Research

ORGANIZATIONAL CONDITIONS FOR THE AI-READY ENTERPRISE
A research project exploring the organizational conditions that determine whether AI capabilities take root and scale.

Organizations are investing heavily in AI, but technology alone doesn't determine whether those investments create meaningful enterprise value. This research explores the less visible factors that can - organizational design, governance, decision rights, culture, leadership, and the conditions that enable experimentation and learning.

Through conversations with senior leaders responsible for introducing, governing, scaling, and creating value with AI, I'm looking for patterns in what works, what doesn't, and why.

Perspectives Informing the Research

The research brings together perspectives from senior leaders and practitioners across:

Enterprise AI and transformation
Security, risk, and AI governance
AI technology and organizational systems
Business architecture and operating models
Organizational change and human capability
Workforce and economic policy
AI literacy and the future of work

Together, these perspectives provide different lenses on the same underlying question: What enables organizations to translate rapidly evolving AI capabilities into sustainable value?

Have relevant experience to share?

I'm continuing to interview senior leaders and practitioners with meaningful responsibility for AI adoption, governance, transformation, or its organizational implications. Interviews are 30 minutes and confidential. Contact me

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Research Update #3: From AI Adoption to Continuous Adaptation

Week of August 24, 2026

This week, several threads in my research began to converge around a larger question. I've been exploring what organizations need to become “AI-ready,” particularly as AI moves from assisting people toward acting with increasing autonomy. I'm increasingly convinced that the challenge isn't simply AI adoption - it's continuous organizational adaptation.

Three ideas stood out this week.

1. The target keeps moving.

Organizations are discovering that AI can't simply be added to existing processes. Workflows, roles, decision rights, and operating models need to change around it.

In 1970, Roger Conant and W. Ross Ashby argued that to effectively manage a complex system, you need an accurate understanding of how that system works. And when the system changes, your understanding has to change with it.

That's the challenge organizations now face with AI: the thing they're trying to manage may be changing faster than their ability to understand and adapt to it.

2. Governance may need to become an adaptive capability.

Cisco recently began deploying personalized AI agents across its workforce of approximately 90,000 people. What's particularly interesting isn't simply the scale, but how Cisco is approaching control.

The agents can have considerable freedom in determining how to accomplish an objective, while consequential actions remain bounded by permissions, policies, human approvals, monitoring, and accountable ownership.

That illustrates an important distinction: Intelligence, autonomy, and authority don't have to increase together. As organizations gain experience and confidence, authority can expand alongside their ability to observe, evaluate, and govern it. Governance becomes less like a fixed rulebook and more like an operating capability that evolves with the technology.

3. Human expertise may be part of the control system.

AI can make experienced people dramatically more productive. But what happens when AI begins performing the work through which inexperienced people traditionally became experts?

If humans increasingly supervise AI, they still need the expertise and judgment to recognize when it's wrong. A human “in the loop” isn't much protection if that person can no longer effectively evaluate the machine.

Where This Leads

That leads to the question I'll be exploring next: What organizational capabilities allow companies to continuously adapt to increasingly capable AI while safely delegating greater autonomy and preserving meaningful human control?

I'm continuing to interview senior leaders who have meaningful responsibility for introducing, scaling, governing, or creating business value with AI. Successes, failures and false starts are all useful.

If you're working through these issues - or know someone who is - I'd welcome the conversation.

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Research Update #2: Can Organizations Keep Pace with AI?

August 21, 2026

Another week of conversations has shifted my thinking about enterprise AI. Last week, I wrote about starting with business problems, creating space for experimentation, and using governance to make that experimentation safe. This week, a larger pattern is emerging: The challenge may not simply be adopting AI. It may be building organizations capable of adapting as quickly as AI itself is changing.

A few observations from this week's conversations:

1. AI transformation may have its own J-Curve

Major technologies rarely create their full value simply by being deployed. The Productivity J-Curve describes a pattern seen with previous technology transformations: organizations invest in and adopt a new technology, but the expected productivity and ROI don't immediately appear. Value comes later, after organizations redesign processes, roles, skills, and structures around the new capability. AI appears to be following the same basic pattern - but potentially at a dramatically faster pace. That raises an uncomfortable question:

Can organizations redesign themselves quickly enough to keep pace with the technology?

If not, some of today's disappointing AI ROI may reflect an adaptation gap, rather than a failure of the technology itself.

2. Autonomy may be a ladder, not a leap

One enterprise example this week offered a useful progression:

Business problem → constrained AI → demonstrated value → guardrails and learning → progressively greater autonomy

Rather than jumping directly to autonomous agents, the organization deliberately expands what AI can do as confidence, capability and controls mature. That suggests an important principle:

The level of autonomy we delegate should grow alongside our ability to govern it.

3. Agentic AI changes the governance problem

Traditional software executes logic we explicitly prescribe. With an AI agent, we increasingly specify the goal and allow the system to determine how to achieve it. That means governance has to address behavior we didn't explicitly program - and may not fully understand.

Recent work in mechanistic interpretability makes this particularly interesting. Researchers are beginning to identify internal representations and computational pathways inside large language models. One implication is especially important:

An AI's explanation of why it did something may not faithfully represent the internal process that actually produced the behavior.

So explainability and auditability may not be the same thing.

4. The emerging issue may be continuous adaptation

Put these ideas together and I'm beginning to wonder whether we're framing AI transformation too much like previous transformation programs - with a future-state operating model we're trying to reach.

What if the target doesn't stay still?

AI capability continues to advance. Organizations redesign around it. Before that redesign is complete, the capability changes again. The leadership challenge may therefore be shifting from:

“How do we successfully adopt AI?” to:

“How do we build an organization capable of continuously adapting as AI itself continues to change?”

That question is becoming one of the central threads I'm exploring.

If you're working through these issues - or know someone who is - I'd welcome the conversation.

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Research Update #1

August 14, 2026

I’m researching why some organizations are able to create enterprise value with AI while others struggle to move beyond individual productivity gains. I’m interviewing senior leaders to understand the organizational conditions that make the difference.

A few themes are beginning to emerge...

1. Productivity isn't the same as Enterprise Value

AI is already helping individuals work faster. What's much harder is using AI to create value across an organization. And, the costs matter - licenses, tokens, infrastructure and support can add up quickly. Productivity gains only matter if the value created exceeds the cost.

2. Start with the Business Problem, not the AI

This keeps coming up in the interviews: organizations frequently struggle to clearly define the problem they want AI to solve. Without that clarity, it's hard to know what to build, how sophisticated it needs to be, or whether it's worth the cost. Start with the business need, then ask what AI can do about it.

3. Experimentation requires Governance

Organizations need to experiment because we're still discovering what AI can do. But agentic AI raises the stakes: agents can access data and systems - and take actions. That means some guardrails need to exist before experimentation begins.

Two approaches I've heard: a) tightly isolated sandboxes, and b) Zero Trust / Least Privilege – that is, give AI access only to the minimum data, systems and actions it actually needs.

The conclusion: Good governance doesn't prevent experimentation - it makes safe experimentation possible.

4. The Issues are Interconnected

Strategy, leadership, culture, governance, decision rights, workflows and operating models keep coming up - but they're not independent. People need psychological safety to experiment. Experimentation needs guardrails. Guardrails require clear decision rights. And those decisions need knowledgeable leaders with executive backing. The relationships among these factors may matter as much as the factors themselves.

Finally, I'm comparing what I'm learning with established guidance including ISO 42001, NIST, the EU AI Act, PMI, Microsoft and OWASP. The goal isn't to create another AI governance framework - I'm trying to understand what has to be true inside an organization for good AI practices to actually work and create value.

Join the discussion on LinkedIn

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Research Updates

August 6, 2026

Designed to Fail? - Launching the Research

Every leader I talk to is asking the same questions: "We bought the licenses. We ran the training. Why isn't our bottom line moving?"

Here's the question I've been wrestling with: What if most organizations are structurally built to reject the true value of AI?

Much of the enterprise AI conversation is trapped in what I think of as the "Productivity Sandbox" - we are obsessed with making individual employees 10% faster at tasks they already do.

But the biggest opportunity isn't making existing work faster; it's discovering entirely new ways to create value that are only possible because of AI.

That means building new, AI-driven business capabilities at the team and functional level. And that is where things get much harder.

What if the organizational structures that make mature companies great at execution - rigid governance, predictable ROI models, strict decision-rights - are the same structures that suffocate AI exploration?

Maybe you don’t have an AI technology problem. Maybe you have an organizational design problem.

To explore this matter, I’ve launched a new research initiative:

DESIGNED TO FAIL?
An interview-based research project exploring the organizational conditions that determine whether AI capabilities can take root and scale.

We’re looking beyond the tech, models, and prompt engineering to understand which governance models, decision-rights, cultural conditions, and organizational structures allow new AI capabilities to take root and scale - and which get in the way.

I’m looking to interview Directors and VPs who have had meaningful responsibility for introducing, scaling, governing, or creating business value with AI.

Success stories are welcome. So are failures, false starts, and scars.

If you’ve been trying to move your organization from “AI tools” to “AI capabilities,” I’d like to hear what you’ve learned.

Interviews are 30 minutes and confidential. Contact me if you’re interested - or if someone in your network has relevant experience, I’d be grateful for an introduction or repost.

Let’s figure out how to stop building organizations that reject the future.

Join the discussion on LinkedIn

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