Innovation Leaders Are Taking the Helm on AI Governance – Here’s Why
Enterprises are starting to recognize that robust frameworks are about more that just compliance and risk
By Corinium Global Intelligence
As enterprise AI initiatives grow in number and complexity, strong governance has become a make-or-break factor in their success. But in a shift from the traditional approach, it is now innovation leaders – not legal or compliance teams – who are increasingly taking the lead.
Research from Corinium and ModelOp reveals that 46% of enterprises now cite the Chief Innovation Officer as a key stakeholder in AI governance, while just 10% assign responsibility to legal, compliance and risk teams. This signals a strategic evolution in how businesses view governance: not merely as a risk control mechanism, but as a catalyst for scalable innovation.
“Enterprises are realizing that AI governance is not a bureaucratic hurdle – they’re recognizing it as a trustworthy engine to accelerate and scale innovation,” says Jim Olsen, CTO of ModelOp.
This shift is reflected in significant governance budgets. Most organizations – 43% – have allocated between $250,000 and $1m annually to AI governance, while another 27% are budgeting between $1m and $5m, and 9% spend more than $5m. No respondents reported a budget below $100,000.
This investment enables organizations to develop more agile and accountable approaches to governance – especially critical when responsibility for governance is distributed across teams.
Here are some key takeaways for those looking to foster innovation via good governance:
1. Assign Clear Leadership, Even in Cross-Functional Models
While 27% of respondents have assigned governance to a cross-functional committee or Center of Excellence, shared ownership carries a risk of stalled decision-making. For governance to be effective at enterprise scale, someone must take the lead.
“Someone needs to be accountable – not just for the risk but for ensuring AI investments actually drive business value,” says Skip McCormick, CTO of Cornerstone Technologies. “That’s just as important as governance.”
Leaders should assess whether their governance structure empowers decision-makers to move fast, maintain oversight, and adjust strategy based on outcomes.
2. Fund Governance as a Strategic Investment, Not an Afterthought
Though 99% of enterprises allocate some level of funding to AI governance, only 29% have a dedicated budget, with the rest embedding it in broader IT or innovation portfolios. This lack of visibility can lead to underinvestment in tools, personnel, and oversight.
Governance enables teams to evaluate AI’s impact on business outcomes – something too often overlooked.
“The ROI of AI investments is both incredibly hard to measure and incredibly important,” says McCormick. “Companies have launched these big initiatives and invested heavily in them, and now they’re coming back and asking, are we actually getting a return on this? The first response is often: I don’t know.”
3. Use External Partners to Scale Effectively and Reduce Risk
Seventy-four percent of respondents have engaged external consultants or professional services firms to support governance, and 14% use commercial AI governance software. This reflects a recognition that in-house teams alone may lack the capacity to manage compliance and performance monitoring after deployment.
“A lot of organizations – ones I’ve worked with included – try to build their own model risk management solutions,” says McCormick. “But then the question becomes: Who’s responsible when those metrics aren’t up to date? Or when they don’t meet the right thresholds? Or worse, when you’re not even measuring the right things?”
Relying solely on data scientists for post-deployment monitoring isn’t a sustainable strategy. As McCormick notes, “Data scientists are notorious for moving on to the next challenge after deployment.” That creates long-term risk for the business—and exposure in the event of a compliance breach.
“Instead of being left to answer to auditors, investors, or governance teams alone, you can point to a structured process,” he adds. “‘We thought our data scientists were on top of it’ isn’t a good defense.”
4. Prioritize AI Portfolio Intelligence
More than half of the leaders surveyed – 54% – have made AI Portfolio Intelligence a budgeted line item for 2025. This suggests growing awareness that AI success depends not just on developing individual models, but on managing the full lifecycle and portfolio performance.
Portfolio-level governance connects investment decisions with outcomes, enabling leaders to cut underperforming use cases, replicate successes, and hold teams accountable.
But challenges remain: 24% of organizations still cite budget constraints as a barrier to adopting governance tools or platforms, indicating that while intent is high, execution varies.
AI governance is no longer just about mitigating risk – it’s a strategic lever for innovation. But to make it work, enterprises must assign clear ownership, fund it properly, and embrace tools and partners that ensure governance scales with demand.
As many deal with hundreds of use cases in the pipeline and growing pressure to demonstrate ROI, strong governance leadership is no longer optional – it’s a competitive necessity.
For a more in-depth look at the emerging role of innovation leaders in this area, access the full report here.
