AI Will Amplify Dysfunction, Not Solve It
AI is fast at a lot of things, but in most organizations, the system around it isn’t.
That’s the real risk I see. Not that AI will fail, but that it will succeed just enough, in the wrong places, for the wrong reasons, and with little ability to scale or sustain.
When teams don’t understand where the current friction or tension in the system really lies, AI will become an accelerant of those dysfunctions; it won't solve them.
The Hidden Cost of Ignoring Flow Friction
In theory, AI should help teams move faster, but in practice, organizations are experiencing the opposite. They add AI to one step of the process, say, content generation or forecasting, and suddenly:
Multiple teams are using different tools to solve the same problem
No one’s sure who owns the AI-powered version of the capability
The insight or output doesn’t flow to where it’s needed next
It highlights that friction was already present in the system. AI just revealed it faster than before.
Most Friction Lives Between Teams, Not Within Them
We often think of delivery problems as local to a team. But most of the friction in an organization lives in the handovers, dependencies, and misalignments between teams.
Examples:
A product team builds an AI-driven feature, but it depends on data owned by another team, who have different priorities and a different timeline.
A platform team launches an API that provides generative AI capabilities, but adoption is poor because downstream teams weren’t consulted, trained or needed it in the first place.
Two teams unknowingly solve the same user problem using different models, leading to rework, duplication, and inconsistency in the customer experience.
None of these are technical problems. They’re flow-related problems.
Three Questions to Surface the Issues
Before deploying AI, ask:
1. Where are decisions slowed by coordination overhead?
AI can speed up tasks, but if every decision needs a committee or cross-team negotiation, that gain disappears.
2. Where does work get stuck or handed off awkwardly?
If your process already has hidden delays or rework loops, AI won’t fix that, it’ll just make them faster and harder to see.
3. Where are teams reinventing the same capability?
This is a red flag. Without shared understanding and ownership, AI becomes a patchwork of unconnected, redundant tools.
What Winning Organizations Will Do Differently
Forward-looking orgs aren’t rushing to bolt AI onto every process.
They’re taking time to sense where their flow breaks down, so they can make smart, targeted decisions about where AI can actually help.
What that looks like:
Holding short, cross-team exploratory sessions to explore friction and reveal mismatched expectations
Using simple maps or diagrams to trace how value actually moves through the system
Identifying repeat friction points and asking, “Is this a good candidate for augmentation, or a sign we need to rethink how we’re organized?”
They’re not just adding AI.
They’re designing for it.
In Conclusion, AI Won’t Solve Flow Issues; It Will Expose Them.
You don’t need to fix everything before experimenting with AI. But you do need to know where your structure is likely to resist or distort those efforts.
Otherwise, you risk speeding up delivery of the wrong thing, building brittle solutions that don’t scale, or missing out on the real potential altogether.
Don’t just move faster. Move smarter.
Start by understanding your flow of value.
