What's In The Way of Your AI Traction?

Building With AI - Dream vs Reality

Have you tried coding with AI yet? It can be an exhilarating experience. It SHOULD be an even greater experience when you’re at work. The tools are paid for. There are peers who are learning and exploring with you. Maybe there’s even AI training and enablement in place. But what I hear from more and more practitioners and their leaders is disappointment and frustration at 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?

Give Them AI And Watch Them Build

“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 transformational capabilities of GenAI, especially the latest versions of it that can go beyond augmenting your thinking to doing some actual work on its own, they will start to imagine what’s possible and find high impact use cases.

The Reality of Give Them AI

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

Finding these use cases requires curiosity, tinkering, courage. These attributes aren’t evenly distributed across the company.
Even people who are curious risk-takers seem to 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 might come up with AI use cases that don’t move the needle. Worst case scenario 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/evolving.

The Reality of Project Work Before AI

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 of these projects who all 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 has this tendency to extinguish creativity and exploration, with the side effect that sometimes the predefined solution isn’t the right approach.

What Happens When You Throw AI Into The Mix

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/initiatives will hit the same roadblocks. Lots of activity. Little Traction. Little Impact.

A Better Approach To Projects

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, aligning around the intent and maintaining flexibility about what it will take to achieve it.
From Following a detailed plan to adaptive planning - Instead of fully planning out exactly what everyone needs to do when, planning a little bit, doing it, sensing and adjusting. continuously. until achieving the goal.

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

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

Treat Your AI Projects as Products

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/IT organization - they are still rife with 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/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 dependencies as possible.

AI Vibe Coding is useless and frustrating when its bound by the constraints of the wrong ecosystem. It is exhilirating and high-impact when unleashed by an organizational operating system that’s optimized for “building with AI”