How to Choose an AI Marketing Consultant

Once you know the type of consultant you need, these are the criteria I would weigh, in this order. They hold across industries and tend to predict whether an engagement produces something useful.

• Depth of search and SEO foundation. AI visibility is built on the same signals that drive search: clear, structured, credible information about your business in the places where engines read. This is not my opinion alone — the industry consensus is that answer-engine optimization is a specialized form of SEO, and a strong SEO foundation is usually a prerequisite. A consultant without a real SEO background is guessing at a discipline whose foundation they do not know.
• Real prompt research, not invented prompts. This is the criterion that, in my experience, most separates real practitioners from the rest. It has to start with the questions your customers are asking AI engines, captured across all the major engines in the engines’ own words, not a list of prompts a consultant assumes people use. When I helped a pool services retailer, the questions that mattered were the ones buyers pose before hiring (“how do I find a reliable pool contractor”), which ran across every engine and were recorded as the engines answered them. Made-up prompts produce generic findings that may not match your situation. If a consultant cannot show you the real questions, across engines, that they built the analysis on, the rest is built on sand.
• A method that outlasts the tools. AI tools change every month. What sustains engagement is a repeatable process for diagnosing where you stand and deciding what to do — not a list of this quarter’s favorite software. It matters more to describe the data you need and how you use it than to name any tool. If a consultant is not taking raw data and running their own process to analyze it, identify the gaps, and produce recommendations that an AI engine will find worth citing, the results will be generic and may not fit your business. The ability to measure outcomes. Ask how they will show what changed. A consultant who can track which engines mention you, which cite you, and how that shifts over time is measuring what matters. For a UV lamp manufacturer, that meant watching which engines named the company rather than its competitors, and where the gaps closed afterward. One who can only point to general activity is not measuring anything useful.
• Genuine depth in one specialty. A firm that claims to do everything in AI rarely goes deep where it counts. Narrow, demonstrated expertise beats broad familiarity. A consultant who has done this kind of analysis across several very different businesses (say, a hydraulic coupler manufacturer and a medical imaging center) has seen how engines behave in practice, something you cannot pick up from a webinar.
• Geographic and industry fit. A consultant who understands your market and your business can deliver useful recommendations faster than one who is learning your context on your budget. Someone who knows the Connecticut and Northeast markets and has done this across manufacturing, medical, retail, and home services starts with pattern recognition rather than from scratch.
• Transparency about their process. You should be able to see a consultant’s process before you commit. If they cannot walk you through how an engagement runs, that is information. In my process, for instance, every recommendation lands in a cross-reference workbook tied to a specific page on the client’s site, the customer questions that page should answer, and the content to publish there, so the client can see exactly what will change and why, rather than a generic list of advice.
• References and real examples. Ask to see the evidence behind the claims. A consultant with a track record can cite it.

The field is moving quickly, and the best consultants are keeping pace. A few shifts are worth understanding as you evaluate who is current.

The discipline is shifting from deploying tools to redesigning how marketing operates. Early AI consulting was largely about installing software. The more durable value now lies in rethinking how a business produces content, measures visibility, and appears in the answers customers receive.

GEO, AEO, and LLMO have become specialties. Optimizing for how AI engines generate and source their answers is now distinct enough from traditional SEO to warrant dedicated expertise, even though it rests on the same foundation.

AI search is consolidating across platforms, yet the answers remain fragmented. The same question runs through ChatGPT, Google’s AI Overviews and AI Mode, Gemini, Perplexity, and Claude — and each returns a different set of businesses. Consultants who think in terms of a single platform have a narrow view and will miss important patterns and insights.

Measurement frameworks are catching up. The key question is no longer only where you rank, but whether AI engines mention you, cite you, and direct customers your way. Consultants worth hiring can measure that and treat the numbers as directional rather than as a promise of precise gains, since published findings on AI visibility still vary widely.

Amid the noise, there are concrete places where good AI consulting earns its keep.

It makes visibility legible. A clear read on what AI engines say about you, where you are cited, and where you are absent turns a vague worry into something actionable. It produces competitive intelligence at the level of individual questions — what an engine says about you compared with what it says about the businesses it names instead. It speeds up research and drafting without sacrificing quality when a real editorial standard underpins the tools. And it ties engagement to measurement, so content and technical fixes connect back to whether your visibility improves. The common thread is that the value comes from judgment applied to the tools, not from the tools alone.