The Code Compiles. The Org Chart Doesn’t.

At Avalia, we spend a lot of time inside other people’s codebases. We’re called in to assess software during M&A deals, tech transformations, or AI strategy shifts. And more often than not, the systems technically work. The pipelines run. The tests pass. The dashboards light up green. But something still feels off.

It’s the people side that breaks first.

We recently read Harvard’s 2025 Global Leadership Development Study, and it put numbers behind something we run into constantly: AI adoption is outpacing team readiness.

The study says 55% of leaders are prioritizing GenAI or ML this year. But it also shows a widening gap in “speed to skill.” That tracks. We’ve reviewed systems where AI tools are deployed into environments that haven’t had a serious skills audit in years. You get code acceleration without architectural understanding. Features ship faster, but tech debt quietly spreads.

The old model, learn a skill, apply it for a decade, is gone. Today’s engineers are asked to pick up new frameworks, learn prompt engineering, or manage LLM-based systems on the fly. What we’ve seen is that the best teams don’t treat their org structure as fixed. They refactor it like code, roles get redefined, and juniors run AI experiments. Architects spend time explaining edge cases to product teams. There’s no “handoff”—there’s just continuous integration between people.

The Harvard report also talks about "collective intelligence"—not just AI helping humans, but a two-way learning loop. We’ve seen teams start to embed GPT-style agents into documentation flows or architecture reviews, and it changes how knowledge is stored and shared. But it also surfaces a leadership gap. Engineers can build the system. Few are ready to own the ethical, strategic, or communication implications. One of the most consistent issues we flag during due diligence isn’t missing features, it’s missing governance.

The study recommends “full-immersion learning.” We like that framing. In practice, it looks a lot like staging environments.

You learn by breaking things in a controlled space and figuring out what fails loudest. The orgs that get this right run internal experiments with AI—not because it’s trendy, but because they want to see where the process snaps under pressure.

In the end, AI changes how decisions get made, who owns what, and how fast the team has to adapt. When we evaluate a company’s software, we’re looking at both the repo and the reflexes. Code can scale. But scaling trust, decision-making, and judgment? That’s harder—and that’s where things break if you’re not ready.