before issues appear. These tools create space for real thinking and real impact.

A recent Vistage study found that 63 percent of companies reporting higher productivity also saw increases in profitability, and 72 percent reported revenue growth. Efficiency doesn’t just cut costs; it creates opportunity.

This is not about replacing people. It is about amplifying them. Technology should clear the runway, not compete for control of the cockpit.

What Leaders Can Do Next

Efficiency only becomes real when leaders slow down long enough to see how work truly gets done. Intelligent transformation begins with honest reflection, not more tools or louder messaging. When leaders understand the truth of their operations, they see the patterns that move work forward and the anti-patterns that quietly slow it down. This quick scan helps teams understand where they stand and what needs to change.

Image of a runway being cleared visualizing tasks being moved out of the way (Image by J Eselgroth with Gen AI)

Efficiency as Freedom

Efficiency done well creates freedom. Freedom for teams focusing on work that matters. Freedom for leaders building without chaos. Freedom for customers and citizens receiving value without delay.

The future will not reward more dashboards or louder talking points. It will reward leaders designing operations with intelligence and care.

Efficiency is not about moving faster. It is about moving with intent. It is about finally doing what you said you would. Consistently. Clearly. With purpose. Better operations create better lives. The work improves so the person can breathe again.

Sources

Image of the topic Operational efficiency trendline on Google Trends (screenshot of Google Trends by J Eselgroth)

Recent research underscores the point. PwC found that 57 percent of operations leaders have integrated AI into at least one business function, yet 92 percent say those tech investments have not fully delivered expected results. Similarly, McKinsey reports that while 88 percent of organizations now use AI in at least one area, only about one third have scaled it successfully.

The conclusion is clear: efficiency has become a performance, not a practice. Organizations talk about it, report on it, and market around it, but few live it. The intention is real, but the follow-through is missing. The path from knowing to doing remains broken.

Who Actually Walks the Talk

Picture a simple two-by-two grid. I call it the Dog food Index. One axis shows how loudly a team talks about efficiency. The other shows how deeply it practices it.

A Simple Starting Path

Real progress begins with awareness. Leaders improve operations by seeing their system clearly, fixing obvious friction, and automating where it serves people and purpose. Testing changes on themselves first strengthens integrity and builds trust. Intelligent transformation grows through steady practice, not bigger claims.

Bending the Iron Triangle: Why Intelligent Transformation Makes Old Constraints Malleable

Thu, 13 Nov 2025 18:57:14 GMT

BLUF | The Iron Triangle is not dead. It becomes malleable when intelligent transformation changes the materials, not the physics. But to bend it, you must first survive the effort hump.

The Old Laws Still Echo

An old friend in firearms development reminded me recently: "Signature, lethality, mobility. Pick two." It's the weapons designer's iron triangle. Reduce signature, sacrifice mobility. Max lethality, signature grows. Another friend of mine in a different conversation reminded me: "Quality, time, features. Pick two." It's the software engineer's iron triangle. Increase features and meet quality standards, it'll take more time. Want features faster, either choose fewer features or be prepared to sacrifice quality.

We keep discovering the same constraint everywhere. But in 2025, in the age of AI, what if we can bend these laws?

The Three Mirrors

Across industries, domains, and decades, we keep running into the same immutable shape. Whether designing weapons, building software, or modernizing enterprises, leaders face a repeating pattern of constraint. Different contexts change the vocabulary, but the structure stays the same. And understanding these mirrors is the first step toward bending them.

The Iron Triangle (software): Time, Features, Quality
The Mission Triangle (weapons): Signature, Lethality, Mobility
The Transformation Triangle (AI era): Speed, Cost of Ownership, Decision Velocity

Three triangles same system of trade-offs (Image by J Eselgroth with GenAI)

One universal truth: trade-offs never disappear. But in the age of AI, their behavior changes.

Compute replaces labor.
Automation compresses time.
AI augments quality.

The constraint remains. The materials shift.

The Effort Hump

If you look at the two charts that accompany this section, you can actually see this story play out. The first curve shows the traditional digital transformation lifecycle, with its steep climb, second spike, and long tail of stabilization. The second curve overlays Intelligent Transformation on top of it and makes the contrast impossible to ignore. The shapes are familiar, but the behavior changes. The peaks flatten. The dips smooth out. The entire system becomes easier to manage.

The Digital Transformation Curve time and complexity (Image by J Eselgroth)

Transformation often begins with optimism and the promise of speed, efficiency, and improvement. Then reality arrives. The effort hump appears, the steep rise in complexity before benefits become visible. Teams retrain. Processes shift. Governance tightens. New tools collide with old patterns. This phase demands maximum cognitive load. Requirements take time. Infrastructure planning requires alignment. Testing and evaluation add weight. Security threads through everything.

In traditional transformation, a second bump follows as teams struggle to settle into new rhythms, creating friction and unplanned effort.

Now look at the Intelligent Transformation curve.

Intelligent Transformation curve, where complexity reduces and time to value increases (image by J Eselgroth)

This is where the orchestration layer truly appears. During this phase you:

Establish you approach to the 5Ps (People, Policy, Process, Partners, Platforms)
Build your Business (or Mission) Body of Knowledge
Redesign foundational workflows
Define requirements and alignment
Coordinate infrastructure planning
Test systems and integrate security
Stabilize operations and maintenance

The phases remain, but the amplitudes shrink. The first spike drops by roughly 40 percent. The O&M bump almost disappears. Modernization arrives sooner and with less strain. AI does not remove the journey. It reduces the pain . Automation absorbs repetitive tasks during planning. Machine learning accelerates validation and testing. Intelligent systems smooth the transitions that normally cause turbulence.

Key insight: Never exceed the first bump in maximum effort. Over time everything should become easier and simpler.

The gray ghost line shows the traditional path. The colored line shows intelligence bending effort itself. New materials reshape the experience, making old constraints manageable.

From Violinists to Conductors

From Violinists to Conductor (Image by J Eselgroth)

Many organizations hold modern tools but operate with old mental models. They treat AI like another instrument rather than a shift in how work happens. The old world depended on violinists. Craftsmanship lived in the hands. Quality depended on execution. Speed was limited by human capacity.

The new world needs conductors . Humans set intent, sequence, and tempo while AI-powered tools perform the heavy lift. Agentic workflows, generative models, and automated pipelines become the instruments. Humans shape the outcome.

This pivot redistributes the physics:

Speed comes from automation
Cost shifts to compute
Quality emerges from orchestration

Organizations failing to make this shift scale inefficiency. Those that do, unlock momentum and bend the Iron Triangle toward advantage.

The Super-Serum Principle

The same tension we see in the triangles shows up here too. Tools do not change your fundamentals. They exaggerate them. The same formula that created Captain America created the Red Skull . Technology amplifies what already exists.

Sloppy processes? AI scales sloppiness.
Strong standards? AI scales excellence.
Corner cutting? AI accelerates it.

Craftsmanship still determines trajectory. AI magnifies your habits, good or bad, and this is why bending the Iron Triangle requires discipline before acceleration.

Good Enough vs. Perfect

Quality becomes a managed continuum in the AI era, and leaders must rethink what progress looks like. The 80/20 rule still applies, but the rhythm changes.

AI gets you to ~80% faster, the heavy lift handled in seconds.
Humans refine the work to 90 to 95%, applying judgment, nuance, and context.
Perfection still wastes resources and almost never pays back.

"Good enough" paired with decision velocity outperforms "perfect" delivered late, every time. Momentum beats polish when polish delays impact.

This is where expectation management becomes mission critical. It is no longer a soft skill. It now sits beside prompt engineering as a foundational discipline. When expectations are unclear, quality debt accumulates fast.

Speed without quality does not create value. It just produces bad outputs quicker. The fixes' cost more. Teams burn out. Trust erodes in ways that can take years to repair.

From Triangle to Flywheel

From Iron, through malleability, and morphing into the flywheel (Image by J Eselgroth with Gen AI)

The Iron Triangle assumes static trade-offs, fixed levers you push and pull and hope to balance. Intelligent transformation changes that posture. It replaces static constraint with dynamic motion.

Think about the flywheel as the moment when a system stops resisting progress and starts reinforcing it. Each rotation builds upon the last. Each improvement strengthens the next. It is the shift from forcing progress to enabling it. Each rotation of the flywheel:

Increases decision velocity
Reduces cognitive friction
Improves outcomes
Reinforces learning

The triangle stops behaving like a box of limits and starts acting like a loop of compounding returns. It becomes dynamic, a self-improving system instead of a static set of trade-offs.

Momentum replaces trade-offs.

The Real Calculation

In my " Data-Driven Theater " analysis, a 93-slide briefing cost $241,881. The ratio was 1:3, one hour of creation for every three hours of review.

When intelligence enters the workflow, that ratio collapses. AI generates drafts in minutes. Humans validate in minutes. Creation and refinement finally move at the speed leaders expect. This is not cost-cutting. It is bandwidth reclamation. It frees teams to focus on decisions instead of production.

The entry fees remain : governance, training, integration. Once paid , returns compound quickly. Humans orchestrate. Machines execute. Work shifts from force to flow.

We turn bureaucracy into bandwidth and constraint into capability .

Transformation begins by meeting people where they are. Start with familiar constraints. Show how new materials bend them. Physics stays constant. Materials evolve.

Where You Go From Here

Your transformation either bends the Iron Triangle or it does not. If it does not, you are adding expensive technology to yesterday’s problems. Speed, cost, and quality can improve together, but only after you pay the entry fees. You must move through the effort hump, guide the human shift, and build a foundation for continuous learning.

The triangle is no longer iron. It becomes malleable when the right forces are applied. Bending takes intention. It takes discipline. It takes leadership willing to rethink how work gets done.

What's your developer-to-bureaucracy ratio? More importantly: Does your transformation bend the Iron Triangle or just paint it differently?

If you want help bending your triangle, contact me. I have spent my career helping organizations navigate these trade-offs. Weapons systems or AI systems, the physics stay the same.

You Can’t Outsource Accountability

Fri, 07 Nov 2025 18:46:47 GMT

BLUF | How responsible use of GenAI separates activity from intelligent action and moves you up and to the right of the digital efficiency matrix.

You can delegate tasks, but you can’t delegate responsibility. GenAI accelerates work, but accountability must remain with the professional. Intelligent Transformation isn’t about building smarter systems; it’s about ensuring that as our systems get smarter, people remain responsible for what those systems do. Technology can speed the work, but people still own the results.

From Experimentation to Ownership

Across this series, from GenAI at Work to GenAI in Action to Digital Transformation Is Dead, each step revealed a layer of Intelligent Transformation: awareness, application, and philosophy. Now comes the behavioral layer, accountability.

As AI becomes more integrated into our workflows, responsibility doesn’t shrink. It expands to include the systems we design, prompt, and oversee. The professional standard hasn’t changed; only the scale and speed of our impact have.

When Accountability Slips

Over the past year, I’ve had the opportunity to help several organizations adopt generative AI into their mission and business processes. Across these experiences, I’ve seen a familiar pattern: some people begin treating the LLM as a pass-through. The system produces content, and they accept it with little scrutiny, minor editing, light validation, and little ownership.

Quality declined. Trust eroded. Teams often fall into what I call the accountability gap , where automation runs ahead of professional judgment. On the Digital Efficiency Matrix, they tend to land in the Challenged quadrant, high digital maturity but low efficiency. Everything is digitized, but much of the work remains manual.

AI didn’t fail us; our relationship with accountability did.

Re-Centering Accountability

As a team, we reset the approach. To better understand what was happening we proactively sat down with each team member to understand their approach. Each team member walked through their process step by step. In the discussion and follow up we found three essentials for them to be successful:

Validate outputs, don’t just forward them.
Use AI to augment, not excuse.
Maintain the same quality standards as before; faster doesn’t mean shallower.

We also coached on prompting, validation, and critical review. The message was clear: GenAI supports your work, but accountability stays with you. The bar doesn’t lower because of automation, it rises.

Within the 5Ps framework, this alignment was clear:

People: Ownership and integrity
Process: Verification and iteration
Policy: Human in/on the loop governance

Accountability in Practice: Human in / on the Loop

Accountability doesn’t mean inserting humans everywhere. It means building systems that know when to bring humans back in.

Table of Human IN or ON the loop

Think of it like a control chart . As long as outputs stay within expected bounds, automation runs efficiently. When a data point drifts outside one or two standard deviations, it becomes a signal for review, an invitation for the human to step back in the loop. For a clear, hands-on example of how control limits work in practice, check out Andy Kriebel’s interactive control chart . It illustrates how variation behaves over time and how quickly you can spot signals (red dots) that fall outside acceptable limits, just as we do when monitoring the performance of intelligent systems.

Andy Kriebel's interactive control chart (Screenshot from below interactive chart)

That’s accountability at scale: professionals aren’t micromanaging every output, but they remain responsible for defining thresholds, interpreting anomalies, and acting when things fall out of bounds.

As Mark Graban reminds us in Measures of Success, “The point isn’t creating charts. It’s about wasting less time chasing ‘noise’ in our metrics, which means more time that’s available to work on systemic improvement.” These charts are not just visuals; they’re prompts for focus and intelligent response.

Or, as he also notes, “Charts like these tell us there’s a signal, it doesn’t tell us why (that’s up to us as the users of the charts).” In other words, automation can surface anomalies, but judgment still owns the why (Graban, M. Process Behavior Charts Save Us Time, Help Us Sleep Better at Night, LeanBlog.org , 2017).

Accountability isn’t about doing everything yourself; it’s about building systems that know when to bring you back into the process.

From Dependence to Mastery

Once accountability returned, everything improved. Quality rose above pre-AI levels. Turnaround time dropped from many hours to minutes. The team moved from depending on the model to mastering it.

Within the Digital Efficiency Matrix (below), this specific team’s journey moved from Challenged to Innovators , and now firmly into the Leaders quadrant, where high digital maturity meets high efficiency. This was one of many programs yet to be morphed into the Leader quadrant. For organizations wishing to pursue similar progress, the path forward begins with identifying where each program or office currently sits within the matrix and then focusing efforts on measurable accountability within that specific process area rather than trying to “boil the ocean.”

The Digital Efficiency Matrix (By J Eselgroth)

That’s how organizations scale Intelligent Transformation: not through sweeping change, but by improving individual systems and functions that collectively lift the enterprise.

Leadership lives in the top-right quadrant, where Intelligent Transformation and maximum efficiency converge.

The Leadership Imperative

Accountability is the invisible architecture of trust. You can delegate tasks, but not judgment. Systems don’t replace discernment; they amplify its consequences. Leaders must design for balance, keeping humans in and/or on the loop without reintroducing inefficiency. Intelligent oversight requires clear thresholds, transparent feedback loops, and escalation paths that work.

Delegation of tasks is smart. Delegation of judgment is dangerous.

Closing Reflection

AI can accelerate your work, but only you can validate its worth. True progress isn’t measured by how quickly we automate but by how confidently we can trust the results. Accountability turns automation into augmentation and Intelligent Transformation into trusted performance, because when professionals stay engaged, systems improve, teams mature, and outcomes sustain over time.

This article is Part 4 of the Intelligent Transformation Series.

Part 1 – GenAI at Work : Most organizations have met GenAI, but few have learned to work with it.
Part 2 – GenAI in Action : Moving from experimentation to execution with purpose and structure.
Part 3 – Digital Transformation Is Dead : Why it’s time to shift from doing digital to thinking intelligently.
Part 4 – You Can’t Outsource Accountability: Maintaining human responsibility as automation scales.

Digital Transformation Is Dead! Welcome Intelligent Transformation.

Fri, 07 Nov 2025 18:34:35 GMT

BLUF | Digital Transformation gave us tools. Intelligent Transformation gives us wisdom. The future isn’t about doing more digitally; it’s about deciding more intelligently.

Most organizations have spent the past decade chasing Digital Transformation. We’ve modernized systems, moved to the cloud, automated workflows, and digitized many processes that once lived on paper. We did what we needed to do… and it sort of worked.

But now, it’s time to move on. Digital Transformation isn’t failing. It’s finished. It’s run its course.

The Evolution | From Digitizing to Thinking

When Digital Transformation began, it was the right response to the problems of the time. We had paper-heavy workflows, disconnected systems, and data locked inside silos. We saw the internet, cloud computing, and automation as the tools that could finally pull us forward.

So we digitized. We turned forms into fields, meetings into dashboards, and records into databases. We became faster, more efficient, and better connected. It was exactly what we knew how to do, based on what we knew at the time. But transformation doesn’t stop when technology changes, it evolves when our understanding changes.

Today, in 2025, the boundary of “digital” has been reached. We can automate almost anything, but that doesn’t make us more intelligent. We’ve connected every system, yet we still struggle to connect decisions. The real challenge isn’t doing things faster it’s doing the right things smarter .

We’ve entered a new phase. The opportunity is to up our game moving from digitizing processes to "intelligizing" systems. Even after a decade of investment, only about 48% of digital transformation projects succeed a clear sign that the digital era has reached a plateau. Coherent Solutions, 2025

Defining Intelligent Transformation

Intelligent Transformation is about combining human judgment, machine learning, and contextual data creating systems that are adapting, learning, and improving. It’s the shift from activity to intelligence from dashboards that report the past to ecosystems that anticipate the future.

Comparing Digital to Intelligent Transformation (by J Eselgroth)

Where Digital Transformation built efficiency, Intelligent Transformation builds advantage.

Digital was about doing things digitally . Intelligent is about deciding things intelligent ly .

The rise of AI adoption underscores this shift, as an example the IT and telecom sectors alone report a 38% AI adoption rate and forecast over $4.7 trillion in gross value creation by 2035. Netguru, 2025

A Framework | The APEX Process

To help organizations move toward that next level, I use a framework called APEX , a four-stage process describing how to evolve from wherever you are on the transformation continuum toward the upper-right quadrant, where efficiency and intelligence meet. What's the efficiency matrix? Learn more in The Unending Quest for Efficiency blog article.

The APEX Process | Where insight learns. And learning becomes Advantage. (by J Eselgroth)

Each of the four stages, Architect, Position, Engineer, and eXcel , represents a disciplined action that builds toward Intelligent Transformation. What connects and empowers them all is the X , powered by the three E’s :

eXchange | the flow of data, context, and learning between systems and people.
eXponential | the compounding improvement that occurs as intelligence scales through feedback and adaptation.
eXperience | the human element that guides, governs, and trusts the system.

Together, these create the multiplier effect that transforms APEX from a process into a living framework for continuous intelligence.

Architect | Define intent, context, and purpose. Align the 5Ps (People, Policy, Process, Partners, and Platforms) to create an intelligent blueprint. This is where vision meets structure and where transformation gains clarity of direction. The Exchange begins here as insight and intent inform the design.
Position | Set the conditions for success. Translate the blueprint into readiness. Align teams, data, and technology to operate intelligently. Positioning is both strategic and systemic; it ensures governance, capability, and culture are in place before scaling. The Experience element ensures that human context and capability remain in constant dialogue as readiness evolves.
Engineer | Build with precision and intelligence. Activate the design through AI, automation, and cloud-native architectures. This is the moment when insight becomes capability, when systems learn, adapt, and deliver real-time decision support. Here, the Exponential effect accelerates learning, embedding signals from feedback loops into operations.
eXcel | Sustain and scale. Monitor, refine, and improve through feedback loops that turn data into insight and insight into continuous learning. Excel transforms transformation itself into a living, evolving capability. The three E’s operate together, multiplying impact as knowledge, trust, and performance expand.

Architect with intent . Position with intelligence . Engineer with precision . eXcel with purpose . All powered by the X , the eXchange, eXponential, and eXperience that drive Intelligent Transformation. That is the rhythm of Intelligent Transformation.

Why It Matters Now

Every organization sits somewhere on the 3×3 matrix of digital maturity and efficiency. Some are modernized but inefficient. Others are efficient but lack intelligence. Many are stuck in the middle, busy but blind. For instance, a 2025 McKinsey report shows only 28% of organizations have effectively embedded generative AI into business processes, proof that most are still bridging the gap between experimentation and integration McKinsey, 2025 .

The Digital Efficiency Matrix (left). Your nonlinear journey to being a leader (right) (image by J Eselgroth)

The good news: Intelligent Transformation isn’t linear. You don’t have to finish “digital” before starting “intelligent.” You can begin from any point (e.g. legacy, cloud, or hybrid) as long as you’re willing to connect actions to outcomes.

APEX helps organizations pivot from any position toward that upper-right quadrant, where systems are learning, decisions are accelerating, and leadership regains time for what matters most: judgment, context, and impact.

The Future | From Awareness to Advantage

Digital Transformation gave us tools. Intelligent Transformation enables us to gain wisdom. The organizations that win this decade won’t just be digital, they’ll be decisive. They’ll know how to turn information into action, how to embed learning into every decision, and how to move faster with confidence.

Transformation was about change. Intelligence is about progress.

References

GenAI in Action: 5 Steps to Move from Adoption to Advantage

Fri, 07 Nov 2025 18:24:54 GMT

BLUF | Turning GenAI pilots into scalable, trusted systems enhancing human productivity and organizational intelligence.

From Knowing to Doing

Most organizations have already met GenAI, but few have learned how to work with it. Across both public and private sectors, pilots are everywhere. Tools are multiplying. Yet measurable progress remains limited.

The challenge isn’t awareness. It’s application.

Real GenAI advantage comes when experimentation gives way to execution, when structure, purpose, and people align to make intelligent transformation possible.

Last time, in GenAI at Work: 5 Observations on Overcoming the Adoption Gap , I focused on what I’ve seen inside organizations wrestling with adoption. This piece builds on those lessons and shifts the lens from observation to action, from identifying patterns to creating progress.

Every successful transformation I have seen shares one truth. It doesn’t start with technology. It starts with structure.

That structure is captured in the 5Ps of Intelligent Transformation : People, Policy, Process, Partners, and Platforms. They form the foundation for every change initiative and frame how each of the five steps ahead takes GenAI from pilot to performance.

Because the goal isn’t to deploy GenAI everywhere. It’s to deploy it intelligently, in ways that strengthen how people think, decide, and deliver.

Step 1: Define the Work Before the Tool

Don’t automate confusion. Understand it first.

Every organization has hidden knowledge locked inside policies, workflows, and decisions. The first step toward intelligent transformation is documenting that knowledge so you can see where GenAI fits and where it doesn’t.

This is where the 5Ps come in. They reveal how people interact with policies, how processes depend on partners, and how platforms enable or constrain the mission.

When you map your As-Is and define your To-Be through the 5Ps lense, you create a clear blueprint for transformation. That clarity feeds your Business Body of Knowledge (BBoK) , the living record of how your organization works. It becomes the foundation that makes GenAI relevant, contextual, and trusted.

Step 2: Pair People and AI Intentionally

Match the intelligence to the task and center people in the middle of it. Not every problem needs the same kind of intelligence. Some tasks require precision and control; others need creativity and exploration.

Think of this as a triangle of capability :

On the left side is deterministic AI | predictable, rule-based systems handling structured, repeatable work.
On the right side is non-deterministic AI | adaptive, generative systems interpreting context and create new possibilities.
At the top sits people | providing direction, judgment, and validation.

The Triangle of Capability (Made with GenAI)

When these three forces work together, the organization executes work and builds capability at the same time. Deterministic systems ensure reliability and scale. Non-deterministic systems introduce adaptability and insight. People provide purpose and oversight, keeping both aligned to mission and values. This triangle creates balance. It ensures automation does not erase accountability and creativity does not override control.

The result is a human-centered system that integrates precision and imagination, scaling what works while continuously learning what could work better.

Step 3: Upskill for Collaboration, Not Compliance

Technology doesn’t replace critical thinking. It demands more of it. GenAI works best when people know how to work with it. That means helping teams understand what the tool can do, where it adds value, and how to validate its output. This is not about teaching prompts; it is about building confidence and curiosity. Upskilling the workforce is now a business imperative. Encourage employees to question, test, and iterate. Critical thinking turns GenAI from a novelty into a collaborator.

When teams see that AI amplifies rather than replaces their expertise, adoption accelerates. People begin to connect their individual contributions to the organization’s larger outcomes improving both productivity and trust.

Step 4: Measure What Matters in a Two-Way World

We are no longer giving commands to systems; we are having conversations with them. Traditional technology followed a one-way path; we input, it outputs. GenAI changes that. It is now a dialogue between human intent and machine intelligence.

Organizations must redefine what productivity means in this new relationship. Internally, that may look like reduced cycle times, faster insights, and higher-quality deliverables. Externally, it may mean better customer experiences, stronger sales, and greater innovation.

These outcomes ultimately align with two macro goals: growth and efficiency. Growth through improved products, services, and responsiveness. Efficiency through reduced rework, smarter resource use, and clearer decision-making.

The organizations measuring both will see GenAI’s true value unfold.

Step 5: Build, Measure, Learn, Rebuild, Scale

Adoption is not an event. It is an ecosystem. GenAI maturity grows through iteration. The most effective organizations establish learning loops (e.g. build, measure, learn, rebuild, scale) embedding reflection and refinement into every cycle.

Governance and experimentation must coexist. Each new use case becomes an opportunity to update the BBoK, refine the 5Ps, and strengthen alignment between people and platforms.

When adoption becomes a continuous learning system, transformation no longer depends on big launches or rigid milestones. It becomes a living process adapting as fast as the world around it.

The Learning Loop (Made with GenAI)

From Adoption to Advantage

Every organization is somewhere along this journey. Some are still experimenting. Others are scaling what works. The difference between the two isn’t budget or technology. It is structure, intention, and the discipline to learn.

GenAI doesn’t replace human intelligence; it extends it. The organizations learning how to combine both will not just move faster. They will move smarter.

Remember | Start small . Map what matters. Build your knowledge before your automation.

That is how adoption becomes advantage.

GenAI at Work: 5 Observations on Overcoming the Adoption Gap

Fri, 07 Nov 2025 18:17:39 GMT

BLUF | Organizations don’t fail at GenAI because of the technology—they fail because they skip the foundations of intelligent transformation.

GenAI isn’t stuck in pilot mode because it lacks capability. It’s stuck because most organizations treat adoption as a sprint, not a system. They rush to deploy tools without first aligning people, process, and purpose. Across both public and private sectors, I’ve seen that closing the GenAI adoption gap requires more than enthusiasm. It takes structure, trust, and an understanding of how humans and machines truly work together.

These five observations distill what separates experimentation from execution and how leaders can turn GenAI from an experiment into an advantage.

Observation 1: Understanding the As-Is

Before introducing AI, every organization must understand its current state. Even with digital tools, many still rely on manual synthesis, fragmented documentation, and human memory.

Using the 5Ps (People, Policy, Process, Partners, & Platforms), I’ve helped teams map workflows, stakeholders, and systems, surfacing hidden redundancies and dependencies. This exercise often leads to the creation of a Business Body of Knowledge (BBoK) , a structured repository that documents the who, what, and why behind critical activities.

The BBoK becomes the critical precursor to transformation:

It codifies tribal knowledge and clarifies business logic.
It provides context for generative AI, allowing it to understand relationships, terminology, and decisions.
It aligns people and policy around a shared understanding of the business ecosystem.

Key insight: You can’t intelligently transform what you haven’t meaningfully mapped.

Observation 2: Bringing AI to the Table

Once the foundation is in place, generative AI can join as a new kind of teammate . The most successful transformations start small, piloting use cases where AI accelerates understanding, synthesizes insights, or generates first drafts.

AI tools are guided using the BBoK. Prompts, personas, and contextual cues are refined so outputs align with business standards and tone. Each iteration strengthens the repository, creating a feedback loop of learning.

Across clients, I’ve seen the human-AI relationship follow a familiar pattern: forming, storming, norming, performing . Early trials are clumsy. Mid-way, patterns emerge. Ultimately, AI becomes a trusted collaborator, augmenting human productivity rather than mimicking it.

This isn’t automation for efficiency’s sake. It’s augmentation for adaptability and intelligence .

The GenAI Human Teaming Maturity Curve (Made with GenAI)

Observation 3: The Evolution of Team Mindset

Transformation is as much cultural as it is technical. Teams often begin by overestimating AI (“Let’s see what it can do”) or dismissing it outright. Real progress happens when they ask: “What can we do with it?”

The BBoK plays a quiet but powerful role here. By encoding business rules and relationships, it becomes a teacher to both humans and machines. Each AI-generated output is contextualized, traceable, and improvable.

Over time, skepticism gives way to exploration. I’ve witnessed light bulb moments when people see AI turn complexity into clarity or surface insights no one had noticed. The transformation becomes behavioral. Curiosity replaces caution.

Observation 4: Results and Impact

The impact across organizations is consistent and meaningful:

Cycle times drop. Tasks that once took days now take hours.
Quality improves. Consistency and accuracy rise as AI learns from human feedback.
Confidence grows. Teams begin to trust both the data and their own decisions.

The Business Body of Knowledge evolves into a living decision backbone . It’s no longer static documentation but a continuously curated ecosystem, pruned, refined, and enriched by daily interaction. Generative AI becomes the bridge connecting data, knowledge, and productivity .

The care and feeding of the BBoK become a shared responsibility. It isn’t a deliverable. It’s a living system.

Observation 5: The Bigger Lesson

Intelligent transformation isn’t about deploying AI. It’s about designing for continuous learning.

The biggest lesson: Build your foundation before you automate. A well-structured body of knowledge gives AI context. Context gives decisions meaning.

This journey requires leadership, patience, and partnership. Machines may learn faster, but humans learn wiser. The organizations that master both will outthink, not just outwork, their competitors.

The People, Policy, Proces, Partners, & Platforms Bridge from Experimentation to Execution (Made with GenAI)

FInal Thoughts: From Awareness to Advantage

Across the organizations I’ve supported, intelligent transformation has redefined what progress looks like. By combining the 5Ps evaluation factors, a living BBoK, and generative AI, they’ve turned awareness into advantage.

The true outcome isn’t speed or savings. It’s shared understanding and decision confidence .

To begin your own intelligent transformation:

Start small .
Map what matters .
Build your knowledge before your automation.

Intelligent transformation begins with curiosity and grows through care.

Data-Driven Theater vs. Data-Driven Decision Making

Fri, 07 Nov 2025 18:10:37 GMT

BLUF | Many organizations confuse looking data-driven with being data-driven; only genuine decision ecosystems deliver measurable impact and resilience.

How do you know whether your organization is practicing data-driven decision making...or simply performing data-driven theater? The difference matters more than ever. Despite years of investment, only 37% of enterprises describe themselves as data- and AI-driven in 2025¹. This gap shows how easy it is for organizations to fall into the trap of looking data-driven without actually becoming so. Being data-driven represents potential energy; Decision Intelligence is the actuation of that energy, turning awareness into intelligent action. The real challenge lies in moving past appearances and ensuring every data point connects directly to decisions that matter.

The Illusion: Theater

Data-driven theater thrives on optics. Organizations build dashboards, host reviews, and generate slides that look convincing but fail to guide action. Leaders may equate more slides/charts with better insight, yet numbers without context provide little direction. Vendors showcase glossy dashboards highlighting seasonal fluctuations or aggregate outputs but miss the deeper “so what” behind the numbers. Theater delivers awareness, but not accountability. It creates the feeling of progress while reinforcing cycles of performative analysis. In these environments, unresolved performance gaps linger, decisions default back to intuition, and organizations drift further from genuine improvement. Vanity measures dominate, and the harder questions, are we achieving our goals, and why or why not, go unanswered.

Case Study: The Cost of Awareness

In 2018, I was part of a large interagency team preparing a 93-slide executive briefing for a four-star general. My own contribution was only two or three slides, but the experience was eye-opening and it would turn out this wasn't a one off experience. As we moved through dry runs and prep sessions, I started wondering; how much collective time, talent, and cost went into producing this single presentation?

So, I ran the numbers.

The analysis painted a revealing picture of the hidden cost of “data-driven theater.”

Total preparation cost: $241,881.80 (2018 dollars)
Total preparation time: 772 hours — nearly five full work months
Meetings and dry runs: 14 sessions totaling $43,433
Developer-to-bureaucracy ratio: 1:3 — for every hour spent creating slides, three were spent reviewing, vetting, or updating them

The resulting deck did its job, it informed senior leadership and reflected the organization’s current status. Yet it was fundamentally a rearview-mirror exercise , focused on awareness rather than decision-making. It offered insight into what had happened, not what should happen next.

That realization marked a turning point for me. The effort, while well-intentioned, revealed the structural inefficiency of many “data-driven” processes: enormous energy devoted to building artifacts of understanding, but little investment in converting those artifacts into action. The real opportunity wasn’t in making better slides...it was in designing better systems for translating data into decisions.(image generated with AI)(image generated with AI)

The Reality: Decisions

Breaking free from theater requires a shift from demonstration to deliberation. Real data-driven decision making begins with a clear decision statement: what must we decide, why now, and what success looks like. From there, organizations build reliable pipelines and embed feedback loops to test whether actions change outcomes. Consider a small defense agency, which built forecasting models aligning missions, funding, and identification priorities. Leaders moved from manual data pulls and Excel with static slides to live data feeds, enabling real-time prioritization of global recovery operations. Or the Air Force, which developed a $954M, 30-year range modernization strategy by integrating cost, mission, safety, and joint-service priorities. This effort represented an early form of Decision Intelligence (DI)...integrating diverse data sources and foresight modeling to guide long-term planning. These cases show what is possible when organizations stop admiring dashboards and start embedding data into their operating rhythm building an action-to-outcome DI process. Technology alone does not create the difference; intentional strategy and accountability do.

Spotting Substance

Leaders can tell the difference by asking: is this a stat, a metric, or a performance measure? Stats describe, metrics compare, and performance measures drive action over time. When leaders see a number, they should be able to connect it to a decision, an accountable owner, and a timeframe for change. Think of data and decisions as two sides of the same coin. One side shows the numbers and metrics, the visible data. The other side reflects the confidence, quality, and governance that make those numbers trustworthy. For instance, control charts help distinguish signal from noise, ensuring leaders do not overreact to normal fluctuations. Equally important, confidence in decisions depends on confidence in data quality, the other side of the coin. Without governance, metadata, and validation, even the best-looking metric erodes trust. Dashboards failing to answer “what happens next” are theater. Dashboards highlighting gaps, enabling response, and tracking improvements are substance. These capabilities form part of the foundation for Decision Intelligence, where data meets intent through structured reasoning and feedback.

A dual-sided emblem of trust and logic, where confidence meets data-driven decision-making (image generated with AI)

The Digital Efficiency Journey

Authentic change means embracing intelligent transformation, where data and decisions move together across the enterprise. The journey toward the Leader box in the digital efficiency matrix (learn more about the matrix here The Unending Quest for Efficiency: Navigating Beyond Digital Transformation ) emerges when organizations minimize theater and maximize impact, ensuring insights produce outcomes improving missions, operations, or customer experiences. The journey can be framed through the 5Cs of Intelligent Transformation:

Cognition : Define outcomes, decision rights, and guardrails, aligning augmented intelligence with strategy and objectives.
Capability : Develop people and leadership skills, creating pathways that embed competency and sustain improvement.
Culture : Reinforce behaviors, incentives, and adoption patterns that make data-driven habits part of everyday work.
Connectivity : Integrate data sources, platforms, processes, and workflows, enabling interoperability and automation across functions.
Continuity : Establish governance, risk, and resilience, embedding ethics, security, and continuous improvement into decisions.

Purpose: turn data, AI, and operations into reliable decisions across the lifecycle. Outcome: insight amplified, decisions empowered. The 5Cs provide a practical compass for leaders who want to move beyond vanity dashboards toward measurable transformation, and together, they serve as an operating model for implementing Decision Intelligence at scale.

Leadership Imperative

Data theater is not a method. It dazzles but rarely delivers measurable improvements or enduring organizational learning. General (ret.) Gordon R. Sullivan, in his book Hope Is Not a Method (1997), emphasized that hope is not a method². The same lesson applies here: theater cannot replace genuine strategy. Leaders must identify evangelists, launch manageable initiatives, and build momentum through iteration. Each cycle compounds, shifting culture from reactive demonstration to proactive decision-making. Decision Intelligence transforms theater into traction, embedding forecasting, scenario planning, and consequence modeling directly into leadership forums. Evidence must replace entertainment, and courage must replace complacency. Theater entertains, but decision-making wins campaigns. Organizations that separate optics from impact build resilience, trust, and enduring strategic advantage.

So, ask yourself: What’s our developer-to-bureaucracy ratio?

References

MIT Sloan Management Review. Five Trends in AI & Data Science for 2025 by Randy Bean. Link Used for: 37% of enterprises describe themselves as data- and AI-driven in 2025.
Sullivan, Gordon R. Hope Is Not a Method: What Business Leaders Can Learn from America’s Army. 1997. Link Used for: “Hope is not a method” leadership lesson.

Bridging the Gap: Turning Your Vision into Reality

Fri, 07 Nov 2025 18:05:55 GMT

BLUF | Bridging the gap between strategy and execution requires clarity, alignment, and data-driven action to turn vision into results.

The world of business of government is littered with well-intentioned strategies that never see the light of day. The idea that 'strategic planning is dead' caught my attention in a recent Fast Company article by Lisa Bodell[1]. It got me thinking about the disconnect between strategy and execution. But why? In addition to Lisa’s points, it's not because strategy itself is flawed, but rather the execution that often falls short. The gap between a brilliant idea and tangible results can feel like an insurmountable chasm.

Here are some statistics from Lisa Bodell’s article:

48% of all organizations fail to meet at least half of their strategic targets.
61% of C-level executives acknowledge their firms struggle to bridge the gap between strategy formulation and day-to-day implementation.
47% of team members don’t know how their leadership is tracking the strategy execution.

But what if it doesn't have to be? What if turning strategy into reality is less about grand pronouncements and more about understanding the mechanics of action? The ability to connect the high-level vision to the boots-on-the-ground implementation is where the true magic happens. It's about understanding how the 'rubber meets the road' and propels the organization forward.

Let’s explore how to bridge that gap and delve into the common pitfalls that derail even the best-laid plans. Then provide practical steps to ensure your strategies don't just gather dust on a shelf. I'll draw upon my own experiences, which include the successful transformation of Air Force small arms firing ranges and the creation of a unified executive decision support system. These insights will provide you with actionable tips to empower you to drive real change.

Whether you're a seasoned executive or an aspiring leader, this guide equips you with the tools and mindset to move beyond the talk and into the realm of tangible results. It's time to turn your strategies into reality.

The Disconnect: Why Strategies Fail

Before we dive into the solutions, let's examine why so many strategies falter. The reasons are multifaceted, but some common culprits include:

Lack of Clarity: The strategy itself may be vague or poorly defined, leaving teams unsure of how to proceed.
Misalignment: Different departments or individuals may have conflicting interpretations of the strategy or prioritize their own goals over the overarching vision.
Insufficient Resources: The strategy may require resources (financial, personnel, technological) that are not readily available.
Resistance to Change: People are naturally resistant to change, and a new strategy can disrupt established routines and power structures.
Failure to Measure Progress: Without clear metrics and regular monitoring, it's impossible to know if the strategy is on track or needs adjustments.
Confusing Motion with Productivity: The hustle and bustle of meetings, discussions, and busy work can create an illusion of progress. However, true productivity lies in tangible outcomes and measurable results.

Don't Confuse motion with productivity (Image generated with Gemini)

By recognizing these pitfalls, leaders can proactively address them and increase the likelihood of successful strategy execution.

Bridging the Gap: A Framework for Success

Turning strategy into reality requires a systematic approach that addresses these challenges. Here's a framework to guide you:

Clarity is king (Image generated with Gemini)

Clarity is King: A crystal-clear strategy with well-defined goals, objectives, and KPIs is essential. Everyone involved should understand the 'why' and their role. This alignment fosters ownership and empowers individuals to drive the strategy forward.
Align and Communicate: Foster a culture of open communication and collaboration. Break down silos and ensure everyone is working towards the same goals. Regularly communicate progress and address any concerns or roadblocks. Radical transparency is paramount.
Resource Strategically: Conduct a thorough assessment of the resources required to implement your strategy. Understand the people, policy, process, partners, and platforms (5Ps) that are related to the strategy. Secure the necessary funding, personnel, and technology upfront, or develop a plan to acquire them as needed.
Embrace Change Management: Recognize that change is a process, not an event. Anticipate resistance by understanding the 5Ps mentioned earlier and proactively address it through clear communication, training, and support. Celebrate early wins to build momentum and enthusiasm.
Measure, Monitor, and Adapt: Establish clear metrics to track progress and regularly review performance data. Be prepared to adjust your strategy as needed based on feedback and changing circumstances.

Actionable Tips for Leaders

Embrace a Systems Thinking Approach: Understand how different parts of your organization interact and impact each other. Recognize that even small actions can have ripple effects.
Adopt a Decision Driven Data Mindset: Base your decisions on evidence and insights, not just gut feelings. Leverage data to track progress, identify roadblocks, and make informed adjustments.
Foster a Culture of Transparency and Communication: Break down silos and encourage open dialogue. Ensure everyone understands the strategy and their role in its execution.
Start Small and Iterate: Don't try to tackle everything at once. Begin with a pilot project or a specific area where you can demonstrate success and build momentum.

Start small and iterate, from Idea to full maturity (Image generated with Gemini)

Celebrate Wins and Learn from Setbacks: Acknowledge progress and recognize the efforts of your team. When things don't go as planned, analyze what happened and use those lessons to improve.
Lead by Example: As a leader, your actions speak louder than words. Demonstrate your commitment to the strategy and inspire others to follow suit.
Cultivate a Culture of Risk-Taking: Encourage experimentation and innovation. Celebrate both successes and well-intentioned failures. Publicly acknowledge those who take risks, even if the outcome isn't perfect. This fosters an environment where people feel safe to share ideas and push boundaries, ultimately driving progress and growth.

My Personal Journey: From Data Silos to Strategic Action

My journey towards turning strategy into reality has been shaped by diverse experiences, each offering unique lessons. One particularly impactful endeavor involved supporting a small defense organization in their quest to streamline decision-making and enhance data utilization.

Situation: The organization, like many others, grappled with siloed data sources and a lack of cohesive insights to inform strategic decisions.
Task: As a trusted advisor to the Chief Data Officer, I led a team that was tasked with developing and implementing a comprehensive data strategy to address these challenges.
Action: We spearheaded the integration of six disparate data sources into a unified executive decision support system, aptly named the "common operating picture." This involved close collaboration with stakeholders across departments to understand their specific data needs, designing a scalable data architecture, and implementing data pipelines for real-time insights. Recognizing the critical importance of data quality for confident decision-making, we also developed the organization's first data health dashboard to provide transparency into data integrity.
Result: The common operating picture dramatically reduced the time required for information gathering, empowering senior leaders to make faster, more informed decisions. The data health dashboard fostered trust in the insights generated from the system, further enhancing the effectiveness of the decision-making process. This initiative not only improved efficiency but also had a ripple effect, benefiting the organization's customers and stakeholders.

Beyond this specific project, my journey has been marked by a commitment to developing decision intelligence strategies and tools, fostering digital and data literacy, and championing agile DataOps processes. These experiences have reinforced the transformative power of data-driven approaches in turning strategy into tangible outcomes.

Final Thoughts

Turning strategy into reality is not a mystical art reserved for a select few. It's a discipline that can be learned and mastered. By embracing a systematic approach, fostering a culture of collaboration, and leveraging data-driven insights, you can bridge the gap between vision and execution.

Remember, the journey from strategy to reality is not always linear. There will be challenges and setbacks along the way. But with perseverance, adaptability, and a commitment to continuous improvement, you can achieve remarkable results.

So, take that bold step. Embrace the challenge. Turn your strategies into reality and lead your organization to new heights of success.

[1] https://www.fastcompany.com/91176813/strategic-planning-is-dead-this-is-why-we-need-to-revive-it

The Unending Quest for Efficiency: Navigating Beyond Digital Transformation

Fri, 07 Nov 2025 17:30:18 GMT

BLUF | Digital transformation is not the finish line. True leaders pursue continuous improvement through efficiency, innovation, and adaptability.

The year 2013 marked a turning point in the business world with the introduction of the term "digital transformation." 1 Over a decade later, a striking “70% of companies either have a digital transformation strategy in place or are actively working on one” 2 . However, as the digital landscape becomes increasingly saturated, simply having a strategy is no longer enough. Organizations must go beyond initial adoption and focus on continuous innovation and optimization to extract maximum value from their investments. Even for the “21% of companies that believe they've completed their digital transformation” 3 , the journey is far from over. The question for these organizations is not 'Are we done?' but rather 'What's next?' The focus must shift from achieving a static state of digital maturity to embracing a mindset of continuous evolution and adaptation.

The Digital Efficiency Matrix: A Roadmap for Progress

To understand where we stand and where we can go next on this journey, we introduce the Digital Efficiency Matrix. This 3x3 grid provides a comprehensive view of an organization's digital maturity and efficiency, guiding us toward the ultimate goal of becoming leaders in our respective industries.

Understanding the Axes

Efficiency (X-Axis): This axis measures how effectively an organization utilizes its resources (time, money, labor, etc.) to achieve its goals. It ranges from low efficiency, characterized by high manual labor, low compute power, and high costs, to high efficiency, marked by minimal manual labor, extensive compute power, and lower costs.

Digital Maturity (Y-Axis): This axis represents the extent to which an organization has adopted and integrated digital technologies into its operations. It spans from paper-based processes to intelligent/autonomous systems that leverage AI and machine learning.

The Journey So Far: The Inner 2x2 Matrix

Traditionally, organizations have been assessed using a simpler 2x2 matrix, focusing on basic levels of digital maturity and efficiency. This matrix identified four quadrants:

Laggards: Low efficiency, low digital maturity
Stragglers: Low efficiency, moderate digital maturity
Experimenters: Moderate efficiency, low digital maturity
Implementers: Moderate efficiency, moderate digital maturity

The Path Forward: The Expanded 3x3 Matrix

The expanded 3x3 matrix offers a more nuanced perspective, revealing new possibilities and potential paths for organizations to advance:

Early Adopters: Moderate efficiency, high digital maturity
Challenged: Low efficiency, high digital maturity
Streamliners: High efficiency, moderate digital maturity
Innovators: Moderate efficiency, high digital maturity
Leaders: High efficiency, high digital maturity

Charting Your Course: Where Are You Now, and Where Can You Go?

The first step in any successful strategy is understanding your current position. By identifying your organization's location within the Digital Efficiency Matrix, you can gain valuable insights into your strengths and weaknesses and chart a course toward greater efficiency and digital maturity.

Descriptions and Strategies

Laggards: Organizations in this location are heavily reliant on manual, paper-based processes. They often experience high operational costs, slow turnaround times, and a higher risk of errors due to the manual nature of their work. To progress, they should focus on basic digitization efforts to kickstart their digital journey.
Experimenters: These organizations are taking their first steps into the digital realm, experimenting with new tools and processes. While they are making some progress, their efforts are often scattered and not fully integrated. Investing in digital tools and training can help them improve efficiency and move towards higher digital maturity.
Early Adopters: These organizations have embraced digital technologies early on but may lack a cohesive strategy. They are seeing positive results in specific areas but have the potential to achieve even greater efficiency by expanding their digital initiatives and developing a comprehensive digital strategy.
Stragglers: These organizations have adopted some digital tools but are not using them effectively, leading to limited efficiency gains. This could be due to a lack of training, poor implementation, or resistance to change. To overcome these challenges, they should focus on optimizing processes and enhancing their digital capabilities.
Implementers: These organizations are systematically implementing digital technologies and are seeing moderate improvements in efficiency. To maintain momentum, they should continue to refine and expand their digital initiatives.
Streamliners: These organizations have well-integrated digital systems and automated workflows, leading to high efficiency. They use data to make informed decisions and continuously optimize their processes. To further advance, they can enhance digital innovation and explore emerging technologies.
Challenged: These organizations possess advanced technology but face challenges in implementation and achieving efficiency. Addressing inefficiencies by optimizing digital tools and processes is key to unlocking their full potential.
Innovators: Organizations in this category exhibit high digital maturity and moderate efficiency. They are innovative but may need to enhance efficiency by focusing on scaling innovations and improving process efficiency.
Leaders: These organizations are at the forefront of digital transformation and efficiency, leading in both digital innovation and process optimization. To maintain their leadership position, they must continue to push boundaries, set benchmarks, and embrace a mindset of continuous evolution and adaptation.

Overcoming Challenges and Embracing Continuous Improvement

While the journey towards digital maturity and efficiency is promising, it's not without its challenges. Organizations may encounter obstacles such as resistance to change, a lack of skilled talent, and difficulty measuring ROI. Strong leadership, a clear vision, and a commitment to continuous improvement are essential for overcoming these hurdles and achieving long-term success. (read about why the 5Ps are essential to Digital Excellence )

The Role of Leadership

Leaders play a crucial role in driving digital transformation and fostering a culture of continuous improvement. They must champion the adoption of new technologies, invest in employee training and development, and create an environment that encourages experimentation and innovation.

The Power of Continuous Improvement

In the dynamic digital landscape, there is no finish line. Organizations must embrace a mindset of continuous improvement, constantly seeking ways to optimize processes, leverage new technologies, and enhance efficiency. By doing so, they can stay ahead of the curve, adapt to changing market conditions, and achieve sustainable growth.

Conclusion

The pursuit of efficiency is an ongoing journey, not a destination. While digital transformation has been a significant step for many organizations, it's essential to recognize that the quest for improvement never ends. By leveraging the Digital Efficiency Matrix, organizations can gain a clearer understanding of their current state, identify areas for growth, and develop strategies to achieve higher levels of digital maturity and efficiency. With a focus on continuous innovation and adaptation, organizations can navigate the complexities of the digital age and thrive in the years to come.

Footnote

2013 The term “Digital Transformation” is coined ( Hatchworks )
70% of companies either have a digital transformation strategy in place or are working on one. ( Tech Pro Research )
21% of companies believe they’ve completed their digital transformation. ( Forrester )

Decision Intelligence: The Missing Piece in AI Orchestration?

Fri, 07 Nov 2025 17:09:21 GMT

BLUF | Decision Intelligence bridges data, technology, and human insight to orchestrate AI effectively, transforming fragmented efforts into real-world outcomes.

In the world of AI, "Most AI projects fail. Some estimates place the failure rate as high as 80%" . Simultaneously, the buzz around Decision Intelligence (DI)* is growing . Could DI be the key to AI orchestration? In my view, the answer is both yes and sort of.

AI orchestration, while promising, presents a myriad of challenges. On the technical side, the complexity of managing diverse AI components, ensuring scalability with growing data volumes, and integrating with existing systems can be daunting. On the human side, there are skill gaps in AI and software engineering, collaboration difficulties between teams, resistance to change due to workflow disruptions, and ethical considerations around data privacy and bias.

The Case for DI

To the "yes", DI brings together the often-disparate elements of data and technology into a unified strategy. It addresses the human element – the "squishy things" like subjectivity and intuition – that are often overlooked in AI implementations. Think of DI as the glue that holds everything together, focusing our efforts on making sense of the data available to us and transforming it into improved outcomes. When faced with the challenge of finding a needle in a haystack, DI helps us "burn the hay" – eliminating irrelevant data and focusing our efforts on the most valuable information. In essence, DI helps us cut through the noise and focus on finding the right data.

When trying to find the needle (the right data) in the haystack, we need to ask ourselves “what if we burned the hay?” (image developed with DALL-E)

However, to the "sort of", implementing DI isn't as simple as plugging it in and expecting magic. It requires a holistic approach, what I call the "digital transformation (DT) of becoming a data-driven decision organization." This means considering the impact on people, policy, process, partners, and platforms – the 5Ps.

The 5Ps: A Framework for Bridging AI Orchestration Challenges to Impactful Outcomes with DI

The 5Ps framework, when applied through the lens of Decision Intelligence (DI), can effectively bridge the gap between AI orchestration challenges and impactful organizational outcomes:

People: DI empowers individuals by fostering a data-driven culture and enhancing decision-making skills. By investing in training and development, organizations can cultivate a workforce proficient in both AI and software engineering, mitigating the skills gap and fostering collaboration. This leads to improved communication, streamlined workflows, and reduced resistance to change, ultimately driving higher adoption rates and successful AI implementation.
Policy: DI-driven policies establish clear ethical guidelines for AI, ensuring responsible and unbiased decision-making. By defining roles and responsibilities, organizations can create a governance framework that promotes transparency and accountability, mitigating risks and building trust in AI-powered systems. This results in ethical AI practices, reduced legal and reputational risks, and increased stakeholder confidence.
Process: DI enables the design and optimization of processes that seamlessly integrate AI into existing workflows. By leveraging change management principles and involving employees in the process, organizations can minimize disruptions and ensure smooth transitions. This leads to increased efficiency, reduced errors, and improved decision-making across the organization.
Partners: DI facilitates effective collaboration with internal and external partners, leveraging their expertise to overcome technical challenges and accelerate AI implementation. By identifying and managing partners, organizations can access specialized skills, knowledge, and resources, leading to faster time-to-market, reduced development costs, and innovative solutions.
Platforms: DI guides the selection and implementation of scalable and flexible AI platforms that align with organizational needs. By ensuring compatibility with existing systems and incorporating robust monitoring and maintenance tools, organizations can maximize the value of their AI investments. This results in improved performance, enhanced scalability, and the ability to adapt to evolving business requirements.

From Lab to Reality: Bridging the Gap

While the 5Ps framework provides a solid foundation for addressing AI orchestration challenges, it's important to acknowledge the gap between theory and practice. Many AI initiatives that show promise in the lab fail to deliver the expected results in real-world scenarios. This can be due to various factors, such as unforeseen technical issues, organizational resistance, or a lack of understanding of the specific business context.

To bridge this gap, organizations can leverage DI to create a feedback loop that continuously improves AI models and their real-world performance. By applying DI principles, businesses can identify and rectify biases, refine algorithms, and ensure that AI systems align with evolving business objectives. This iterative process not only enhances the accuracy and effectiveness of AI but also fosters trust and transparency, crucial factors for successful AI adoption.

Start Small, Win Big

To win over leadership and ensure successful DI adoption, I advocate for a "start small" approach. Begin with small, impactful MVPs (Minimum Viable Products) that demonstrate the value of DI in real-world scenarios. These early wins can build momentum and pave the way for wider adoption. Find one problem the organization has been trying to answer, and start there. Focusing on a single problem instead of trying to boil the ocean allows you to learn and finetune before doing more. The momentum from, hopefully, a successful DI implementation encourages others to participate and simultaneously improve leaderships confidence DI. After all, as I discovered in my master years ago, in order for change to be accepted by all, it must start off small.

AI orchestration (image developed with DALL-E)

"In order for change to be accepted by all, it must start off small" - James Eselgroth

Final Thoughts

Decision Intelligence has the potential to revolutionize AI orchestration. By focusing on the right data and "burning the hay" of irrelevant information, DI empowers organizations to make better, faster, and more informed decisions. But to realize its full potential, organizations need to take a holistic approach, considering the 5Ps and fostering, as Mark Zangari mentioned to me recently, a "decision driven data culture." Remember, start small, win big – and don't underestimate the power of people in the success of your DI journey

*"Decision intelligence (DI) is a practical discipline that advances decision making by explicitly understanding and engineering how decisions are made and how outcomes are evaluated, managed and improved via feedback." - Gartner Glossary

Augmented Intelligence: Leveraging Technology like a Superpower

Fri, 07 Nov 2025 16:38:37 GMT

BLUF | Augmented Intelligence fuses human creativity with machine precision to enhance decisions, amplify insight, and drive smarter outcomes.

Over the last decade, and especially the last 18 months, artificial intelligence (AI) has been heralded as the next great technological revolution. From self-driving cars to virtual assistants, AI promises to transform nearly every aspect of our lives. However, as impressive as modern AI systems are, they still face significant limitations. Limitations such as understanding context, inability to reason, lacking emotional intelligence, and developing strategy. That's where the concept of augmented intelligence comes into play – an approach that combines the strengths of humans and machines to achieve something greater than either could accomplish alone.

What is Augmented Intelligence?

At its core, augmented intelligence is about enhancing human intelligence rather than replacing it. Instead of attempting to create artificial general intelligence that can match or surpass human cognition, augmented intelligence systems are designed to complement and extend our natural capabilities. These systems leverage advanced algorithms, massive datasets, and immense computational power to process information in ways that would be impossible for the human mind alone.

One of the key advantages of augmented intelligence is its ability to handle complex, data-intensive tasks with incredible speed and accuracy. For example, an augmented intelligence system could rapidly analyze millions of medical records, identifying patterns and insights that could lead to breakthrough treatments or more effective preventative care strategies. Another example, you upload a large document into a Large Language Model (LLM) like ChatGPT, Gemini, Claude, or another one and you start asking questions about the document. The LLM parsed and analyzed the document for you, in seconds, so you can ask it questions. This Saves you time, improves your understanding, and probably improves your productivity.

Augmented Intelligence: Where Human Ingenuity Meets Machine Precision (Image created with DALL-E 3)

Augmented Intelligence in Action: Decision Intelligence

One area where augmented intelligence is already making a significant impact is in the field of decision intelligence. Decision intelligence combines various disciplines, including data science, decision analysis, behavioral science, and AI, to help organizations make better, more informed decisions.

Decision intelligence starts by understanding the decision at hand, breaking it down into its core elements: actions, intermediates, externals, and outcomes. This systematic approach ensures that all relevant factors are considered, from the potential choices that can be made (actions) to the dependencies and consequences of those choices (intermediates) as well as external forces and constraints beyond the organization's control aimed at a desired outcome.

At its core, decision intelligence bridges the gap between the non-material aspects of an organization (e.g. people, strategy, doctrine, policy) and the material aspects (e.g. hardware, software, data, analytics, AI). By leveraging augmented intelligence techniques, decision intelligence systems can provide decision-makers with highly accurate predictions, simulations, and recommendations based on vast amounts of data and complex modeling.

The Power of Human-Machine Collaboration

One of the most exciting aspects of augmented intelligence is the potential for true collaboration between humans and machines. While AI systems excel at processing vast amounts of data and performing complex calculations, humans possess unique abilities in areas such as creativity, emotional intelligence, and strategic thinking.

“Computers are great at patterns, humans are great at strategy” - David Epstein, Range

By combining these strengths, augmented intelligence systems can achieve incredible results. Humans can provide the high-level goals, context, and intuition, while the machine learning algorithms and computational power of the augmented intelligence system can rapidly explore countless scenarios and provide data-driven recommendations.

This collaborative approach not only leads to better outcomes but also helps to build trust and acceptance of these advanced systems. When humans are actively involved in the decision-making process, rather than being replaced by opaque "black box" AI, they are more likely to understand and embrace the insights and recommendations provided by the augmented intelligence system.

You're probably already living with augmented intelligence in your day-to-day life right now without even realizing it. Any guesses? If you use Waze, Google Maps, Apple Maps, or any other GPS navigator, you're taking information from a complex supercomputer powered by billions if not trillions of data points to provide you with a recommended path to your destination. But you are in the driver's seat – the computer is providing you, the human, a recommendation based on current conditions. You can either choose to follow it or not. And if you're like me, sometimes when you override the recommendation, you're stuck in really bad traffic. And you think to yourself, "I knew I should've followed Waze."

Leveraging Augmented Intelligence in our daily lives Using a Navigation device (Image created with DALL-E 3)

As we continue to push the boundaries of what's possible with technology, it's becoming increasingly clear that augmented intelligence, rather than pure artificial intelligence, may be the true superpower of the future. By combining the complementary strengths of humans and machines, we can tackle challenges that would be insurmountable for either alone, unlocking new levels of innovation, productivity, and understanding.