A Leader’s Guide To Operational AI in Action

Start With Search. Build the Context Layer. Scale to Agents.

When an answer that’s almost right is still wrong, productivity AI isn’t enough. This practical guide shows how some of the world’s most complex and regulated organizations build operational AI: grounded in their own knowledge, governed end to end, and ready to move from finding answers to taking action.

Why Operational AI, Why Now

The first wave of enterprise AI (chatbots, copilots, productivity assistants) was built for the office layer. For many teams, it delivered real value. But in organizations that build aircraft, develop life-saving molecules, or protect the financial well-being of millions, the decisions that matter most don’t happen in email threads or slide decks. They run on specialized, siloed systems of record: PLM, ERP, LIMS, portfolio management systems, and regulatory repositories.

Operational AI is built for that work. It connects to the systems where work actually runs, retrieves the domain-specific knowledge they hold, and produces answers that can be trusted, defended, and acted on. Yet only 10% of enterprises have deployed agentic AI in production. This guide gives leaders a practical framework to close that gap, backed by real results from industry leaders.

What you will learn

What Operational AI really is

Why productivity AI falls short for mission-critical work, and the three requirements every leader needs in place from day one.

The roadmap from search to agents

The four building blocks and three-stage maturity framework, plus the two traps that stall organizations along the way.

How industry leaders are doing it

How industry leaders turned trusted knowledge into measurable results, and where agents deliver outsized value next.