Why scaling AI now demands continuous oversight, and how Cognizant Trust™ Framework and Collibra AI Command Center turn governance from paperwork into a running capability.
Most AI governance programs were designed around a single moment: the review. A model gets documented, assessed, approved and shipped. Everyone signs. The binder closes.
That worked when AI mostly predicted things. It got harder when generative AI arrived with hallucinations, prompt injection and IP leakage, none of which show up neatly in a pre-deployment checklist. And it breaks down completely with agents. An agent doesn't just produce an answer; it decides, calls tools, reads enterprise data and triggers actions in other systems. The question is no longer "Can we approve this?" It is "Can we still vouch for this while it's running?"
That is a different question, and it needs a different kind of answer.
Why scale exposes the cracks
Working with Cognizant on our joint whitepaper, we kept landing on the same four pressures, and none of them is exotic. AI sprawl: models, use cases and agents multiply across business units faster than any central team can log them. Shadow AI: teams stand up initiatives outside the process, so the inventory is incomplete before it's finished. Opaque decision chains: an agentic workflow can span several systems, and reconstructing which data, model and policy drove an outcome becomes forensic work. And continuous risk: systems that retrain, update and act in real time don't wait politely for the next audit cycle.
None of this is a reason to slow down. The numbers say the opposite. McKinsey's 2025 State of AI survey found 88% of organizations use AI in at least one function, with 23% already scaling agentic systems and another 39% experimenting. [1] Yet MIT's Project NANDA, after studying more than 300 enterprise deployments, found that 95% of generative AI pilots produced no measurable P&L impact. [2] Read those together and the message is clear: the bottleneck isn't ambition. It's execution. Programs that can't show what their AI is doing, and who is accountable for it, are the ones that stall.
Foundations and oversight are not a choice
Here is the argument I'd defend most strongly from the whitepaper. Scaling AI safely takes two layers, and they only work together.
The first is the foundation: every use case, model and agent is registered, owned, classified and tied to the policies that apply to it. The second is continuous oversight: those same systems are watched as they run, change and act in production.
Skip the foundation and monitoring has nothing to anchor to; you get alerts about systems nobody owns. Skip the oversight and the foundation goes stale the day the system goes live. Documentation describes what was approved, not what is happening. Most programs today have some of the first layer and almost none of the second.
An operating model and a control plane
Closing that gap requires two things most organizations don't have in one place: a way of working and a way of seeing.
Cognizant Trust™ Framework covers the first. It gives organizations a framework-led approach for making responsible AI repeatable across the enterprise, built on the shared principles behind the EU AI Act, NIST AI RMF and OECD guidance. In practice that means aligning AI use cases to business objectives and decision impact, assessing and setting up the governance framework itself, building the AI asset inventory and mapping, establishing traceability across use cases, models, agents and data, translating regulation into operational controls, and standing up the committees, roles and literacy that make a governance operating model actually operate. Cognizant brings a decade as a Collibra partner and more than 100 Collibra implementations to that work.
Collibra AI Command Center covers the second. It's the control plane where that operating model becomes visible, enforceable and current. Cognizant's framework helps organizations define how AI should be governed. AI Command Center is where that definition runs, continuously, across the AI lifecycle.
Structure, operate, oversee
The journey inside AI Command Center runs in three moves.
Structure the foundation. Every use case, model and agent lands in a unified AI registry with ownership, business context, lifecycle stage and risk classification attached. ML engineers register systems straight from the command line, so the registry stays accurate as a by-product of building rather than a separate chore. Out-of-the-box assessment templates aligned to the EU AI Act, NIST AI RMF and AI UC-1 (the emerging framework for autonomous agents) give teams a trusted record before anything goes live.
Operate with live signals. Automated traceability connects each system to the data it consumes and the policies it must meet, with lineage collected across Databricks, Google Vertex AI, Amazon SageMaker and Bedrock, Azure ML and AI Foundry, SAP AI Core and MLflow. No manual stitching. On top of that, the AI Trust Score rolls documentation completeness, data quality, policy alignment and risk exposure into one continuously updated readiness signal per system. Static documentation becomes living evidence, and teams see when readiness shifts and why.
Oversee the portfolio. Leaders get a single live view across every use case, model and agent, organized by lifecycle stage, trust score and risk concentration, so they can spot where exposure is building, prioritize remediation and show boards, auditors and regulators current evidence rather than last quarter's report.
Agents raise the stakes
Everything above matters more once AI acts. A predictive model that drifts gives worse recommendations. An agent that drifts does things. It touches data, invokes tools and kicks off downstream processes. The gap between a periodic review and a live signal stops being a compliance nuance and becomes an operational risk. The progression is simple: predictive to generative to agentic, and periodic to continuous.
Control as the accelerator
The organizations that scale AI won't be the ones that pick between speed and control. They'll be the ones that make control part of how AI operates, so approval becomes a milestone rather than a gate and accountability travels with the system into production.
That's what the Cognizant and Collibra partnership is built to deliver: a framework that helps organizations define how their AI should be governed, and a control plane that keeps it that way while the AI runs.
Read the joint whitepaper, Scaling AI with trust and control, and explore Collibra AI Command Center.
References
[1] McKinsey & Company, The state of AI in 2025: Agents, innovation, and transformation, November 2025. https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai
[2] MIT Project NANDA, The GenAI Divide: State of AI in Business 2025, July 2025. https://mlq.ai/media/quarterly_decks/v0.1_State_of_AI_in_Business_2025_Report.pdf
All Cognizant Trust™ Framework and Collibra AI Command Center claims are drawn from the joint whitepaper Scaling AI with trust and control (2026).
- David TalagaDavid TalagaProduct Marketing DirectorCollibra
David Talaga
David Talaga
Product Marketing Director
Collibra
