Insights Topic
AI creates speed, not automatic value. Your operating model decides whether that speed becomes business impact.
Most AI initiatives are swimming in activity but starving for business results. Below, I’ve mapped out my core perspectives on how to resolve the operational bottlenecks that prevent AI from driving top-line or bottom-line value.
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Most AI projects fail not because the models are wrong — but because the management operating system isn't built for high-uncertainty work. How agility principles change the odds.
Read the full articleWe are accelerating the wrong parts of the system. In engineering, we use AI to dramatically speed up the inner loop—writing code or drafting documents. The problem is that the downstream outer loop, where reviews, security validations, and integration happen, remains slow and unchanged.
When you push a faster inner loop into a constrained outer loop, you do not get more business throughput. Instead, you get "acceleration whiplash." Work piles up in queues, creating massive amounts of unreleased inventory. Realizing impact requires shifting focus from local developer productivity to end-to-end system flow.
AI Theater is what happens when organizations measure success through activity metrics. You will see dashboards boasting about the number of licenses purchased, workshops completed, or hackathons held. These are input metrics, and they create the illusion of progress without solving any actual customer or business friction.
Escaping this theater means shifting to outcome-oriented steering. We need to stop mandating tools top-down and start focusing on pull-based adoption. Teams should adopt AI capabilities because it demonstrably reduces their lead times or solves specific operational constraints.
Treating AI as a simple IT rollout is a fundamental mistake. Realizing value requires treating internal AI capabilities—and the organizational context surrounding them—as products. This means applying Lean Startup techniques, building in small incremental steps, and managing flow economics.
Start by identifying the actual constraint in your business, whether that is customer onboarding or sales enablement. Fund experiments in stages, releasing investment only as teams demonstrate sponsor-ready evidence of real value.
AI Theater is when an organization measures AI success by activity—such as seats allocated, prompts typed, or demos built—rather than actual business outcomes. It creates the appearance of progress without driving revenue, cost savings, or cycle-time improvements.
Measure AI value by tracking system-level outcomes: end-to-end cycle time, release frequency, change failure rates, and customer value indicators. Avoid local productivity metrics like lines of code generated, which ignore downstream review and testing bottlenecks.
Because value is constrained by the slowest part of the end-to-end stream. If engineering speeds up but product discovery, code reviews, deployment, or customer adoption remain slow, you simply pile up work-in-progress (WIP) and create longer queues, not faster value delivery.
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AI agents generate code faster than ever, but the bottleneck has shifted to human decision latency and review capacity. Here is the operating system that closes the gap between AI activity and business impact.
AI work creates value when it starts with a real business constraint, not a tool wishlist. Before building the agent, run a small test, look for evidence, and make sure the problem is worth solving.
Most AI efforts start with tools and demos. A better move is to use product discovery and Lean Startup habits to aim AI at a real business constraint, test the riskiest assumption, and learn before you burn.
AI accelerates team delivery, but it also collapses procedural complexity. As team-level dependencies thin out, the mechanics of scaling frameworks become optional while the first principles of flow and managing WIP become more important.
When AI coding increases the arrival rate of pull requests, asking reviewers to work faster is the wrong response. Use end-to-end feature flow, spec-driven development, and the Theory of Constraints to improve reviewability and business throughput.
AI isn't killing Scrum, but it is shrinking the team size from cross-functional squads to highly leveraged 1-3 person units. Here is how team-level descaling triggers a fractal collapse of organizational complexity.
Why the rise of Forward Deployed Engineering is the next agility problem — and how to scale "unreasonable agility" without it eating itself.
Spec-driven development looks like a step backward if you think of it as requirements theater. But the better frame is that the spec is becoming a higher-level programming language for human intent.
Claude's new /goal feature lets AI agents keep working until a completion condition is met. The examples are mostly output-oriented: tests pass, code compiles, backlog empties. What's missing is outcome. Here's why that gap matters.
AI coding tools can make engineering dramatically faster. That does not automatically make the business faster. The constraint often moves to deciding what is worth building, reviewing safely, getting adoption, and proving impact.
Most AI efforts are still stuck in personal productivity. The interesting shift starts when AI moves from something individuals use around the edges of the process to something that changes the process itself. This is what that shift actually looks like inside a Fortune 500 from the CTO's chair.
If your AI effort has plenty of pilots, training, tools, and output but still not enough business traction, the problem may not be AI. It may be that AI is improving the wrong part of the system.
Spec-driven development can be a smarter way to steer AI coding agents, or it can become waterfall with more tokens. The difference is whether the spec creates learning or pretends learning is no longer needed.
Moving from chatting with AI to unleashing agentic AI is not just a productivity boost. It changes leadership work. The point is not human-in-the-loop, but humans moving up a level.
AI vibe coding is frustrating when it is trapped inside the wrong operating system. It becomes high-impact when the organization is built to turn AI experiments into business traction.
A conversation with Dave West at Scrum.org about the 95% AI failure rate and why organizations need a different management operating system for AI work.
95% of AI projects fail — not because the models are wrong, but because organizations run high-uncertainty AI work through a project-mode operating system. Here is what changes when you treat AI as product work instead.
AI initiatives die in the proof-of-concept stage when they are run as fixed-scope projects. Clemens Adolphs and I unpack why AI work needs real product discovery, internal market validation, and outcome-based steering instead of a gantt chart with the labels filed off.
Why scattering AI initiatives without identifying bottlenecks wastes effort — and how a Theory of Constraints lens helps you aim GenAI at the right problems before you automate.
Adopting AI (or Agile, or OKRs) out of FOMO almost always turns into JOMO — the Joy of Missing Out. A five-question framework for connecting any initiative to your most expensive problems before you commit.
AI context — system prompts, knowledge bases, and tool configurations — is a product to be continuously discovered and improved. Applying product thinking to AI enablement.
AI tools promise to accelerate product discovery — but do they? A critical look at where AI actually helps in discovery, and where the bottleneck remains human judgment.
Practical patterns for using AI across the product development flywheel — from discovery and validation to faster build cycles and better feedback loops.
Every major tech wave has a cart-before-the-horse phase. What product-led AI looks like instead: starting with customer outcomes, embedding cross-functional ownership, and exploring problems before solutions.
GenAI transformation is a classic VUCA challenge — high uncertainty, fast-moving, multi-disciplinary. How organizations can use agile operating principles to navigate AI investments without drowning in hype.