Things can move pretty fast in the AI space. Just a couple of years ago, all IT buyers wanted to know was whether their AI tools could generate business gains. Now they want their vendors and suppliers toexplain how they manage risk, make decisions, and maintain accountability over time. That has propelled AI governance from a compliance to a business discussion.
But businesses are impatient. And they’re automating compliance processes in a bid to speed things up. Doing so risks leaving critical questions unanswered and documentation incomplete. Going forward, the companies emerging as winners will be the ones treating governance as an operational discipline, not a box-ticking exercise.
From the back office to the procurement team
Nearly two-fifths of firms are now harnessing AI in various ways to drive process efficiencies, boost worker productivity and transform customer experiences. But AI also means risk.
The risk of sensitive customer data leaking through unwise prompts. Inaccurate outputs that worsen business decision making. And of systems that expand the corporate attack surface. Two-thirds of organizations have suffered a security incident stemming from AI agents over the past year, according to one report.
In addition, more than half (54%) of respondents to our State of Information Security Report 2025 say they adopted AI technology too quickly and are now facing challenges in scaling it back or implementing it more responsibly.
AI in this context is not just a potential financial and reputational risk. It’s a regulatory one, thanks to the EU AI Act and a growing number of state-level laws in the US. These are risks that can’t simply be delegated to technology vendors.
For procurement teams historically focused on security, privacy, financial stability, and legal risk, AI has now become another material risk category. That has created a new appetite for AI governance. But it’s not just regulators that expect it. So do customers and boards.
For procurement, the commercial imperative has created a new urgency to provide evidence of robust AI governance. Those that can are more likely to speed through procurement processes and due diligence reviews. It’s good for buyers that want to empower workers and supercharge their output with AI products and services. And it’s good for sellers that want to win more business.
AI governance might appear in everything from supplier questionnaires and security assessments to RFP requirements.
At a bare minimum, organizations are asking their vendors to demonstrate how bias, accuracy, and explainability are managed. Whether there’s adequate human oversight. What data is used to train or inform AI outputs. And how AI-related incidents are identified and escalated.
Fast answers and faster documentation
As the maturity shift from experimentation to operationalization continues, AI governance means asking the hard questions. Who owns AI risk internally? How are AI systems approved before deployment? How are decisions monitored over time?
But asking the questions is one thing. Getting (and providing) the right answers is another. Organizations want proof that policies and principles on a supplier website are actually being implemented in practice. Among other things, they might ask for governance committee records, supplier reviews, monitoring activities, and audit trails and decision logs to help secure that assurance.
The risk is that, in the rush to turn AI governance into competitive advantage, they rely too heavily on automation. Yes, automated compliance tools can help organizations to monitor controls, track obligations, collect evidence, generate reports and surface risks. But governance ultimately requires human judgement.
The risk is that procurement teams end up blindly trusting AI-generated assessments, missing context-specific risks, failing to challenge inaccurate outputs, and overlooking emerging regulatory requirements. If overused, automation can create a false sense of compliance assurance.
Why structured governance wins the day
Organizations cannot automate their way to compliance. Effective governance requires a combination of technology, oversight, accountability, and continuous review. Structured approaches like ISO 42001 offer a better way forward.
Compliance with this best practice standard for AI governance helps suppliers to prove to buyers they have systematic processes in place to govern AI risk. And it’s also useful for buyers who want to prove to regulators and customers that they treat AI governance as a priority.
In these terms, it can accelerate innovation by removing uncertainty around acceptable AI use, driving consistent decision-making, accelerating procurement, and reducing legal and other risks. By embedding responsible AI practices into their operations, businesses can build trust with regulators, customers and partners, while positioning themselves to take full advantage of the opportunities that AI technologies offer.
ISO 42001 also makes sense in that it brings together AI governance with aspects of data privacy and information security. By addressing these in a single, cohesive management system, organizations can ensure visibility, identify areas of crossover, and adapt to new or changing regulatory requirements. All of this, while building critical operational resilience.
It’s no longer good enough to use AI to power business output. True competitive advantage comes from assuring customers, partners and regulators that you’re using it responsibly.
