The State of AI Search in Marketing
Most wealth firms are still trying to solve the wrong problem. Instead of asking ‘How do we rank better?’, the more important question now is much less comfortable. ‘Are we even eligible to be recommended?’
AI is not simply changing how people search; it’s changing how financial decisions are shaped. Increasingly, investors and advisers are asking AI to compare providers, explain tax strategies, summarise propositions and shortlist options on their behalf. Models synthesise, interpret and even recommend. The competitive set is being defined upstream, before a prospect even visits your website or downloads a brochure.
For CMOs, and especially those of us in wealth, we need to sit up and pay attention
How can CMOs adapt to AI search right now?
Our industry is built on trust, credibility and regulated responsibility. Historically, the brand and relationships sat at the point of persuasion. Performance marketing and search drove visibility, while brand and reputation helped clients choose.
But AI shifts that sequence: the first hurdle is no longer preference, it’s eligibility.
“Winning in AI-led discovery is therefore not about tools, platforms or chasing the latest model. It is about eligibility, trust and framing, coordinated across the organisation.”
Ritchie & Mark of The Digest.
If an AI model can’t confidently put your firm in the right category, describe what you do in clear terms and corroborate your credibility through third-party signals, you may never enter the answer at all. You can optimise perfectly and still not be included.
The new failure mode: visibility without inclusion.
A firm can remain indexed, compliant and apparently visible, while AI systems quietly shape the shortlist elsewhere. Your traffic begins to decline, and consideration softens. The cause is often misdiagnosed because most organisations still measure performance rather than recommendations.
There is also a structural leadership tension emerging across wealth firms. Optimisation feels controllable: adjust the site, improve technical SEO, produce more content and test formats. Those activities are visible and measurable while trust building, brand clarity and cross-functional alignment are slower, harder and span multiple teams, including compliance, PR, product and client experience.
But AI systems are not asking which firm has the most optimised page. They are asking which firm is credible enough to cite safely.
Brand clarity matters in a very literal way
If your positioning relies on abstract language, elastic promises or broad claims, interpretation confidence drops. In financial services, ambiguity is penalised not only by regulators but increasingly by machines. Clear category definition, explicit use cases and consistent language across earned media, regulatory disclosures and product documentation increase eligibility.
Search, in this context, is no longer a channel. It is an ecosystem. AI tools, social platforms, industry forums, professional commentary and on-site experiences form one connected discovery system. Prospects experience a single, joined-up evaluation moment, but many firms are still structured in silos. That gap is where eligibility is lost.
Reframing the problem internally
For wealth and financial services leaders, the task is not to chase every new AI feature or declare a new “GEO strategy.” It is to reframe the problem internally from traffic to decision inclusion. To treat PR, compliance clarity, product accuracy and client experience as discovery infrastructure. To make explicit trade-offs between speed and coherence, experimentation and commitment, optimisation and trust.
If you work in wealth or financial services, here’s the point:
In an AI-shaped market, your competitive advantage will not be who shouts loudest or publishes the most. It will be who is trusted enough, clear enough and consistent enough to be included in the answer before the client even knows they are choosing.
Because in this industry, losing clicks is manageable, but losing consideration is existential.
AI strategy is easy to debate but harder to execute.
In their Digest paper, Mark and Ritchie identify the 15 most valuable on-the-ground actions CMOs can take.
Lever 1: Treat PR and earned media as AI-readable trust signals, not just reach drivers
Why this matters?
AI models rely on authoritative third-party sources to validate claims.
What leading teams are actually doing: Prioritising explainers, thought leadership and category education in PR Ensuring consistent language across earned media Tracking whether coverage improves citation and framing, not just impressions
Lever 2: Shift from keyword lists to a small set of priority prompts
Why this matters?
Trying to optimise for everything leads to incoherence.
What leading teams are actually doing: Defining the 10–20 questions they must be recommended for aligning content, PR, video and product narratives around those prompts and using those prompts as a unifying lens across teams
Lever 3: Make brand positioning simpler, clearer and more literal
Why this matters?
Abstract brand language performs poorly in AI interpretation.
What leading teams are actually doing: Rewriting positioning in plain, functional language and being explicit about what the brand is for and not repeating that clarity consistently across all visible sources
Lever 4: Align the organisation around a ‘search everywhere’ mindset
Why this matters? Consumers do not separate AI, social, ecommerce and on-site search.
What leading teams are actually doing: Expanding “search” beyond Google to include AI tools, social and retail environments. Aligning social, content, SEO and e-commerce teams around shared discovery goals
Lever 5: Establish a cross-functional operating rhythm
Why this matters? Fragmented optimisation leads to inconsistent brand framing.
What leading teams are actually doing: Treating AI-led discovery as a transformation initiative and appointing a coordinator or PM to lead.
Lever 6: Accept imperfect measurement and prioritise directional learning
Why this matters? Waiting for perfect data is slowing progress.
What leading teams are actually doing: Tracking citations, visibility and referral signals directionally. Using changes in framing and inclusion as leading indicators, and most importantly, being unafraid to learn by doing rather than waiting for standards
Lever 7: Engineer “decision-ready” content, not just discovery content
Why this matters: AI is now being used to evaluate, compare and decide, not just discover.
What leading teams are doing: Auditing sales decks, proposals and product claims for clarity and verifiability while ensuring language is consistent, factual and defensible. Removing vague superlatives that collapse under AI scrutiny
Lever 8: Strengthen local-language and local-source presence
Why this matters: AI responses vary dramatically by language and locale.
What leading teams are doing: Auditing how brands appear in non-English prompts, investing in local-language sources, forums and media and ensuring local dominance is reflected in local citations
Lever 9: Treat product and CX as discovery inputs, not downstream outcomes
Why this matters? AI increasingly infers brand quality from what customers say, not what brands claim.
What leading teams are doing: Pulling frontline feedback, reviews and service data into discovery conversations while working with product and CX teams to close experience gaps that show up in AI answers. Treating reviews as a strategic asset, not reputation hygiene
Lever 10: Prepare for agent-to-agent competition, not just consumer discovery
Why this matters? Agents are increasingly making decisions on behalf of people and other agents.
What leading teams are doing: Thinking about how brand value can be expressed as data, not just story. Exploring direct-to-agent experiences or proprietary assistants and stress-testing propositions against logic-first decision-making
Lever 11: Double down on YouTube as a discovery and trust engine, not just a media channel
Why this matters: Early signals suggest AI overviews overweight YouTube as a brand authority signal.
What leading teams are actually doing: Treating YouTube as search infrastructure, prioritising explanatory, comparative and “how it works” formats and optimising titles, descriptions and transcripts for clarity, not clickbait
Lever 12: Actively build presence on Reddit and forums where real experience lives
Why this matters: Community discourse is treated as evidence of lived experience.
What leading teams are actually doing: Identifying relevant subreddits and forums, monitoring how the brand is framed, not just mentioned, and encouraging authentic participation, not promotion
Lever 13: Rebuild content around longer, conversational queries
Why this matters? AI prompts are longer, more specific and contextual.
What leading teams are actually doing: Shifting from short SEO pages to deeper, question-led content. Structuring pages to answer multiple related questions. Designing content to be read, summarised and reused by machines.
Lever 14: Invest heavily in FAQs, structured answers and plain-English explanations.
Why this matters: FAQs are disproportionately cited in AI-generated answers.
What leading teams are actually doing: Creating FAQs at category, product and use-case level Writing answers in clear, non-jargon language Embedding FAQs across site sections
Lever 15: Invest in AI literacy across the organisation, not just marketing
Why this matters? Poor internal AI use creates poor external assumptions.
What leading teams are actually doing: Training teams to challenge and validate AI outputs, educating people on how models source and weight information and treating AI capability as a core business skill
As a CMO in wealth, my takeaway from this?
Stop asking how to optimise for AI and start by asking whether your firm is structured to be clearly understood, independently validated and confidently recommended when a model is asked to compare your category.
In a trust-based industry, recommendations are not a bonus; they're fast becoming the new entry point.
