Conversational AI speeds up familiar work. Configuring AI to interrogate your data — what the authors call directing intelligence — surfaces insights that familiar ways of working tend to miss.


James Yang/theispot.com
While conversational generative AI can be used to test thinking, an agentic system can hold a large volume of data and sustain analyses across entire data sets without losing the thread. Working with agents, executives can define context (what the agent can access), capabilities (what the agent can do), and orientation (what the agent pays attention to). Multiple agents can be configured to do analyses with different orientations against the same data to compare approaches to solving a problem.
The most valuable thing a professional produces is not a faster analysis or a better summary. It is insight: a genuinely new way of seeing a problem, a connection that changes how they understand a situation, or a pattern that nobody has named. Think of a strategy consultant who identifies the real competitive challenge behind a client’s margin erosion, or a team member who notices a silence in the data that everyone else has learned to take for granted.
What makes this kind of discovery hard is that expertise, the very thing that makes professionals effective, also makes certain kinds of insight difficult to reach. The frame that lets them see a problem clearly also shapes what they look for — and what they stop looking for. But insights that change thinking are often found at the edges of that frame, such as in the friction between competing interpretations.
Conversational AI is already moving in this direction. A well-constructed prompt can surface competing interpretations, expose gaps, and challenge assumptions. But agentic AI — systems that are configured and directed rather than conversed with — can take users further still. Unlike a prompted conversation that is bounded by what a human supplies and thinks to ask, an agentic system holds more data and sustains analytical orientations across entire data sets without losing the thread. The discovery moves are the same; the depth is not.
Using agentic AI this way demands a professional skill different from both prompting and automation: knowing how to design systems for insight and how to make sense of what they reveal. We call it directing intelligence.
Two Ways of Working With AI
Most professionals encounter AI as a conversation: They type a question, evaluate the response, refine, then ask again.
Références
i. J. Sloan and V.L. Glaser, “Robotic Artistry: Four Surprise Pathways for Generative Artificial Intelligence-Assisted Abductive Theorization,” express manuscript, Strategic Organization, published online April 28, 2026, https://doi.org/10.1177/14761270261448648.
ii. V.L. Glaser, J. Sloan, and J. Gehman, “Organizations as Algorithms: A New Metaphor for Advancing Management Theory,” Journal of Management Studies 61, no. 6 (September 2024): 2748-2769, https://doi.org/10.1111/joms.13033.
Remerciements
This research forms part of the UK Research and Innovation project “Innovating Across Sectors” (MR/Y034430/1; PI: Angela Aristidou). The analyses and conclusions presented here are those of the authors, not of the funding body.

