AI Is a Systems (Not Solutions) Problem

(& Why better AI decisions come from the people closest to the work)

Most organizations approach AI adoption as a technology decision.

Is the system accurate enough?
Will it reduce costs or improve efficiency?
Can it integrate with existing tools and workflows?

Those questions are reasonable, but they are incomplete.

AI decisions are also decisions about systems: how work is organized, where authority sits, what dependencies are created, and how difficult it will be to change course later. When those dynamics aren’t examined early, they tend to surface downstream as risk, friction, or failure.

Performance is only one dimension

A tool can perform well in isolation and still create problems once embedded in a real organization.

That’s because AI never arrives alone. It moves through procurement processes, legacy systems, informal workarounds, labor structures, compliance regimes, and power dynamics that weren’t designed with it in mind. When AI is evaluated primarily as a solution, these surrounding systems remain invisible, even though they shape its real impact.

What often gets optimized for:

• speed

• accuracy

• cost savings

What often gets overlooked:

• dependency and lock-in

• repair, maintenance, and adaptation

• changes to judgment, discretion, and accountability

• how work actually changes, not how it’s assumed to

A visible example — but not a unique one

Autonomous vehicles make this tension easier to see.

They’re frequently discussed in terms of safety improvements, efficiency gains, and cost reduction. On those terms, the technology can look compelling. But those metrics don’t capture the full system being built around them: who maintains the vehicles, who controls repair and updates, how labor is displaced or reorganized, and what happens when assumptions change.

These questions aren’t unique to transportation.

The same pattern appears in healthcare systems that can’t be audited or adjusted without vendor approval. In workplace tools that automate decisions without making them contestable. In educational platforms that reshape learning while locking institutions into proprietary models.

Different domains. Same structural issue.

Why decisions fail without the people doing the work

Where AI decisions most often break down isn’t at the level of intent. It’s at the level of understanding.

The people who do the work — whether in operations, customer service, healthcare, education, or public administration — have knowledge that rarely appears in demos or executive summaries. They understand where judgment matters more than repetition. They know which “inefficiencies” are actually safeguards. They can see where a workflow is brittle, where it’s resilient, and where AI will amplify existing problems rather than solve them.

When those people are excluded from AI decisions, organizations don’t just risk resistance. They lose critical intelligence about the system itself.

This is why involving people early and meaningfully isn’t primarily about buy-in, morale, or culture (though those may be welcome side effects). It’s about producing better decisions.

What changes when this work is done together

When AI decisions are examined collaboratively — across roles, disciplines, and levels of authority — different questions emerge:

• What assumptions is this system making about how work happens?

• What breaks if those assumptions are wrong?

• What new dependencies are we accepting? Are they reversible?

• Where does value actually show up, and for whom?

These questions surface risks while there is still room to respond. They reveal opportunities that performance metrics alone would miss, and they make it possible to shape systems deliberately rather than inheriting them by default.

The real choice

AI will reshape systems whether organizations acknowledge it or not.

The question isn’t whether to adopt AI or reject it. It’s whether those changes are guided by insight from the people who understand the work from the inside or whether decisions harden into infrastructure before their consequences are fully understood.

Doing this work together isn’t about slowing progress. It’s about making decisions that hold up over time because they’re grounded in how work actually happens, not how it looks from a distance.