Leaders deploying agentic AI need more than vision. They need a risk playbook.

We reviewed recent arXiv research and combined it with our hands-on work in regulated industries.

What we learned: the risks range from simple operational overreliance on agentic AI to more complex threats, like in-context scheming.

Most teams are only prepared for some scenarios (or none).

This guide breaks down the risks and shows you how to build systems that are auditable, controllable, and production-ready.

What we wrote is simple. And it works.

This Week: AI That Starts With People ft. Jimmy Iliohan

Can AI adoption succeed without rethinking how people actually work?

Is your proof of concept solving a real problem, or just adding more tech for tech’s sake?

Jimmy Iliohan, General Manager at LINKITSYSTEMS, shares why successful AI starts with workflow clarity, cross-functional alignment, and a people-first mindset.

We covered:

• Why the real challenge begins after the POC, when systems meet live data and real-world complexity

• How mapping workflows before introducing AI avoids wasted investment and user frustration

• The difference between U.S. and European approaches to AI-driven interfaces

• Why AI should enhance (not replace) human connection and judgment

• How internal champions (not top-down mandates) drive lasting adoption

AI Can’t Save You From Bad Data

Data quality matters more than ever before.

While some AI use cases let you “prompt your way” to value, the more complex and meaningful automations (like invoice reconciliation or claims analysis) depend on a deep foundation of clean, historical data.

If your company is pulling back on data investments to chase AI shortcuts, this might be the wake-up call you need.

Watch the full clip to rethink where ROI really starts.