AI and engineering challenges

The AI and engineering challenges that stall your roadmap.

These are the AI and engineering problems that surface when you actually try to ship. Some are AI-specific: pilots stuck at eighty percent, features you cannot prove are correct, token bills with no ceiling. Some are older and just as costly: vibe-coded software that breaks when you touch it, a green build you still do not trust, a senior hire that is a $300K coin-flip. We help teams ship AI, so the first thing we tell you is where not to use it, because most of what gets pushed to a model is deterministic work that plain software does cheaper and safer. Below are the twenty challenges we hear most from VPs of Engineering, CTOs, and founders. If one of them is yours, it is a solvable one. The fastest path is usually a short, honest scope: the AI Readiness Assessment gives you a ranked 90 day roadmap, a Forward Deployed AI Engineer builds it to production, and if the real question is whether to hire, here is how forward deploying compares to a senior hire.

From pilot to production

The demo dazzled. It never shipped.

The demo worked in a day, then stalled at eighty percent for two quarters. The last mile is a different project than the demo.

You can't prove it's right, so you can't ship it.

It works in the demo but behaves differently every run, and human review does not scale. The first confident wrong answer ends the program.

You're paying for AI where plain software would be cheaper.

A model got wired into deterministic workflows. Now you fight its non-determinism daily and pay for latency and liability you did not need.

AI got added because it's AI, not because it solved anything.

A chatbot got bolted on so the release notes could say now with AI. A couple of clicks, then nothing. Nobody asked what problem it solved.

When the AI is wrong, no one owns it.

Error rate drops to one percent, but at scale that is a hundred wrong decisions a day, with no owner, no audit trail, and real liability.

You tried to fix your data with AI. AI can't fix your data.

You assumed the model would clean it up. The data was half noise, identity was inconsistent, and the output came back garbage. Everyone blamed the model.

Your token bill is going through the roof.

You prototyped on the frontier model. In production the agent loops on its own errors, spend climbs, and the unit economics stop working.

Your team is pasting company secrets into ChatGPT.

Employees paste customer records and credentials into personal AI accounts because it is convenient, and nothing deterministic is keeping any of it inside your walls.

Every engineer is using a different AI tool their own way.

Copilots and agents spread one developer at a time, no standard, no auditability, no consistent quality. Security, compliance, and budget are all exposed.

The model is a commodity. Your wrapper is the product, and it's thin.

Everyone has the same models. A generic output sounds robotic and anyone can tell. The value was never the model, it is the hardened layer around it.

The codebase: quality, debt, delivery

Vibe-coded software that works until you touch it.

An AI-generated app runs in the happy path. Ask for a small change and the whole thing breaks, because there is no architecture underneath the demo.

Your juniors are shipping code they don't understand.

An agent writes the code, the diff is too big to review carefully, and no human truly owns it. The org will never hold the AI accountable.

Green CI, and you still don't trust the release.

The build is green but your gut says no. High coverage hides that the critical path, the thing a user actually depends on, is unverified.

The rewrite you're dreading is coming.

Old libraries, missing docs, patch on patch, and one day the team says it has to be rebuilt from scratch. That means re-delivering what you already paid for.

One architecture call by someone who couldn't make it.

A decision that defines everything got made by someone without the systems background to make it. Six months later you re-engineer the feature and everything on top.

The team: hiring, ownership, leverage

Hiring a senior is a $300K coin-flip you can't call for 90 days.

Now that anyone can vibe-code an answer, your interview tests the agent, not the person. A bad senior hire wreaks havoc and you cannot tell for 90 days.

Your dev shop ships what you asked for, and misses what you needed.

The agency ships exactly the ticket, no pushback, no skin in the game. You become the PM, PO, QA, and designer, and the gap still shows.

Your offshore team says yes to everything, and you find out too late.

They agree to every request and do not flag the problems, so misalignment surfaces in production instead of planning. A good engineer gets mislabeled.

You're staffing a pyramid when two seniors would ship faster.

You pay for one senior over two mid over four junior, while the leverage has moved to seniority and judgment, exactly what AI cannot supply.

The product: build the right thing

You built it well. Nobody uses it.

The team shipped it on time and clean, and none of the users touched it. Building is easy now. Deciding what to build is the part that got skipped.

The fastest first step is the AI Readiness Assessment.

Two weeks, founder led, and you walk away with a written 90 day roadmap you keep, whether or not you build it with us. It is credited in full toward a Forward Deployed engagement. Book a 30-minute call and tell us which challenge is yours.

Book a 30-minute call

Or see the AI Readiness Assessment and the Forward Deployed AI Engineer.