Stoyan Mitov is the CEO of Dreamix, a custom software development company helping tech leaders increase capacity without giving up quality.
Ask any technology leader whether it makes sense to build an AI capability once and reuse it across every product line, and they will agree with you immediately. But if you ask the same leader six months later how many separate AI integrations their company is running, the number is usually higher than they expect.
We’ve recently worked with a company providing compliance technology to regulated financial firms, where each product line needed the same core AI capabilities. Building each one separately would have been slow, fragmented and expensive to maintain, and their teams already understood that. Understanding it was still not enough on its own. Acting on it required someone to own the decision before the next product line started building anything independently.
Usually, the gap sits between agreeing and following through, and following through means someone has to say no to a team in a hurry.
Consensus doesn’t build anything.
Usually, a product team gets a deadline and a budget for an AI feature. Building it themselves, with whatever stack they already know, is the fastest path to shipping. Waiting for a shared platform that may or may not exist yet, owned by a team they’d have to coordinate with, is the slower path. Every individual team, acting rationally, chooses the fast path. Then the company ends up with duplicated infrastructure.
Full adoption of a shared platform model is rare even among companies built to pull it off. 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 organizations, and even among those top performers, fewer than 1 in 10 have gotten there fully.
Gartner projects worldwide spending on AI platforms and models will reach $64 billion this year, up 63% from the year before.
So, companies can afford to build this right. Most of them simply haven’t.
What changed the outcome?
In my experience, a shared AI platform only survives contact with real product deadlines when one person owns it, with the authority to say no to a duplicate build and the job of making the shared version good enough that saying no doesn’t cost anyone extra time. That’s the principle we applied with the compliance technology client. We put ownership in place before the next product line started building on its own.
We built the foundation in a specific order. First, we established a single access point connecting the client’s products to more than 100 AI models across four providers, so no product team needed its own vendor contract or integration. Then came document processing that could handle the client’s messiest legacy files, because a shared platform that can’t handle real data gets abandoned fast. Once that worked, we added a searchable knowledge layer, and later, AI agents that could act inside live systems.
Each product team kept its own road map and user experience. The AI plumbing underneath stopped being something each team decided on its own. As new product lines have come online since, none of them have needed to build a separate AI stack. A working shared version already exists, and someone is responsible for keeping it that way.
Where can you start?
A few principles made the difference here. Check them against your own situation before your next product team starts building its own AI stack:
• Name an owner before the next request comes in. Designate one person with the authority to say no to a duplicate build.
• Make the shared version fast to use. A technically sound platform that’s slow to access won’t stop teams from building their own.
• Start with the boring layer first. Model access and data processing come before anything visible. Skip this and the shared platform breaks the first time it meets real data.
• Let product teams keep their own road map. Centralize the infrastructure, and leave product decisions to the teams. Teams that lose both tend to route around the platform.
• Revisit the decision once, not constantly. Once ownership and access are in place, most teams stop rebuilding on their own. If they don’t, the problem is usually speed of access, not willingness.
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