The Pivot: How Good AI Governance Enables Innovation

Last week I participated in “AI Ready or Risky?”, an executive working session at Northeastern University during Boston AI Week. An interactive event that brought together business leaders, academics and technology experts to examine AI initiatives through four lenses: business value and ROI, people readiness, data readiness, and governance and access.

I went into the event looking for perspective. AI is moving extraordinarily quickly, and I wanted to hear how other leaders from a wide range of functions were thinking about AI risk and governance. I left the morning encouraged, because many of the conversations reinforced something that we believe strongly at DataPivot: AI governance shouldn’t be about finding reasons to say no. It should give organizations the confidence and structure they need to say yes.

Every Organizations Is Already on the AI Journey

The real question is whether or not organizations understand how AI is being used, it’s about what data it can access and the risks that come with it.

What really surprised me was how many organizations are still early in putting formal governance around that activity. Large, highly structured organizations may have frameworks taking shape, but commercial organizations are operating much more loosely. Employees may be using generative AI tools, developers may be experimenting with agents, and departments may be adopting Copilot or other applications before the organization has established a consistent way to evaluate the risks across the enterprise.

That doesn’t mean the answer is to shut everything down. What it means is that organizations need a framework, which do not have to be complicated on day one.

Governance Makes AI Easier to Use, Not Harder

At DataPivot, we meet organizations wherever they are in their AI journey. We start by understanding what and where AI is currently being used for, what data it touches, what business outcome you’re trying to achieve and what happens if something goes wrong. From there, risks can be classified by their potential impact and addressed accordingly.

If something presents relatively little risk, there may be no reason to slow innovation down. If an AI use case could have a major financial or operational impact, it deserves stronger controls and a clear mitigation plan. We generally encourage organizations to work in roughly 90-day increments because AI is evolving too quickly. The objective here isn’t perfect governance. It’s about putting enough structure underneath innovation to enable people to move forward confidently and responsibly.

AI Governance Is an Extension of Data Governance

Another important takeaway for me from the conversation was how closely the AI conversation connects to the important work that organizations should already be doing around their data. Here at DataPivot, we’ve spent more than a decade helping organizations think about data governance, cybersecurity, and resilience. AI doesn’t replace those disciplines. It makes them even more important. If you don’t understand where your data resides, who can access it, how it is protected, and what systems are making decisions with it, adding AI can amplify those weaknesses rapidly.

That’s why we don’t begin by asking which AI technology a customer should buy and deploy. We begin with the business problem and the foundation underneath it. What are you trying to accomplish? What AI is already in the environment? What data does it need? Who owns the workflow? Where are the risks? Sometimes the first solution isn’t another technology at all, it may simply be a policy, documented workflow or set of controls that allows everyone to work from the same playbook.

Pivoting From Defense to Offense

AI can feel overwhelming. We talk with organizations that are very worried about shadow AI, data exposure, autonomous agents or simply not knowing what their employees are using and doing. That’s understandable, but remaining permanently on defense isn’t the right strategy either.

Our job is to help you pivot you and your organization pivot from a defensive posture to an offensive one.

Put the right foundations in place. Understand and prioritize the risks. Address the highest-impact issues quickly. Then use that structure to give people permission to experiment, innovate and pursue the business value that made AI attractive in the first place.

The organizations that succeed with AI won’t necessarily be the ones that move the fastest or impose the most controls. They’ll be the ones that understand how to move quickly without losing control and that’s the careful balance that we’re working to help our customers achieve at DataPivot.