Very Dry but Necessary

AI Governance Makes Financial Governance Look Exciting. It's the Plumbing.

The responsible deployment and use of AI begins with governance. Modeled (to some extent) on the ideas of financial accountability, AI governance looks to the future while its financial progenitor looks backwards. Financial governance is retrospective. It explains and controls the movement of money that has already been spent. AI governance is prospective. It establishes the boundaries within which future decisions, models, and deployments can occur.

Governance is the process of translating organizational values, risk tolerances, and authority structures into operational rules.

Financial accounting covers levels of financial authority and documentation requirements. Its sole focus is the management of cash and capital. It determines who can sign for what, what constitutes an acceptable invoice or receipt, how costs/budgets are allocated, and the elements of good reporting.

AI accountability is a couple of layers more complex. That’s because the underlying elements are dynamic and likely to change over time. Large organizations are likely to require dozens of policies. Many of the key pieces of AI governance remain to be understood and formalized:

• Data Governance

• Model governance

• Human oversight

• Security and compliance

• Vendor and license management

• Performance and productivity measurement

This is not exciting stuff.

But, attention to governance prevents a future of locking the gate after the horse has left. It’s easier to build in advance than it is to manage after the catastrophe. It requires the very hard work of navigating layers of approval levels for interdepartmental work.

And, we are learning what we need as we go. The Silicon Valley philosophy of leaping before you look is great for discovery and innovation.It’s also an excellent way to create risks that only become visible after the system is embedded in the business. Actually harnessing innovation and disruption demands clear repeatable constraints.

As dry as it is, AI governance is going to require the attention of our best minds. This is where the dryness of the arena is a significant problem. The sorts of people who are good at defining constraints are not the same people who succeed with discovery.

The challenge is not finding the perfect balance between control and innovation. The challenge is building institutions that can accommodate both. The people who discover new possibilities are rarely the people who define durable constraints. AI governance requires both disciplines, working together while the terrain continues to shift beneath them.

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