Answer capsule
The FTC warns businesses not to exaggerate what an AI product can do or claim superiority without adequate proof. An executive choosing a human coach should apply that ordinary evidence discipline to any AI-enabled method, outcome, or differentiation claim.
What the source establishes
- The FTC’s February 27, 2023 business guidance says AI-related advertising claims remain subject to established truth-in-advertising principles.
- The FTC tells businesses not to exaggerate what an AI product can do and to possess adequate proof for performance claims.
- It asks whether a claimed AI product performs better than a non-AI alternative and whether that comparison is supported.
- The guidance also asks businesses to consider reasonably foreseeable risks and whether a product actually uses AI as claimed.
Identify the exact coaching claim
The direct buyer question is what the coach says AI changes: preparation speed, pattern recognition, reflection, personalization, availability, behavior, leadership performance, or business results. Terms such as AI-powered, evidence-based, transformative, or predictive are too broad to evaluate without a named method, population, comparison, measure, and time period.
Ask the named coach to separate the human engagement, the AI tool, and any combined method. A claim about a platform should not be assigned to the practitioner, and a practitioner testimonial should not become evidence that the technology caused the result.
The accountable team should translate this point into a named workflow, affected population, source data, human owner, approval right, exception path, retained evidence, and review date. That translation is what separates an interesting AI development from a decision that can be governed and evaluated.
Match proof to the promised outcome
A demonstration can show an interface; a case story can show one reported experience; a satisfaction score can show sentiment; and a controlled study may support a bounded causal claim. These evidence types are not interchangeable. The stronger and more specific the promise, the closer the evidence should match the actual buyer, method, outcome, and setting.
The executive should inspect who produced the evidence, sample and exclusions, comparison, measure, observation period, attrition, conflicts, and whether the configured use resembles the studied one. Missing evidence should remain visible rather than be filled by the coach’s confidence or the fluency of an AI output.
The accountable team should translate this point into a named workflow, affected population, source data, human owner, approval right, exception path, retained evidence, and review date. That translation is what separates an interesting AI development from a decision that can be governed and evaluated.
Test superiority and risk claims separately
The FTC asks whether an AI product is claimed to outperform a non-AI alternative. A coach who says AI makes the engagement faster or better should identify the alternative and the observed difference. Convenience, novelty, availability, and outcome are distinct claims and may require different evidence.
Risk also deserves its own record. Confidential strategy, personnel issues, sponsor power, psychological safety, tool error, privacy, and role boundaries cannot be offset by a generalized benefit claim. The executive should know how the coach limits AI use, detects problems, responds, and preserves a human-led one-to-one relationship.
The accountable team should translate this point into a named workflow, affected population, source data, human owner, approval right, exception path, retained evidence, and review date. That translation is what separates an interesting AI development from a decision that can be governed and evaluated.
Keep advertising review and coach fit distinct
FTC guidance addresses business claims; it does not certify a coach, prescribe a coaching method, resolve professional ethics, or determine whether an engagement fits one executive. A truthful bounded claim can still describe a poor fit, and an absence of an enforcement action does not prove a claim.
The final selection should join claim evidence with practitioner identity, experience, method, confidentiality, sponsor terms, continuity, cultural and role context, boundaries, and the executive’s working judgment. Record what was verified, what was reported, and what remains unknown before relying on the AI distinction.
The accountable team should translate this point into a named workflow, affected population, source data, human owner, approval right, exception path, retained evidence, and review date. That translation is what separates an interesting AI development from a decision that can be governed and evaluated.
Decision test
Ask whether the source changes the decision itself, the evidence required, the implementation sequence, or only the language used to describe an existing capability. Record which claims are directly supported, which are provider statements, which require an independent test, and which remain unknown. A source-linked review should make uncertainty easier to see, not bury it inside a blended score.
Questions to take into review
The publication supports research and executive decision preparation. It does not provide legal, financial, accounting, employment, clinical, cybersecurity, investment, procurement, or implementation advice.