AI models can generate sophisticated marketing strategies, analyses, recommendations and content in seconds. But the quality of those outputs can vary considerably.

An AI response can be factually plausible but wrong. It can answer the prompt while missing the underlying business issue. It can provide sensible recommendations that are too generic to be useful. And it can sound authoritative while overlooking context that an experienced marketing professional would recognize immediately.

My work focuses on evaluating those outputs—not simply for whether they sound good, but for whether they are accurate, strategically sound and useful in a real-world business setting.

What I Evaluate

A structured approach to evaluating AI output

I developed the Abelt AI Marketing Evaluation Framework (AAMEF) to provide a consistent, business-focused methodology for evaluating AI-generated marketing analysis.

Each output is examined across six dimensions:

1. Prompt Understanding
Did the AI understand and answer the user's actual question? Did it address the requested task, audience and context?

2. Marketing Accuracy
Are the observations and conclusions factually supportable? Has the model made unsupported assumptions, overlooked evidence or presented inference as fact?

3. Strategic Reasoning
Does the AI connect its observations to meaningful marketing or business strategy—or merely produce plausible-sounding recommendations?

4. Business Impact
Does the analysis explain why its findings matter to the organization, customer, brand or business objective?

5. Actionability
Are the recommendations specific and practical enough for a marketing executive or business leader to act upon?

6. Executive Communication
Is the output clear, concise, appropriately prioritized and useful to its intended audience?

What an Evaluation Can Identify

A structured evaluation can identify:

Factual errors and unsupported claims — where an answer sounds credible but isn't adequately supported.

Reasoning gaps — where the model reaches a conclusion without adequately connecting the evidence to it.

Missed business context — important strategic, competitive or audience considerations the model overlooks.

Generic recommendations — advice that may be technically reasonable but could apply to almost any organization.

False confidence — conclusions presented with greater certainty than the available information warrants.

Missed opportunities — insights an experienced domain practitioner would recognize but the AI does not.

Executive usefulness — whether the final output is something a decision-maker could actually use.

Available for contract and project-based assignments involving:

• AI model-output evaluation

• Marketing-domain subject matter evaluation

• Prompt and task design

• Evaluation criteria and rubric development

• Comparative model evaluation

• Written evaluation rationale

• AI-generated content assessment

• Human refinement of AI-generated business analysis

AI Marketing Evaluation Portfolio

My portfolio includes four case studies evaluating AI-generated marketing analyses from ChatGPT, Claude, Gemini and Perplexity.

The cases demonstrate how structured human evaluation can identify factual issues, reasoning gaps, overlooked strategic considerations and opportunities to make AI-generated analysis more useful to business decision-makers.

VIEW AI MARKETING EVALUATION PORTFOLIO →

Why Marketing Experience Matters

AI evaluation is strongest when the evaluator understands the domain being evaluated.

My perspective is informed by more than 25 years of senior marketing experience across financial services, technology and B2B markets. 

That experience provides the context to distinguish between an AI response that merely sounds convincing and one that reflects sound marketing strategy, credible reasoning and practical business judgment.

VIEW PROFESSIONAL EXPERIENCE