Platform Engineering for Enterprise AI: Principles That Deliver at Scale

AI dominates headlines and strategy decks, but most enterprises tell the same story: pilots everywhere, outcomes nowhere. Tools are scattered throughout the organisation, costs creep up, and audit trails are thin. The missing piece isn’t the next model; it’s the absence of platform discipline.

At Syntasso, we’ve seen this pattern across industries. Just as cloud and containers only scale when wrapped in strong platform practices, AI requires the same foundation. Without a platform approach, adoption stalls, governance gaps widen, and “shadow AI” flourishes. With it, AI access becomes a stable, governed service your organisation can consume, evolve, and trust.

Independent research reinforces this. MIT’s 2025 State of AI in Business report found that despite tens of billions in spend, only 5% of custom enterprise AI tools make it to production. Enterprises lead in pilot volume yet lag in scale. The findings point to approach, not model quality, as the real limiter.

This article explores three platform-engineering principles that consistently turn AI from hype into working enterprise systems.

1. Publish Once, Swap Under the Hood

Don’t wire apps directly to today’s hottest model. Instead, deliver AI through a stable platform contract: a catalog entry and API that stay the same while the platform team can swap providers, optimize cost, or introduce custom models behind the scenes.

This matters in a domain that changes on a weekly basis. New models arrive, toolchains evolve, evaluation practices improve, and regulations tighten. A stable API provides product teams with consistency, while the platform absorbs the churn.

Platform building tools such as Kratix enable platform teams to package AI access as a self-service product, ensuring integrations remain intact even as the underlying providers or policies evolve.

2. Run AI Like a Fleet, Not a POC

AI services multiply fast. Without fleet discipline, you get version sprawl, unmanaged keys, and outages that ripple across teams. Tools like Kratix enable you to treat models like you treat clusters and runtimes: version them, roll them out gradually, retire unsafe options, and measure SLOs. 

When AI access is run as a fleet, change becomes routine instead of a fire drill. Teams know which models they’re using, which budgets they’re consuming, and how to roll back safely if performance drops.

3. Put Policy Where Access Happens

Compliance rules shouldn’t live in app code. By enforcing budgets, regions, and workload restrictions at the platform’s access layer, you close the “shadow AI” gap and turn governance into configuration.

The MIT report shows why this matters: in over 90% of surveyed companies, employees use personal LLM accounts, while only 40% have official subscriptions. Developers are already adopting AI; the question is whether your platform provides a safer, governed substitute.

Having a robust platform lets teams consume AI responsibly, with governance and observability built in from the point of request.

From Pilots to Production

The takeaway is straightforward: AI works in the enterprise when it’s delivered like the rest of your platform. One place to request it. One interface to use it. Clear budgets and policies. Fleet practices that make change safe. Business logic that reflects your strategy and regulation.

The MIT numbers explain the cost of skipping these steps: pilots that stall, shadow usage that grows, and long cycle times that drain momentum. A platform approach flips that script. It converts hype into systems your organisation can trust and scale.

And that’s where Kratix comes in, helping platform teams turn AI access into a repeatable, governed service that meets developers where they are and evolves with your business.