What's In The Way of Your AI Traction?

Have you tried coding with AI yet? It can be an exhilarating experience. It should be an even better experience at work. The tools are paid for. There are peers who are learning and exploring with you. Maybe there is even AI training and enablement in place.

But what I hear from more and more practitioners and leaders is disappointment and frustration about what AI coding inside the organization really feels like. What’s going on? What’s making it so hard to get AI traction in an organizational context? And what could you do to unleash the potential of building with AI inside your company?

“We’ve given everyone Claude Code/Cowork (or Codex, Or Gemini CLI). Now we’re waiting for the magic to emerge.”

That’s a common story I hear from leaders who are trying to figure out how to take AI from literacy and activity to strategy and impact.

The thinking is that once smart people have access to the latest GenAI capabilities, especially tools that can go beyond augmenting your thinking and do some actual work, they will start to imagine what’s possible and find high-impact use cases.

There are a couple of problems these leaders are currently seeing, though:

• Finding these use cases requires curiosity, tinkering, and courage. These attributes are not evenly distributed across the company.
• Even curious risk-takers may be afraid to experiment.
• Many people seem like they’re deer in the headlights stuck between the fear of being replaced by AI and the fear of using AI to cut the branch they’re sitting on (sorry for the metaphor mixup - AI would never go for that ;-)
• When people don’t know what to focus on, they may come up with AI use cases that don’t move the needle. Worst case, they make an improvement that actually piles more work on other people. For example, generating more code and features when the bottleneck is training your customers.
• High impact often requires coordination between people, since it spans across functions.

None of these are AI problems. They are all human nature and company culture problems.

AI is just very good at exposing how effective you really are as an organization at developing and evolving.

A lot of leaders were already feeling the pain before AI:

• People are buried in their day-to-day responsibilities and have little capacity for extra “projects.”
• Too many projects hit the same group of people, so projects are often late despite everyone working hard.
• Project management is focused on activity, and even “successful projects” often don’t deliver an impact.
• People are told exactly what to do. Sponsors ask for a specific solution, which tends to extinguish creativity and exploration. Sometimes the predefined solution is not the right approach.

In order to deliver AI impact we need to improve our ability to deliver value with projects.

Yes, by giving people AI there’s the potential that they’ll improve their personal productivity.

But most interesting organizational “alpha” will require more than individual productivity improvement.

It requires a strong capability for the organization to develop/evolve itself.

Otherwise your AI projects and initiatives will hit the same roadblocks: lots of activity, little traction, little impact.

When you look at companies that manage to improve their project traction and impact, you often see several major shifts:

• From activity to flow - from starting to focusing and finishing.
• From scope to outcomes - instead of fixing the solution, align around the intent and maintain flexibility about what it will take to achieve it.
• From detailed plans to adaptive planning - instead of fully planning exactly what everyone needs to do and when, plan a little bit, do it, sense, and adjust continuously until you achieve the goal.

These shifts come from the product and software development world, where we’ve been tackling complex projects with high degrees of uncertainty about what to build, how to build it, and whether it will even be useful.

Product and software development organizations have been shifting from project thinking to flow and product thinking. They’ve been using more focused, iterative, and adaptive ways of working. Dare I say, more agile?

If you think about your AI projects, they look a lot like this. Even if they’re not about your product and don’t live inside your product, engineering, or IT organization, they are still full of uncertainty and complexity about why, what, and how.

Which is why when you look at case studies of companies who are getting better traction with their AI investments, you can see flow and product thinking at play:

• Focusing on the investments/initiatives that really matter.
• Acknowledging investments are bets and emphasizing learning and discovery before doubling down.
• Assigning directly responsible individuals who own an outcome and have flexibility about the solution.
• Steering based on traction on leading indicators and early feedback loops.
• Creating empowered pods that can run with an idea with as little friction and dependency drag as possible.

AI vibe coding is frustrating when it is bound by the constraints of the wrong ecosystem. It becomes high-impact when it is supported by an organizational operating system designed for building with AI.