Insights Topic
You can buy licenses and mandate tools. You cannot buy business impact. That comes from where you aim AI, how you validate internal and customer bets, and what you are willing to stop.
This is the business and leadership side of my AI work: how to move beyond personal chat productivity and scattered proofs of concept to real workflow and business impact across sales, marketing, legal, shared services, and operations. If your question is how software engineering and product teams build with coding agents, see the companion topic below.
Start here · Cornerstone read
Why I shifted from Scaling with Agility to Scaling AI from Activity to Impact, and why the disciplines of flow, empiricism, and outcome focus are what turn AI ambition into business results.
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Where to play · Customer factory
Most of the company still uses AI as a better search box. How to map your customer factory across acquisition, activation, retention, and operations to aim AI at the real bottleneck.
Enterprise case study · Beyond personal productivity
Making lawyers, scientists, and marketers 20% faster on individual tasks is the easy part. Zoetis CTO Kumar Venugopal on redesigning departmental and cross-functional workflows around AI.
Insights Database
Browse the complete archive of articles and case studies related to ai activity to impact.
Why I pivoted from Scaling with Agility to Scaling AI from Activity to Impact. Agility is the "Powered by Intel" inside the AI-Native organization, and the lessons from scaling it are what turn AI ambition into impact.
Every enterprise is arguing about the right per-employee AI spend cap. Tomer Elias and I compared notes on why that number is unanswerable until you can attribute the spend to something.
A mandated AI tool gets token adoption: people use it, do a demo, and go through the motions. Treat the change as a product, find the people with an unavoidable project, and check activation and churn before you roll it out to everybody.
Most AI efforts start with tools and demos. Better to use product discovery to aim AI at a real business constraint and test the riskiest assumption first.
Most AI efforts are still stuck in personal productivity. Zoetis CTO Kumar Venugopal on what it takes for AI to change the process itself.
"Human in the loop" is too small. The real shift is humans moving up a level: shaping the work, the workflow, the agents, the guardrails, the next decision.
AI vibe coding gets frustrating inside the wrong operating system. It gets traction when the organization can turn experiments into business results.
95% of AI projects fail: not because the models are wrong, but because organizations run high-uncertainty AI work in project mode.
AI initiatives die in the proof-of-concept stage when run as fixed-scope projects. Clemens Adolphs and I unpack why AI work needs real product discovery.
Adopting AI, Agile, or OKRs out of FOMO usually ends in JOMO. Five questions that tie any initiative to your most expensive problems first.
Every tech wave has a cart-before-the-horse phase. What product-led AI looks like instead: start with customer outcomes and explore problems before solutions.
Turning AI capability into impact takes work at three levels: the product, the software factory, and the company around them.
AI will flatten parts of the organization, but it won't remove accountability. The real shift is that more of the company's operating logic becomes encoded, observable, and continuously adjustable, and someone still has to own it.
AI work pays off when it starts from a real business constraint, not a tool wishlist. Run a small test and check the problem is worth solving.
Plenty of pilots, training, tools, and output but not enough business traction? The problem may not be AI. AI may be improving the wrong part of the system.
Most AI portfolios suffer from the same disease: too many pilots, too many ideas, not enough kill criteria. Portfolio flow beats AI idea theater.
Scattering AI initiatives without finding your bottleneck wastes effort. A Theory of Constraints lens helps you aim GenAI at the right problem first.
AI context (system prompts, knowledge bases, tool configs) is a product you keep discovering and improving. Product thinking applied to AI enablement.
AI can help you spot weak OKRs: vanity metrics, output-shaped objectives, key results nobody can actually measure. A practical guide to fixing them.
GenAI is a classic VUCA challenge: high uncertainty, fast-moving, multi-disciplinary. How to steer AI investments without drowning in the hype.