Episode Details

With the rise of agentic AI, strong governance will help organizations manage security and accuracy. ModelOp CTO Jim Olsen describes enterprise agentic AI risks and solutions.

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Podcast transcript:

Jason Lopez: How do companies control, govern, and deploy AI safely at scale? When Jim Olsen, the Chief Technology officer at ModelOp, talks about AI adoption, the theme of what he says rests on the idea of restraint.

Jim Olsen: Try not to just build things for the heck of it. That’s why you have to tie all this back to a use case to understand, what is my goal and what does success look like?

If you don’t have a clear process in place everybody can understand and follow, then you lose that trust, and the solutions just don’t happen. That’s, of course, a missed opportunity.

If you have a truly resilient agentic system, it can adapt to new business needs, new things that just pop up. Given that autonomy, how do you actually control and make sure it doesn’t disclose all your user passwords?

Jason Lopez: This is the Tech Barometer podcast I’m Jason Lopez. This story is part of our ongoing thought leader series with people at the cusp of AI technological development, like Jim Olsen of ModelOp, a platform that helps govern, monitor, and manage AI and machine learning models to ensure they are compliant, reliable, and aligned with regulatory and ethical standards.  Agentic AI is the operating model that sets direction, plans the work, and brings team members together to achieve a larger goal. When you talk to Jim Olsen, before he gets into the AI conversation, he’s laser focused on why you need it in your organization. What’s the use case?

Jim Olsen: If you can actually get agents to automatically do that stuff and do it reliably, it obviously increases the success of your business at its core. That’s why it really comes down to what is your business value, what is your use case, and the success is going to look very different based on that. Ultimately, my business is successful, everyone’s happier, and I’ve reduced my overall costs.

Jason Lopez: But that promise only holds if the system performs as expected. The models need to be trusted and safe.

Jim Olsen: The nature of generative AI is that it does go out and perform differently based on very minor changes or even sometimes no changes at all. If you don’t have some insight into that, naturally, people are concerned and paranoid about what could happen. You need that clear, transparent process in place in order to build that trust that we can see what’s going on. We do know we’ve put the research in behind this to make sure it’s going to behave okay.

Jason Lopez: Olsen says that while a clearly defined use case is the bedrock of an organization’s deployment of AI, another critical part of the strategy has to be managing AI’s behavior.

Jim Olsen: When you use these tools or allow people to use these tools, what impact that could have? What kind of information could go out? What kind of information could come in? What kind of damage could be done? So you need an approval mechanism in place to actually do that.

You do need some automated process in order to scale this in an appropriate manner, because what you really need to know is, okay, what are the use cases out there that need to use agentic AI? Is it appropriate for them to be using agentic AI? Then what pieces are they using? What tools? What model? How many tokens are they actually using? Are you getting your value back out of your investment in t