Post-Series A scale-ups: delivering on the AI roadmap pitched to the board

A scale-up that has just closed a Series A or Series B lives twelve months of simultaneous pressure. Hiring pressure, because half the funds raised goes to growing from fifteen to forty people in a year. Roadmap pressure, because the pitch deck sold features to investors that are now baked into six- and twelve-month plans. And, since 2024, AI-specific pressure — almost every post-2023 investment thesis mentions an AI dimension, central or complementary.

The operational problem is well known: hiring a Head of AI and their team takes four-to-six months if everything goes well; by the time that team ships something production-ready, you're nine-to-twelve months past the round. The board, meanwhile, expects signs of progress at the first quarterly review.

Three wrong answers

Faced with that pressure, leadership teams often go for one of three solutions, all of which have limits.

First: have the existing product team bricoler the feature alongside their main missions. It's the apparently cheapest option. It produces a visible result fast, and durably degrades morale and the quality of the core product. Senior engineers leave six months in, tired of stacking two jobs.

Second: ship a marketing POC — a demo running in a controlled environment, shown to the board without going to production. This buys time but is risky if a client or journalist asks to try it. And it postpones the real work without reducing it.

Third: take a large consultancy to ship the feature at high speed, on a several-hundred-thousand-euro budget. The risk here isn't cost, it's that the delivered feature ends up disconnected from the internal architecture — a parallel system the internal team, once hired, will have to rewrite or maintain reluctantly.

The middle option that works

The approach that produces the best results in the scale-ups we work with is to have v1 delivered by an external partner who commits to ship inside the target architecture — the one the internal team will inherit — with a complete handover at the end. Not an isolated subsystem, not a separate demo, not a temporary wrapper to throw away. The code shipped is the code the internal team will take over.

This option assumes two technical conditions. First, that the external partner works in the client's stack, or ships in a standard stack future hires will master. Not in a proprietary platform that will have to be worked around later. Second, that the domain modelling — the scale-up's business concepts — is documented and aligned with the rest of the system. Otherwise the AI feature, even shipped, doesn't integrate with the main product.

Synchronising with hiring

The ideal time to start an external partnership is early after the round — week four or six — in parallel with the start of the Head of AI search. v1 ships around week fourteen, which roughly matches the time the Head of AI takes the seat. The handover happens over two-to-four weeks, during which the external partner keeps supporting before transferring ownership.

This sequencing has another benefit: a Head of AI candidate shown a feature already in production — with clean code and documentation — is easier to recruit than one asked to build everything from zero. The shipped v1 becomes a hiring argument.