Every Technology Leader Agrees on Shared AI Platforms and Almost Nobody Has One

Ask any technology leader whether it makes sense to build an AI capability once and reuse it across every product line, and the answer arrives instantly. Of course it does.

Ask the same person six months later how many separate AI integrations their company is running, and the number is usually higher than they expected.

Stoyan Mitov, chief executive of the software development firm Dreamix, describes a recent engagement with a company supplying compliance technology to regulated financial firms. Every product line needed the same core AI capabilities. Building each separately would have been slow, fragmented and expensive to maintain, and the teams already understood that perfectly well.

Understanding it was not enough.

The gap is not comprehension

The failure sits between agreeing and following through, and following through means somebody has to tell a team in a hurry to wait.

The dynamic is entirely rational at the level where the decision is actually made. A product team is handed a deadline and a budget for an AI feature. Building it themselves, on whatever stack they already know, is the fastest route to shipping. Waiting for a shared platform that may not exist yet, owned by another team they would have to coordinate with, is slower.

Every individual team, behaving sensibly, takes the fast path. The aggregate result is the fragmentation everybody agreed to avoid.

Someone has to say no to a team in a hurry

How rare success actually is

The numbers put the difficulty in perspective, and they are not encouraging.

McKinsey's latest research on technology operating models found that top-performing companies adopt unified platform models across all their teams at roughly four times the rate of other organisations.

Even among those top performers, fewer than one in ten has achieved full adoption.

That is worth restating plainly. Among the companies that are best at this, by a factor of four, ninety per cent still have not got there. This is not a problem that yields to knowing about it.

The shape of that statistic also rules out the usual explanations. If the obstacle were technical, the organisations with the strongest engineering would have cleared it. If it were budgetary, the best-funded would have. The companies at the top of this distribution have both, and still mostly fail, which points the cause somewhere other than capability.

The spending makes it urgent

Gartner projects worldwide spending on AI platforms and models will reach 64 billion dollars this year, up 63 per cent on the year before.

A growth rate like that is exactly the condition under which fragmentation compounds. Budgets are expanding, deadlines are short, and every team has the money to solve its own version of the problem independently.

Duplication at 63 per cent annual growth does not stay a tidy inefficiency. It becomes an estate of parallel integrations, each with its own vendor relationship, its own security posture and its own maintenance burden, assembled by people who all agreed this was a bad idea.

What would actually change it

The implication of Mitov's diagnosis is uncomfortable, because it locates the problem in governance rather than architecture.

If every team choosing the fast path is behaving rationally, then no amount of advocacy for the shared platform will work. The incentives have to change, which means the shared capability has to be genuinely faster to adopt than building fresh, or somebody senior enough has to be willing to hold a delivery date hostage to it.

The first is an engineering investment most organisations underfund. The second requires an executive prepared to accept a visible, attributable delay now in exchange for an invisible saving later.

Which is a reasonable explanation for why fewer than one in ten of the best companies has managed it.