“Start with a problem, not a tool. Before buying anything, run an operational audit to find where time and money are actually being lost, set basic guardrails for how AI can be used, and train your people on how the tools work. Audit, guardrails, and training form the foundation everything else is built on.”
“Enough to lead the people who do the technical work. You do not need to be a prompt engineer or a data scientist. You do need enough fluency to tell the difference between a smart investment and an expensive experiment, and to know when your team is solving a business problem versus chasing a technology fad.”
“Not expertise. Judgment. Your board does not expect you to understand large language models or AI architecture. They expect you to know what this means for the business, where the risk is, whether you have a direction, and whether you can answer when asked. That is an executive problem, not a technology problem.”
“A chatbot answers. An agent acts. An agent combines reasoning, memory, and access to tools so it can carry out multi-step work: retrieve information, interpret it, update another system, and follow up. That shift from producing an answer to taking an action is what changes both the value and the risk.”
“Recurring, information-heavy work where someone gathers from several systems, interprets, and moves a result somewhere else. But feasibility is not the test. The test is whether automating it produces enough value to justify the added complexity, and plenty of automatable processes do not.”
“Only as much as the consequence of being wrong justifies. Grant the least access the job requires, put approval points where actions become hard to reverse, log what it does, and keep a way to stop it. Capability should never automatically become permission.”
“No. Building a separate agent for every department recreates the shadow technology problem companies spent two decades cleaning up, and it accumulates debt that lands on the organization later. Coordinate before you proliferate.”