An organization is a system that links people, places, productivity to a common purpose. That system uses technology to help people be in more places than one and 10x their productivity. But what happens when AI is thrown into the system? How is AI reaching 100x productivity for some people and parts of a system versus others?

Intention. Whether there is a useful outcome to using AI, or more accurately large language models or LLMs, is predicated on thoughtful system design and people-centric implementation.

Let's unpack intentional systems and user adoption.

There's a beautiful insight from systems theory that explains why some organizations thrive with AI while others struggle. Think of a Slinky walking down stairs—the behavior isn't created by external forces, but by the structure of the spring itself. The system contains its own behavior.

Your organization is the same way. If your team is resistant to change, introducing AI won't magically create innovation. If your culture rewards individual hoarding of information over collaborative problem-solving, AI tools will amplify that dysfunction, not cure it.

The feedback loops are already there, waiting.

When AI enters your system, it creates feedback loops—and these loops determine whether you get exponential growth or exponential problems.

Reinforcing loops accelerate whatever's already happening. If your team collaborates well, AI becomes a force multiplier. If they're siloed and territorial, AI becomes another tool for gatekeeping knowledge.

Balancing loops seek equilibrium. When one department starts seeing massive productivity gains from AI, others notice. They either adapt and learn, or they push back and create resistance.

Russell Ackoff reminds us that managers don't solve problems—they manage messes. Complex, interconnected, constantly changing messes.

Here's the thing about AI implementation: if you tear down inefficient processes but leave the thinking patterns that You created them intact, those patterns will simply recreate the inefficiency in new forms.

I've seen companies spend millions on AI tools only to recreate their old bureaucratic approval chains digitally. The technology changed. The system didn't.

So how do you create intentional systems that amplify AI's benefits rather than its risks?

Start with your why. Not why you want AI, but why your organization exists. What problem are you solving for the world? When everyone understands the infinite game you're playing—continuous improvement rather than just quarterly wins—AI becomes a natural extension of that mission.

Design for curiosity, not control. The organizations winning with AI aren't the ones with the most sophisticated governance frameworks. They're the ones that encourage experimentation, reward learning from failure, and trust their people to discover new possibilities.

Remember that adoption is emotional, not logical. People don't resist AI because they don't understand the benefits. They resist because change feels threatening. Create psychological safety first. Show, don't tell. Let early adopters become your internal evangelists.

We're not just implementing technology—we're evolving how humans and machines work together. This isn't a finite game with clear winners and losers. It's an infinite game where the goal is to keep playing, keep improving, keep discovering what's possible.

The systems we design today will shape how we work, think, and relate to each other for decades to come.

Will you design systems that amplify human potential, or systems that merely automate human limitations?

The choice isn't really about AI at all. It's about the kind of future we're building together, one intentional decision at a time.

What feedback loops have you noticed in your organization as AI tools become more common? I'd love to hear about your experiences—the successes and the messes.

Continue the journey.