AI Agents, Optimization, and the Future of Decision Intelligence
Everyone's Buying AI Agents. Almost No One Is Asking What They're Actually Good At.
A reality check on where AI investment pays off, where it doesn't, and why the math still has to do the heavy lifting.
AI, large language models, optimization, agents: most executives use these terms interchangeably, and most executives are wrong to. In this episode, Russell Halper, Founder of Insight Kitchen, joins our Podcast to pull these ideas apart and explain what each one is actually built to do.
Halper doesn't sugarcoat where the technology is weak. Large scale numerical decisions and complex trade-offs, the kind of problems where getting it almost right isn't good enough, are still poorly handled by AI on its own. That's exactly where optimization earns its return, and where Halper argues executives should be applying the same investment discipline they'd apply to any other major bet. The conversation closes on people: how AI is reshaping consulting and expertise, and why judgment, context, and leadership matter more, not less, as these systems get more capable.
What you'll take away:
A clear line between AI, LLMs, optimization, and agents, and why treating them as one technology leads to bad investment decisions
Where AI spending is actually paying off today, and where the returns are still weak
Why keeping people in the loop, to guide systems, review outcomes, and manage risk, becomes more important as the technology improves, not less
