Part 2: Designing AI Governance That Works

In Part 1, we explored why AI governance has become a strategic imperative for organizations aiming to achieve their AI goals. We also discussed why governing AI can be so challenging—not just because the technology itself evolves so quickly, but also because it pushes organizations to rethink and restructure established processes and practices to effectively adopt AI. 

Building on the need for this future-oriented approach to governance, this blog dives into what effective AI governance actually looks like in practice. While AI governance is still maturing as a discipline, the insights here are drawn from dozens of real-world experiences helping organizations design and implement their own approaches to governing AI.

Design Practices for Effective AI Governance

Even though most organizations understand that AI is fundamentally different from past technologies, many still try to govern it the same way—by applying familiar frameworks or turning governance into a checklist. That might feel safe, but it rarely works. It turns governance into a box-ticking exercise that often slows things down without actually helping teams make better decisions.

We’ve seen what happens when organizations take a different approach. The practices below reflect patterns that consistently lead to more effective governance—especially when treated not as a fixed set of controls, but as an evolving capability that helps organizations use AI thoughtfully and at scale. 

Practice #1: Embed governance into how decisions are made

To be effective, AI governance needs to show up at the point where AI-related decisions happen: for instance, in funding discussions, product roadmapping, procurement reviews, vendor evaluations, and legal sign-offs. Designing for this means mapping out where AI enters or advances within the organization and embedding governance into those workflows—ideally through existing committees or approval mechanisms rather than creating entirely new ones. If governance isn’t influencing real choices, it’s just documentation.

Where to start: Identify the 3–5 most common paths through which AI enters the organization (e.g., vendor purchases, internal development) and build in review checkpoints early in each process, before decisions are finalized.

Practice #2: Give teams clear guidance—and clarity on when to involve others

Good governance empowers teams to act responsibly in their day-to-day roles—without needing to route every decision through a central committee. For governance to become part of how teams work, they need both autonomy and direction. That includes knowing when escalation is appropriate and where to turn for advisory support or questions. The goal is to build a culture that avoids bottlenecks caused by over-engineered processes, while also preventing blind spots by involving the right stakeholders when it matters. Achieving this starts with defining which types of use cases or risks require escalation, and who’s responsible for making that call.

Where to start: Define a set of common AI decision scenarios or risk indicators that should trigger review or consultation. Make it clear who to engage, when to bring in cross-functional input, and how those conversations are expected to happen.

Practice #3: Focus on practical literacy and change management, not just policy

Responsible AI policies are a necessary foundation, but they’re not enough on their own. Too often, organizations invest heavily in policy development while underinvesting in the practical tools and support teams need to apply those policies in real decisions. Governance needs to show up in the flow of work, not just in annual training or static documentation. That means offering ongoing, role-specific resources like checklists, quick-reference guides, and FAQs that meet teams where they are. It also means investing in the change management required to bring people along—helping teams not just understand AI and how to use it responsibly, but feel equipped and supported to do so as part of their everyday work.

Where to start: Develop role-specific FAQs and set up channels where teams can ask questions, share learnings, and raise concerns. Consider creating a community of practice, either company-wide or within key functions, to support peer learning and surface emerging issues.

Practice #4: Treat governance like a product—build it, test it, and improve it

AI systems rarely deliver high performance out of the gate. The initial model is just the starting point, it gives you something to train, test, and refine. In practice, most of the value emerges through iteration. Governance should be approached the same way. Waiting to design a fully comprehensive framework risks slowing momentum, or worse, allowing AI to advance without any guardrails in place. Start with a minimum viable framework focused on your highest-risk use cases, pilot it in a few areas, and refine based on feedback and real-world use.

Where to start: Roll out a lightweight governance model in a few high-priority areas. Gather feedback from teams and reviewers, identify pain points, and make targeted adjustments before expanding more broadly.

Practice #5: Establish clear ownership across the organization

One of the fastest ways for AI governance to fail is unclear accountability. Without defined roles and decision rights, governance becomes fragmented, duplicative, or ignored altogether. A successful model includes both executive sponsorship and cross-functional ownership, often through a central coordination group with representatives from legal, risk, data, and business functions. That kind of coordination doesn’t have to slow things down. AI decisions often span multiple domains, and when you have a forum where those perspectives are integrated early, teams can reach clearer, more strategic outcomes, without the delays and disconnects that come from stitching together siloed decisions after the fact.

Where to start: Formalize governance ownership across at least three key levels: executive sponsorship, cross-functional coordination, and operational execution.

Practice #6: Prioritize transparency and traceability

Documentation shouldn’t be a compliance burden; it should support better decisions and make systems easier to maintain, evaluate, and evolve. Focus on capturing the decisions that matter: why a system was approved, what risks were identified, what mitigations were put in place, and how performance will be monitored. This helps teams stay aligned, makes it easier to revisit decisions as circumstances change, and ensures accountability when issues arise.

Where to start: Set clear, minimal documentation standards—for example, using model cards, system datasheets, or decision logs—and establish a repeatable process for capturing key decisions and approvals across teams.

Practice #7: Treat governance as a lifecycle responsibility

Once you’ve established a governance framework, it’s critical to ensure that clear procedures are in place from the earliest stages of scoping and design for any new AI solution, when foundational decisions are being made. Governance shouldn’t begin at deployment; it should shape how systems are developed from the start and continue after a system is in production.  Many risks only become visible once a system is in a real-world environment, whether due to data drift, shifting social or business contexts, or how the system interacts with users or downstream processes. Because AI systems are dynamic and operate within evolving environments, they need to be continuously monitored to ensure they’re still working as intended.

Where to start: Map your governance checkpoints to both your AI development lifecycle and your broader product or SDLC processes. Be clear about when governance should be involved, what’s expected at each stage, and how it fits into existing workflows.

Practice #8: Connect governance to your AI strategy

One of the biggest gaps organizations face is failing to connect governance with their broader AI strategy. When governance is framed purely as a way to reduce risk or prevent harm, it can serve that function—but it’s limited in scope and impact. The real opportunity comes when governance is designed to support how the organization wants to use AI: where it aims to scale adoption, where it sees business value, and what capabilities it wants to build. In that context, governance becomes not just a safeguard, but an enabler, helping teams move faster, with clearer guidance, shared standards, and more confidence in what “responsible AI” looks like in practice.

Where to start: Start by clarifying your organizations strategic objectives for AI, then assess how your governance efforts are supporting those goals. Where are they enabling progress, and where might they be creating friction or gaps? Use that insight to focus your governance design on what matters most to the business.

There’s no universal playbook for governing AI, but there are patterns that work. The practices in this piece offer a starting point for designing governance that is actionable, grounded, and built for how AI shows up in your organization. In Part 3, we’ll look at how to take these practices further by choosing a governance model that fits—tailored to your strategic goals, organizational structure, and level of AI maturity.

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