How to Scale AI From a Single Use Case to the Entire Enterprise
Learn how to scale AI from a successful pilot to an enterprise-wide program with repeatable use cases, governance, and measurable business impact.
In this segment of Joseph Ours’ Forbes Technology Council column, he explains why enterprise AI governance needs a critical third layer—platform authorization—to help organizations manage the legal and operational risks of deploying AI agents across third-party systems.
Gartner researchers project that 40% of enterprise applications will embed task-specific AI agents by the end of this year, but many organizations racing toward that number have governance frameworks that weren’t designed for it.
Those frameworks were built to answer two questions: Does the technology work, and has it been approved internally? Although both matter, a March 2026 federal court ruling established that a third question now carries legal weight: Does the platform your agent is operating on permit it to be there?
For most enterprises, the question hasn’t been asked and, worse, it can’t be answered.
Why Existing Frameworks Fall Short
Most organizations have invested in model risk management, human-in-the-loop checkpoints, role-based access controls and deployment review boards. However, according to McKinsey’s 2026 AI Trust Maturity Survey of approximately 500 organizations, agentic AI controls lag behind every other governance dimension, with just 30% reaching meaningful maturity in that area.
Those frameworks were designed for AI systems operating within organizational boundaries, on internal data, against internal systems and under internal supervision. By design, agentic AI operates across those boundaries. Agents interact with supplier portals, partner platforms, SaaS tools and third-party data sources as part of their standard operations.
I’ve found that there are two governance layers that cover internal operations. The first, technical capability, tells you what the agent can do. The second, organizational governance, tells you what it’s been approved to do. Neither address whether the external platform the agent is working against has authorized that activity.
That third layer, platform authorization, is what most governance programs haven’t built. Gartner researchers forecast that more than “40% of agentic AI projects will be canceled by the end of 2027, due to escalating costs, unclear business value or inadequate risk controls.” I’ve found that those “inadequate risk controls” often include platform authorization gaps.
The Third Governance Layer: Platform Authorization
Organizations that have closed this gap assign platform authorization as a dedicated function within their AI governance program, with explicit ownership, a review cadence and integration into the deployment life cycle. The investment is process design and accountability, not headcount. Without a named owner, the inventory doesn’t get built, the terms don’t get reviewed and the deployment gate doesn’t get enforced.
Why This Matters Now
Gartner analysts identify “loss of control,” or agents operating outside appropriate constraints, as “the top concern for 40% of Fortune 1000 companies by 2028.” Platform authorization is one of the most concrete and addressable forms of that risk.
The riskiest deployments are those that work exactly as designed, operating against platforms that didn’t authorize them and accumulating exposure until it can’t be ignored. Enterprise AI governance programs are well-constructed for the environment that existed two years ago. Building the third layer is how organizations keep their agent programs running through what comes next.
