AI Governance sounds like something regulators and compliance officers should be concerned with. The rapid rise of agentic AI - systems that plan, decide, and act autonomously - makes AI Governance a core competency for AI First firms.
We are rapidly approaching a world where AI agents will be technically capable of approving your loan, handling your legal defense, and managing your stock portfolio. Humans in the workplace have clear boundaries, sometimes explicitly documented but often implicitly understood.
AI Governance lays out a corporate framework for how AI agents make decisions, when humans need to be in the loop, and more importantly who is accountable for those decisions. Especially if a decision turns out to be a bad one.
One of the first steps is to distinguish between Data Governance and AI Governance.
AI is only as good as the data it’s fed from retrieval pipelines. The phrase “garbage in, garbage out” is still relevant today. This means Data Governance and AI Governance need to work hand in hand. Agentic AI starts with good quality foundational data to make good decisions.
While Data Governance and AI Governance are similar disciplines, and AI is heavily reliant on Data Governance, they are two different but related frameworks.
The key purpose of Data Governance is to produce high quality data. Whereas the key purpose of AI Governance is to clearly define who is accountable when AI leads to bad outcomes.
Most firms already have Data Governance frameworks. They can clearly identify if data quality is sufficient for business purposes. They know who is accountable for data quality, how to measure it and what to do when bad data shows up.
Assuming an AI model has been fed good quality data and has been appropriately trained, it then goes on to generate output. In the case of Agentic AI the output from an AI model is taken one step further and is used to make decisions. There needs to be a whole separate framework around the model, the output and the decisions.
This is where AI Governance kicks in.
Firms need AI Governance to ensure that their AI model is generating output that is fair and unbiased, and the decisions made by AI agents are transparent and explainable. AI cannot be a black box crunching data, then making decisions in the real world.
The guardrails and policies around when an agent can and cannot make a decision need to be documented. There needs to be a reliable and robust mechanism to enforce those rules.
Corporations need clear legal and ethical accountability when Agentic AI makes decisions in their name.
Perhaps more important than anything else, there needs to be a clear set of policies defining precisely when a human needs to be in the loop.
Last, there needs to be an audit log to track and review how decisions were made.
That’s lot of upfront work for Agentic AI to be successful.
When AI Governance is done right, multiple layers of complex technology need to be orchestrated, and likely in near real time. Additionally, there will be a massive amount of brain power from business teams to configure and maintain those technologies. Compliance, Legal and Technology need to be active participants, but they don’t own the business configuration and ongoing updates.
AI Governance frameworks need to be in place before Agentic AI solutions are launched, and then they need to be continuously monitored and updated. The AI Governance model needs to be embedded in the project lifecycle, but as with all frameworks the key to success is creating a cultural mindset across an organization to embrace AI Governance.
That mindset needs to come from the top.
Done right, AI Governance will deliver better decisions, more timely responses, improved accuracy and reduced risk, but more than any of this it will create trust.
The evolution of technology over the last few decades has led humans to assume output from systems is truthful with very few exceptions. AI is vastly more complex and an inherently different species from any technology we have seen before now. It hallucinates, and when it hallucinates it often does so convincingly. When a human isn’t being truthful our sixth sense kicks in. AI doesn’t sweat or glance away when it’s hallucinating. It provides us with unemotional text and our human predisposition to technology leads us to accept that output as factual.
AI Governance helps us better understand what needs to be done when AI hallucinates. This is critical. Especially if the next wave of AI is going to start making decisions that were previously entrusted to humans.
Firms that dismiss AI Governance as a Legal and Compliance issue, or even worse a Technology task, will struggle with Agentic AI. Their Agentic AI solutions will inevitably make bad decisions, not because of inherently bad models but because there isn’t a comprehensive framework to prevent bad outcomes.
There’s likely a whole raft of new jobs that will be created by AI Governance. People who understand how AI architectures really work are going to be in high demand. One of the key roles will be an AI Officer. That role will not only include driving AI innovation but also owning AI risk and the implementation of an enterprise AI Governance framework.
It took a while for Data Governance frameworks to become ubiquitous, and then some time after that for business areas to fully own their data to deliver trusted business insights. Similarly, AI Governance needs to be fully embraced by the underlying business areas so that AI can make trusted business decisions.
The rapid ascent of Agentic AI likely means AI Governance needs to be embraced much faster this time around.