Your business has entered its B-17 phase.
In 1935, Boeing unveiled what should have been the most impressive aircraft ever built. The Model 299 — later known as the B-17 Flying Fortress — could carry five times more bombs than required, fly faster and further than anything before it. It was the obvious winner.
Then it crashed on its demonstration flight, killing two crewmembers. The verdict: pilot error. The B-17 was simply too complex for any single pilot to manage from memory — four engines, retractable landing gear, wing flaps, electric trim tabs, constant-speed propellers, all demanding simultaneous attention.
What happened next is the part that matters. The Army didn't send pilots back to improve their skills, they created a system where skill wouldn't be the deciding factor. They created a checklist. It was short enough to fit on an index card, and included step-by-step checks for takeoff, flight, landing, taxiing. With the checklist, pilots flew the B-17 a total of 1.8 million miles without a single serious accident, and the plane helped win the second world war.
The plane didn't change. The pilots didn't change. They built a system.
Atul Gawande wrote about this in The Checklist Manifesto and drew a line straight to the present: "Much of our work today has entered its own B-17 phase. Substantial parts of what software designers, financial managers, and most professionals do are now too complex to carry out reliably from memory alone."
Most businesses are in exactly that place right now with AI. The problem isn't that their people lack skill. It's that they're relying on skill and memory where a system should be doing the work. Getting your team individual AI chat apps is like asking the B-17 pilots to get more training. It's the right answer to the wrong question.
That's what we build.
The evidence is now hard to ignore.
In 2025, MIT's NANDA initiative published the most comprehensive study to date on how AI actually performs inside companies — 150 executive interviews, 350 employee surveys, 300 deployments analyzed. The headline: roughly 95% of enterprise AI pilots produced no measurable impact on the bottom line. Despite $30–40 billion in spending.
The reason wasn't model quality. It was deployment. The successful 5% had one thing in common: tight integration between the AI and the specific business processes it was meant to improve.
You can see the same pattern at the highest end of the market. Uber burned through its entire 2026 AI budget in four months. Ninety-five percent of its engineers use AI tools every month. A majority of its code is now AI-generated. And its own COO recently admitted — publicly, on a podcast — that he can't draw a clear line between all that spending and meaningful improvements for customers.
That's the trap most companies are walking into. High adoption. Real cost. Activity that never becomes outcome.
The dividing line is context.
Picture a Monday morning. Your account lead needs to prep for a client review. She opens a chat tool and asks it to help pull together a status update. The AI is capable, willing, and has no idea what she's talking about.
It doesn't know the client. It doesn't know what was promised last quarter. It doesn't know which version of the proposal is current, where the budget tracker lives, or that the project is three days behind because of a vendor delay last week. She has to feed it all of that manually — copy-pasting from five different places — before it can help her with anything useful.
Now multiply that by every person on your team, every day, every task. That's not an AI problem. That's a context problem.
The companies in the 5% solved this structurally — they built a system where the context about their business lives in one place, and then brought the AI to that context. Instead of each person manually carrying context to the AI one prompt at a time, the system already knows. The AI already has what it needs.
A custom app molds to you. Off-the-shelf molds you to it.
When you buy a general-purpose tool, you adapt your business to its assumptions. When you need it to behave differently, you file a feature request with a company that has ten thousand other customers — and you wait.
When you own a custom application, that dynamic flips. A workflow that currently takes seven steps and involves three people can be rebuilt to take one. A small recurring annoyance gets fixed in an afternoon instead of becoming a permanent limitation you've learned to live with.
Custom doesn't just fit better at the start. It keeps fitting as your business changes — because it was designed to be changed.
Give your company its own AI.
What we build is a purpose-built system for how your company actually works. Connected to the data your team already generates. Running the workflows that currently live in someone's head. It doesn't vary by user. It doesn't forget. It doesn't leave when someone does.
It just runs. And it gets better the longer you use it.
Sources: MIT NANDA initiative, "The GenAI Divide: State of AI in Business 2025" (July 2025). Uber COO Andrew Macdonald, Rapid Response podcast, May 2026, reported by Fortune.