Your best data science team just spent six months building a model that predicts customer churn with 90% accuracy. It’s sitting on a server, unused. Why? Because it’s been stuck in a risk review queue for a very long period of time, waiting for a committee that doesn’t understand stochastic models to sign off. This isn’t a hypothetical — it’s the daily reality in most large companies.
In AI, the models move at internet speed. Enterprises don’t.
Every few weeks, a new model family drops, open-source toolchains mutate and entire MLOps practices get rewritten. But in most companies, anything touching production AI has to pass through risk reviews, audit trails, change-management boards and model-risk sign-off. The result is a widening velocity gap: The research community accelerates; the enterprise stalls.
This gap isn’t a headline problem like “AI will take your job.” It’s quieter and more expensive: missed productivity, shadow AI sprawl, duplicated spend and compliance drag that turns promising pilots into perpetual proofs-of-concept.The numbers say the quiet part out loudTwo trends collide. First, the pace of innovation: Industry is now the dominant force, producing the vast majority of notable AI models, according to Stanford’s 2024 AI Index Report. The core inputs for this innovation are compounding at a historic rate, with training compute needs doubling rapidly every few years. That pace all but guarantees rapid model churn and tool fragmentation.
Second, enterprise adoption is accelerating. According to IBM’s, 42% of enterprise-scale companies have actively deployed AI, with many more actively exploring it. Yet the same surveys show governance roles are only now being formalized, leaving many companies to retrofit control after deployment.
Layer on new regulation. The EU AI Act’s staged obligations are locked in — unacceptable-risk bans are already active and General Purpose AI (GPAI) transparency duties hit in mid-2025, with high-risk rules following. Brussels has made clear there’s no pause coming. If your governance isn’t rea …