Everyone is focused on the latest AI innovations, from multimodal GenAI advancements to specialized applications and agentic AI. But few are paying attention to one critical gap that puts all this innovation — and its potential — at risk: outdated cloud governance models.
These models weren’t built for the pace we’re moving at today. Most organizations are still trying to govern AI infrastructure with static policies, tagging rules, and post-mortem budget alerts. That’s like trying to control a Formula 1 car with a bicycle manual — it simply isn’t a match for the speed or complexity. And the strain is starting to show.
Governance often gets treated as an insurance policy, a risk-mitigation layer, a box to check. But in today’s environment, it must be more than that. When it works, it makes the right thing the easy thing. If it slows teams down or gets bypassed altogether, it stops being governance — and becomes a liability.
Why Traditional Governance Can’t Keep Up
Legacy governance models were built for more predictable environments — where infrastructure was provisioned manually, by centralized teams, with time to review and react. That reality is gone.
AI workloads are:
-
Dynamic: Infrastructure is provisioned automatically and scales in real-time.
-
Decentralized: Workloads are launched by teams operating outside traditional IT channels.
-
Expensive: High-powered compute jobs accumulate costs fast — often to the tune of $10 to $100 million per model — without clear ownership or oversight.
In environments like this, reactive governance doesn’t just slow things down — it fails. According to Gartner, only 48% of AI projects make it into production, and the average time to get there is eight months — delays often rooted in fractured workflows, unclear ownership, or policy bottlenecks.
I’ve seen it firsthand: a data team bypasses provisioning delays by using a shadow account; an AI pipeline scales unexpectedly over a weekend; cost and compliance issues surface weeks later, when it’s too late to do anything but clean up the mess.
These aren’t isolated events. They’re symptoms of a broader disconnect between how organizations say they want to govern the cloud — and how their systems actually operate.
When Governance Breaks, Culture Follows
The deeper risk isn’t just operational. It’s cultural.
When governance is built around delays, gatekeeping, or reactive controls, it sends a clear message: compliance and velocity can’t coexist. And when teams are forced to choose, they’ll choose speed — every time.
I’ve seen this turn into shadow infrastructure, fragmented decision-making, and team-level workarounds that leave finance and security in the dark. It’s not that people don’t care about governance. But instead of governance being a built-in, preemptive step, it’s become something they just “work around.”
And when that happens, three outcomes typically follow:
-
Cloud sprawl: Teams stand up infrastructure wherever and however they want, with no unified oversight.
-
Unpredictable spend: AI workloads scale unexpectedly, and finance teams are left reacting to invoices instead of managing impact.
-
Compliance gaps: Sensitive data is processed without appropriate controls, exposing the organization to avoidable risk.
By the time any of these issues are visible, policy isn’t enough to solve them. You need structural change.
What AI-Era Governance Demands
To support AI — and future-proof operations in general — governance has to shift from a reactive process to a preventive capability. It has to be built into the infrastructure, not layered on after the fact.
That starts with four core principles:
Platform-embedded policies: Governance logic must live where infrastructure is created. Automated controls on provisioning, access, and resource types prevent problems before they start.
Paved roads, not detours: The easiest path forward should also be the most compliant. When self-service tools and templates include built-in guardrails, teams stay aligned without slowing down.
Real-time visibility with business context: Spend and usage data need to be transparent and visible as they happen — tied to actual workloads, teams, and business goals. Not just cloud accounts and billing codes.
Shift-left FinOps: Cost accountability can’t be a month-end task. When finance and engineering align during planning and development, governance becomes part of delivery — not something bolted on after launch.
This approach changes governance from something people avoid to something they rely on. Not a blocker; a foundation.
Governance as a Strategic Advantage
Done right, governance accelerates innovation. It gives teams confidence to move fast, scaling within a framework that protects the business. It connects technical decisions to business outcomes and ROI.
The old model — manual approvals, siloed oversight, static policy documents — wasn’t built for this era of innovation. It created blind spots, and AI’s rapid acceleration only magnifies them.
It’s imperative to embed AI governance into the systems, workflows, and infrastructure your teams already use. Make it automatic. Make it contextual. Make it native to how people build.
Because when governance works that way — when the right thing is also the easiest thing, the natural thing — teams don’t resist it. They depend on it. And that’s when governance becomes strategic.