Across industries, CIOs are rolling out generative AI through SaaS platforms, embedded copilots, and third-party tools at a speed that traditional governance frameworks were never designed to handle. AI now influences customer interactions, hiring decisions, financial analysis, software development, and knowledge work — often without being formally deployed in the classical sense.

The result is a widening gap between rapid AI deployment and responsible-use protections. Organizations adopt AI faster than they can govern its usage, then scramble to retrofit controls after something goes wrong.

Interviews with five practitioners — each working at a different pressure point of enterprise AI — reveal why this gap persists and what leaders must do to close it before regulators, auditors, or customers force the issue.

Why governance breaks the moment AI hits real workflows

The first problem is structural. Governance was designed for centralized, slow-moving decisions. AI adoption is neither. Ericka Watson, CEO of consultancy Data Strategy Advisors and former chief privacy officer at Regeneron Pharmaceuticals, sees the same pattern across industries.

“Companies still design governance as if decisions moved slowly and centrally,” she said. “But that’s not how AI is being adopted. Businesses are making decisions daily — using vendors, copilots, embedded AI features — while governance assumes someone will stop, fill out a form, and wait for approval.”

That mismatch guarantees bypass. Even teams with good intentions route around governance because it doesn’t appear where work actually happens. AI features go live before anyone assesses training data rights, downstream sharing, or accountability.

What breaks first, Watson said, is data control and visibility. Employees paste sensitive information into public genAI tools, and data lineage disappears as outputs move across systems. “By the time leadership realizes what’s happening,” she said, “the data may already be gone in ways you can’t undo.”

What to do: CIOs must move from model governance to usage governance. You may not control the model, but you can control how it’s used, what data it touches, and where outputs flow. Governance has to be embedded as tollgates inside workflows, not in policy documents that are reviewed after the fact.

Why legacy data governance struggles under genAI

Even where governance exists, it’s often built on assumptions that no longer hold. Fawad Butt, CEO of agentic healthcare platform maker Penguin Ai and former chief data officer at UnitedHealth Group and Kaiser Permanente, argues that traditional data governance models are structurally unfit for generative AI.

“Classic governance was built for systems of record and known analytics pipelines,” he said. “That world is gone. Now you have systems creating systems — new data, new outputs, and much is done on the fly.” In that environment, point-in-time audits create false confidence. Output-focused controls miss where the real risk lives.

“No breach is required for harm to occur — secure systems can still hallucinate, discriminate, or drift,” Butt said, emphasizing that inputs, not outputs, are now the most neglected risk surface. This includes prompts, retrieval sources, context, and any tools AI agents can dynamically access.

What to do: Before writing policy, establish guardrails. Define no-go use cases. Constrain high-risk inputs. Limit tool access for agents. And observe how systems behave in practice. Policy should come after experimentation, not before. Otherwise, organizations hard-code assumptions that are already wrong.

Why vendor AI is where governance collapses

If internal AI governance is weak, third-party AI governance is worse. Richa Kaul, CEO of Complyance, works with global enterprises on risk and compliance management. She sees a sharp divide: while companies are relatively mature in governing AI they build themselves, they are much less prepared when AI arrives embedded in vendor products.

“What we’re seeing is use before governance,” she said. “And it’s often governance by committee — 10 to 20 people reviewing vendors one by one without a shared baseline of questions.” Too often, enterprises ask open-ended questions about AI privacy and accept reassuring answers — what Kaul calls “happy ears.”

Mature governance shows up in specific questions. Is customer data used to train models? Is it reused across clients? Is the LLM accessed via an enterprise deployment or a consumer interface?

“A vendor using Azure OpenAI has a much lower risk profile than one calling ChatGPT directly,” Kaul said.

What to do: CIOs should start with a basic but overlooked step: scrutinize vendor subprocessor lists. Cloud providers are well understood. LLM providers are not. AI has created a second, poorly mapped subprocessor layer — and that’s where governance breaks down.

Why bans fail and incidents repeat

Technology controls alone do not close the responsible-AI gap. Behavior matters more. Asha Palmer, SVP of Compliance Solutions at Skillsoft and a former US federal prosecutor, is often called in after AI incidents. She says the first uncomfortable truth leaders confront is that the outcome was predictable.

“We knew this could happen,” she said. “The real question is: why didn’t we equip people to deal with it before it did?” Pressure to perform is the root cause. Employees use AI to move faster and meet targets — just as they have in every compliance failure from bribery to data misuse.

Blanket bans on genAI do not work. “If you take away responsible use,” Palmer said, “people will use it irresponsibly — in secret, in ways you can’t govern.”

What to do: Shift from awareness training to behavioral learning. Palmer calls it “moral muscle memory,” a scenario-based practice that teaches people to stop, assess risk, and choose a better action under pressure.

Regulators and auditors look for evidence that the right people have received the right training for the risks they actually face. One-size-fits-all AI literacy is a red flag.

Why confidence is not enough when auditors arrive

The final gap appears when organizations are asked to prove their governance works. Danny Manimbo is ISO & AI Practice Leader at Schellman, an attestation and compliance services provider. He sees the same failure pattern repeatedly.

“Organizations confuse having policies with having governance,” he said. “Responsible AI principles don’t matter if they don’t influence real decisions.”

Auditors might start with a simple request: show us a documented AI risk-based decision that changed an outcome. Mature governance leaves fingerprints — including delayed deployments, rejected vendors, and constrained features. Immature governance produces vague assurances.

“The most expensive governance work is the work you try to do after deployment,” Manimbo warned. Walking back data lineage, accountability, and intended purpose is extraordinarily difficult once systems are live.

What to do: Treat AI governance as a management system, not a compliance exercise. Standards like ISO/IEC 42001 work only when they connect risk management, change control, monitoring, and internal audit into a continuous loop.

You can tell governance is working when it changes business decisions, not when it produces documentation.

Closing the responsible AI gap

Across all five interviews, one theme recurs: the responsible AI gap is not primarily a technology failure. It’s a governance timing failure. Controls are being designed for yesterday’s systems while AI is already shaping today’s decisions.

Several of the sources stressed that CIOs should stop framing responsible AI as a future-state program and start treating it as an operational hygiene issue — closer to identity management or financial controls than to ethics committees.

Watson from Data Strategy Advisors emphasized that visibility is the first non-negotiable step. Enterprises that cannot enumerate where AI influences decisions — especially through SaaS tools — are already exposed. “You can’t govern what you can’t see,” she noted, warning that many companies still lack even a basic inventory of AI-affected workflows.

At Penguin Ai, Butt reinforced that point from a data perspective, arguing that inventories must shift from platforms to systems-in-context. An AI feature embedded in HR software and the same feature embedded in marketing automation do not carry the same risk. Treating them as identical is a governance illusion.

Complyance’s Kaul added that the same principle applies externally. Vendor AI governance breaks down when enterprises accept generic assurances instead of mapping where their data actually flows. In her experience, simply forcing teams to trace AI subprocessors exposes risks that executives did not realize they had accepted.

Palmer from Skillsoft focused on the human layer that sits underneath all of this. Governance frameworks collapse, she argued, when they assume people will slow down under pressure. “Pressure doesn’t disappear,” she said. “You have to train for it.” Organizations that fail to do so should not be surprised when employees improvise with AI in unsafe ways.

Finally, Schellman’s Manimbo offered a blunt litmus test: if governance has never delayed a deployment, rejected a vendor, or constrained a feature, it probably does not exist in practice. “Governance has to leave fingerprints,” he said. Otherwise, it is indistinguishable from aspiration.

Taken together, the interviews suggest that closing the responsible AI gap does not require perfect foresight or exhaustive policy. It requires earlier intervention and clearer accountability. Organizations that act now — while AI use is still fragmented and informal — have a chance to shape behavior. Those that wait will inherit systems they no longer control and risks they can no longer explain.

At that point, governance is no longer a choice. It becomes damage control.

Related reading:

• GenAI in productivity apps: What could possibly go wrong?
• Overcome governance and trust issues to drive agentic AI
• The trick to balancing governance with innovation in the age of AI
• Deloitte’s AI governance failure exposes critical gap in enterprise quality controls