“Always invite AI to the table.”
Ethan Mollick offered this as the first of four principles in Co-Intelligence. The advice encourages people to test AI across the tasks they actually perform so they can learn where it helps, where it fails, and how the technology changes the shape of their work. Invitation is an experiment, not automatic delegation. Because model capabilities and limitations are uneven, abstract assumptions become stale quickly. Direct use can reveal surprising strengths while keeping the human responsible for goals, verification, judgment, and the consequences of the result. The business value of the line appears when it changes a real decision. It directs attention toward assumptions that routine can hide and invites a team to examine the system behind an outcome instead of treating the visible result as a complete explanation.
A team can select low-risk tasks, record the original effort and outcome, test several prompting and review methods, and compare quality, time, and new failure modes. Useful patterns should be documented with examples and conditions rather than promoted as universal tricks. Application therefore requires an explicit mechanism: what should people do differently, what signal should move, and when will the team examine the result? Writing those elements down turns general agreement into a testable management practice.
Confidential, regulated, safety-critical, or identity-sensitive work may not belong at the table without approved tools and controls. Users should protect data, verify claims, disclose material assistance when appropriate, and preserve the ability to perform or evaluate essential work themselves. Context matters because an idea that improves one situation can create harm in another. Leaders need boundaries, dissent, representative evidence, and a way to correct course. Accountability cannot be delegated to the quotation or the tool used to implement it.
AI products change rapidly, making organizational learning a continuing process. Shared evaluations, role-specific guidance, and incident reporting are more durable than a one-time training course or a blanket policy written before teams understand actual use. Choose one reversible task this week, define success and prohibited data, complete it with and without AI, and record where the tool improved the work, introduced risk, or changed the expertise required. The review should preserve what was known when the choice was made, document what changed, and share the learning beyond one team. Repeated with discipline, that cycle converts a memorable sentence into better judgment rather than another short-lived management theme. It also makes the cost of assumptions visible, allowing future teams to reuse proven knowledge while recognizing the point at which a different customer, risk, or operating environment requires fresh testing.
Ethan Mollick offered this as the first of four principles in Co-Intelligence. The advice encourages people to test AI across the tasks they actually perform so they can learn where it helps, where it fails, and how the technology changes the shape of their work.
Invitation is an experiment, not automatic delegation. Because model capabilities and limitations are uneven, abstract assumptions become stale quickly. Direct use can reveal surprising strengths while keeping the human responsible for goals, verification, judgment, and the consequences of the result.
A team can select low-risk tasks, record the original effort and outcome, test several prompting and review methods, and compare quality, time, and new failure modes. Useful patterns should be documented with examples and conditions rather than promoted as universal tricks.
Confidential, regulated, safety-critical, or identity-sensitive work may not belong at the table without approved tools and controls. Users should protect data, verify claims, disclose material assistance when appropriate, and preserve the ability to perform or evaluate essential work themselves.
AI products change rapidly, making organizational learning a continuing process. Shared evaluations, role-specific guidance, and incident reporting are more durable than a one-time training course or a blanket policy written before teams understand actual use.
Choose one reversible task this week, define success and prohibited data, complete it with and without AI, and record where the tool improved the work, introduced risk, or changed the expertise required.
Explore more "Quotes of The Day"
Discover more notable quotes from influential voices across politics, science, business, technology, sports, and culture. Each quote offers insight into how ideas, beliefs, and decisions shape the world around us.