Alisson Enz
Founder & CEO
Two kinds of companies are adopting AI development right now, and they look nothing alike.
The small company ships first and asks questions later. If something breaks, a few users notice and they fix it by lunch. The blast radius is tiny, so speed is almost free.
The enterprise can't do that. A bad change touches millions of users, or a regulator, or a payment system that's been running since before the developer who's editing it was hired. So they move slower. Not because they fear AI. Because the cost of being wrong is enormous, and AI doesn't lower that cost. It raises the volume of changes that could be wrong.
Enterprise leaders are under real pressure to adopt AI. The board asks about it. Competitors announce it. Engineers want it, and the good ones will leave for companies that let them work with modern tools. Banning AI is not an option anymore. It's a recruiting problem and a productivity problem.
But the same leaders are responsible for systems that absolutely cannot go down. The trade-off that makes AI development fast at a startup is the exact trade-off an enterprise can't make. "Ship it and see" works when nobody gets hurt. It doesn't work when a wrong line of code freezes payroll for 40,000 people or violates a compliance rule that carries a seven-figure fine.
So the enterprise wants both things at once: developers who move fast with AI, and a system that never breaks. Those goals pull in opposite directions, and pretending they don't is how you end up with either a frozen team or a disaster.
It helps to be specific about where the friction comes from, because it isn't bureaucracy for its own sake.
Blast radius. At a startup, a bug affects a handful of users. At an enterprise, the same bug can hit a whole country's worth of customers in minutes. The review process exists because the downside is that large.
Legacy systems. Most enterprise code wasn't written this year. It's a layered system, decades deep, where the connections between parts aren't always documented and the person who understood them retired. AI is good at generating new code. It's far less reliable at predicting what an old, undocumented system will do when you touch it.
Regulation and audit. In finance, healthcare, and government, you have to prove who changed what, why, and that it was reviewed. An AI that produces code nobody fully understands is a direct problem for that requirement. "The AI wrote it" is not an answer an auditor accepts.
Security surface. More code shipped faster means more ways in for an attacker. AI-generated code can include subtle vulnerabilities that pass review if nobody is looking closely. At enterprise scale, one of those is a breach.
None of this means slow down for the sake of it. It means the cost of a mistake is high enough that speed without judgment is a liability, not an asset.
Enterprises tend to fail in one of two directions, and both are expensive.
The first is the ban. Leadership gets nervous, blocks AI tools entirely, and tells everyone to keep coding the old way. This feels safe and isn't. Your best engineers use these tools at home and resent being slowed down at work. Productivity falls behind competitors who adopted. And people just use the tools anyway, on personal accounts, with company code, which is the worst version of every security concern you were trying to avoid.
The second is the free-for-all. Leadership gets excited, hands everyone AI tools, and sets a velocity target. Now you have junior developers shipping large volumes of code they don't fully understand, into systems where mistakes are costly, with review processes that were never built for that throughput. The bill arrives later, in incidents and cleanup, and it's bigger than the productivity you bought.
The companies that get this right do neither. They adopt AI on purpose, with guardrails, and they hire and train for the judgment that makes guardrails work.
Careful is not the same as slow. The enterprises doing this well are fast where it's safe and deliberate where it isn't. A few things they have in common.
AI is allowed, with a paper trail. Developers use AI tools, and the workflow records where it was used so reviewers and auditors know. The goal isn't to police it. It's to keep the same accountability the company already requires for human-written code.
Review scales with risk. A change to an internal tool gets light review. A change to the payment system gets a senior engineer reading every line, AI-assisted or not. The amount of scrutiny matches the cost of being wrong, instead of treating everything the same.
AI accelerates the safe work, not the dangerous work. Boilerplate, tests, documentation, internal tooling: let AI move fast there. Core systems, security-sensitive code, anything touching regulated data: slow down and apply human judgment. The skill is knowing which is which.
Security review assumes AI-generated code. Static analysis, dependency scanning, and human review are tuned for the reality that a lot of incoming code came from a model. Engineers treat AI output as a draft to be checked, never as a finished answer.
The thread through all of it is judgment. Tools don't make these decisions. People do. Which is where hiring comes in.
The enterprise need is specific, and it's different from what a startup wants. A startup can hire a fast developer and absorb some mess. An enterprise needs developers who are fast and know exactly when to stop being fast.
That's a harder profile to find, and most hiring processes don't test for it. A timed coding challenge measures speed. It tells you nothing about whether someone understands blast radius, reads AI output critically, or knows when a change is too risky to make without a second pair of eyes.
What you want to evaluate instead:
The developer an enterprise needs in this environment is not the one who ships the most code. It's the one who ships the right amount, understands what they're touching, uses AI to go faster on the safe work, and has the judgment to slow down on the dangerous work without being told.
That combination is rare, and it's exactly what gets lost when companies hire on speed alone or screen for skills that AI already handles. The technical bar still matters. But for an enterprise, judgment under risk is the trait that decides whether AI adoption makes you faster or makes you the next cautionary headline.
This is the part we spend the most time on when we vet engineers for clients in regulated and high-stakes environments. Technical depth is the entry ticket. Knowing when to move fast and when to stop is what actually keeps your systems running while everyone else is learning the hard way.
Alisson Enz
Founder & CEO
Founder and CEO of EnzRossi. After years working with tech, I started EnzRossi. Here I write about hiring, remote teams, and what actually makes a developer great.
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