AI Adoption in Software Teams: A Field Report From 2026
I spent months visiting tech teams at outsourcing firms and unicorns. AI adoption is real, but far slower than the headlines. Eight honest field notes.
In The Big Short, Mark Baum doesn't trust the spreadsheets. He flies to Florida, walks through newly built neighborhoods, and sees it with his own eyes: entire streets with no owners. No analyst report could have told him the US mortgage market was a bubble as clearly as an empty cul-de-sac. AI adoption in software teams deserves the same test: stop reading the reports, go and look.
So over the past few months I did something similar. I attended tech team meetups at outsourcing companies and at unicorns, and I asked the unglamorous questions. Not "what did you demo?" but "what actually shipped, who maintains it, and what happened when it broke?"
I did it because YouTube and LinkedIn create a persistent feeling that everyone is making insane progress with AI and the rest of us are falling behind.
Spoiler: we are not. Here is my report from Florida.
What AI adoption in software teams actually looks like
Eight observations, repeated across companies of very different sizes and maturity levels. Not one of them matches the headlines.
SDLC 2.0 is still a slide deck
Everyone can describe the AI-native development lifecycle. Teams are as far from implementing it as they were a year ago. The slides evolved; the pipelines did not.
Security hasn't gone anywhere
Secure, cheap, high quality: pick two. In practice, most companies struggle to get even two. Serious AI initiatives still stall on data access, compliance, and review.
AI is still a house pet
Companies aren't ready to invest properly, so everything runs on enthusiasts. One engineer feeds it after hours. If that person leaves, the initiative leaves with them.
The obituaries are premature
The internet is flooded with "R.I.P. developers / QA / PMs" headlines. In practice these solutions break on the first edge case, and a human quietly steps back into the loop.
Tokens are the new vanity metric
Engineers brag about burning billions of tokens. To me that's a truck driver bragging about fuel consumption. There is no guarantee those costs aren't just a wrong route.
The best ideas stay on the shelf
Most genuinely innovative development ideas live at the level of philosophy and proof of concepts. Demoed once, praised, never wired into the actual delivery process.
"1 person = 1 role" has a hidden cost
The economics look great, but we've lost the debate that decisions need. AI agrees with your bad idea faster than a junior engineer ever would.
Roles haven't been rethought
Developers, QA, PMs, HR, recruiters do the same things they did 5 years ago, just with an AI chat window open. Not redesigned workflows. Not even AI agents.
Truth is born in argument, and today's AI is still a sycophant, not an opponent.
The unicorn that built its own AI code review
Now the most telling scene from my Florida trip.
One unicorn invested serious money into building its own AI code review system. A real engineering effort, done well, by people who knew what they were doing. Then Anthropic and OpenAI shipped their own out-of-the-box code review solutions.
Yes, the custom system is about 20% cheaper to run. But will six months of development ever pay itself back against a platform feature that improves every quarter without a single internal engineer touching it? Almost certainly not. The team built a good product. The market simply moved underneath it.
The trap: you are buying a 3 to 6 month head start
This is the pattern behind almost everything I saw. Companies investing in custom AI tooling today are not buying a moat. They are buying a 3 to 6 month advantage. No more.
After that window closes, here is where each kind of investment ends up:
| What you build | Where it is in 6 months |
|---|---|
| A thin wrapper around a model | Became a platform feature. You maintain an expensive version of a subscription. |
| A custom tool with no owner | Joined the proof of concepts on the shelf, next to observation 06. |
| AI wired into your process and data | Still compounding. This is the layer no platform can ship for you. |
That doesn't mean "build nothing". It means the durable wins are unglamorous: wiring AI into your actual delivery process, cleaning up the data it depends on, and rethinking who does what.
House pet or part of the process?
The revolution has happened. That part is true. Models can genuinely review code, draft tests, triage tickets, and hold a plan across a large codebase. But adoption is an organizational problem, and organizations move at the speed of trust, budgets, and security reviews, not at the speed of model releases.
So here is the honest question to ask about your own team, the same one I asked at every meetup. Which column describes you?
Still a house pet
- ✕ Fed by one enthusiast after hours
- ✕ No budget, no owner, no roadmap
- ✕ Dies when that one person leaves
- ✕ Success measured in tokens burned
Part of the process
- ✓ Has an owner, a budget, and an SLA
- ✓ Wired into the delivery pipeline
- ✓ Survives any single person leaving
- ✓ Success measured in outcomes shipped
If your answer is "house pet" and you want to change that, start small and structural: pick one workflow, give it a real owner, measure the outcome instead of the tokens. And if you'd rather have a team that has already made these mistakes on someone else's budget, that is what we do.
Want AI in the process, not in a cage?
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