AI Visibility and Business Decisions | Denise Au-Yeung

DENISE’S SIGNAL

AI visibility becomes meaningful when it connects with a business question that leaders already care about.

KEY POINTS

Distribution, sales recruitment and service teams bring different business questions to AI visibility analysis.
Not every function needs to own the work, but the people closest to the issue should help shape it.
Check AI answers against approved information and actual customer or operational evidence.
Distinguish visibility and answer accuracy from sales, recruitment and service outcomes.
Agree the decision, owner and evidence needed before measuring.

The same issue can look different across an organisation

Recent conversations with leaders across insurance and banking have reinforced an important observation for AVER Solutions: the relevance of AI visibility is not always immediately obvious outside marketing, IT or AI teams.

That reaction is understandable. A distribution leader may be concentrating on a major product launch, or may be focused on sales recruitment and productivity. A service leader may be measured on customer experience, complaints and claims handling. Each is accountable for different outcomes.

AI visibility analysis examines how an institution, proposition or issue is represented in answers to a defined set of questions on selected AI platforms. That information becomes useful when it helps a leader understand an existing business concern. The starting point is the decision the evidence could support.

Broker distribution starts with the commercial agenda

In one discussion with a broker distribution leader, the immediate priority was a major product launch. The conversation was a useful reminder that the relevance of AI visibility needs to connect with commercial questions already on the agenda.

When prospective customers or intermediaries use AI to explore a financial need or compare providers, is the institution appearing where it should reasonably be considered? Is its proposition described accurately? Are important benefits, limitations and points of differentiation clear? What understanding might a customer bring into a subsequent conversation with an adviser or broker?

These questions can help a distribution team assess how its proposition is understood before a sales conversation begins. They do not establish that stronger AI visibility increases new business. Sales performance has many influences; visibility analysis provides another perspective on the information environment around customer consideration.

Sales recruitment needs its own business questions

In another discussion, the possible connection between AI visibility and sales recruitment did not generate immediate interest. That prompted a useful question for AVER Solutions: had the business relevance been explained clearly enough?

When prospective candidates use AI to explore an insurance career, they may encounter information about an organisation, its training, career development, earning opportunities or professional reputation. An insurance sales team can examine whether those answers describe its career proposition accurately and whether important elements are missing or misunderstood.

AI answers are not evidence of what candidates actually value. Sales leaders and recruiters supply that context through the questions candidates raise, the reasons people join or decline, and the concerns that recur. Recruitment experience should shape the questions being tested and help interpret the findings.

Customer service connects expectations with delivery

A service leader offered another perspective: increasing AI visibility was not part of the function’s responsibility. That may be entirely reasonable. Marketing, corporate affairs, IT or an AI team may lead the work.

Service and operations teams nevertheless see where customer expectations meet what the organisation delivers. They understand recurring enquiries, complaints and confusion about policies, claims processes or service commitments.

If an AI answer describes a claims process incorrectly, the organisation should check the answer against approved information. The gap may call for clearer or more accessible content. If an answer reflects criticism arising from genuine customer experience, the response may require attention to the service itself. An AI answer alone cannot establish which explanation is correct.

Complaints records, claims experience and customer feedback are therefore essential context. Clearer communication can address an information gap; it cannot substitute for resolving an operational problem.

Bring the relevant experience into the measurement

Customers do not experience an institution according to its organisation chart. They may encounter a marketing message, ask an AI platform a question, speak with an adviser, submit an application and later make a claim. Different teams own these interactions, but the customer experiences one institution.

The opportunity is to connect the knowledge needed for a particular question. Distribution teams know what customers and intermediaries ask. Sales recruiters understand candidate concerns. Service teams see recurring confusion. Product teams know the intended benefits and limitations. Marketing and technology teams contribute communication priorities and an understanding of AI capabilities and constraints.

A defined business issue gives these teams a reason to participate without creating a new responsibility or KPI for every function. It also helps leaders judge whether a finding warrants action. Sometimes the evidence may show that no immediate change is needed.

Decide what evidence would justify action

AI visibility is one input into a business decision. A stronger presence in selected answers may coexist with unchanged sales, recruitment or service outcomes. Leaders need to distinguish the quality of the information from the effectiveness of any subsequent intervention.

Before starting, agree what the organisation wants to understand, whose experience will help interpret the findings, what action a meaningful gap could justify, and how that action would be assessed. Those questions make the work more useful than a standalone visibility score.

The management question is straightforward: what are we trying to understand, and which decision should the evidence help us make?

AVER SOLUTIONS PERSPECTIVE

AVER Solutions recommends connecting each AI visibility finding to an accountable owner, a possible response and a separate measure of the business issue. This makes the analysis useful without treating a change in visibility as proof of business value.

AVER Insight helps establish the business question, customer context and information gaps.
AVER Action translates supported findings into clearer content and communication where improvement is warranted.
AVER Checkpoint measures how brands, channel development, product propositions and customer experience are represented across agreed AI platforms and questions.
AVER Impact supports implementation and assessment against the relevant business objective, within an agreed scope.

An illustrative claims example shows the distinction. Suppose an AI answer says a customer must visit a branch, while the approved process allows an online submission. First verify the policy and supporting pages. Then correct unclear or inconsistent information, assign an owner and record when the change goes live. Recheck comparable AI answers for accuracy. Separately review customer enquiries or avoidable branch visits to assess whether the service problem has changed. Neither result should be used as a substitute for the other.

ABOUT AVER SIGNALS

AVER Signals, published by AVER Solutions, brings together perspectives from leaders across insurance, banking, technology and transformation, alongside clearly attributed company reflections. Each contribution explores a practical question: as AI reshapes our industry, what should leaders prepare for, and how can we use AI to work smarter, serve customers better and strengthen trust?

The aim is not to predict the future, but to help our industry prepare for it through clear, practical and responsible leadership perspectives.

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Questions leaders should consider

Does every business function need to own AI visibility?

No. An organisation can appoint one accountable lead while involving other functions where their knowledge is needed. Marketing, corporate affairs, IT or an AI team may coordinate the programme. Distribution, sales recruitment and service teams should help define relevant questions, validate findings and decide what action is justified. For a claims question, for example, the service owner should confirm the approved process before a content team changes the explanation. A useful record identifies the question, evidence, decision owner, proposed action and review date. Participation should follow the business issue rather than create a visibility KPI for every department.

How can a bank or insurer choose useful questions for AI visibility analysis?

Start with a decision the team already needs to make, then turn real customer, intermediary or candidate enquiries into a defined question set. For a product launch, test how AI explains the relevant need, compares options and describes the institution’s own proposition. Include unbranded questions for market context and branded questions for product understanding, reporting the two separately. Agree the market, language and platforms, and retain the wording for repeat checks. AVER Insight can help organise these questions and the supporting context. Findings should be interpreted within that defined sample, rather than presented as the experience of every customer.

How should leaders distinguish an AI information gap from a service problem?

Compare the AI answer with approved product or process information and with evidence of actual customer experience. An incorrect description may point to an information gap, an outdated source or an unsupported AI answer. Repeated complaints about a correctly described process may indicate a service issue. These possibilities require different owners and responses. AVER Action can support clearer content where the explanation is deficient; the operational owner must investigate genuine delivery problems. Record the observed answer, relevant sources where available, verification outcome and action taken. An AI statement is a lead to investigate, not proof that a complaint or operational failure occurred.

What should organisations measure before and after improving their content?

Keep visibility and answer quality separate from customer and commercial outcomes. Establish a baseline using agreed questions, platforms and languages. Track comparable mentions, citations and recommendations where available, and review whether answers accurately explain the relevant product or service. Record publication dates and material changes so later observations have context. AVER Checkpoint supports repeated observation of the agreed question set; subject specialists should interpret accuracy and business relevance. Separately track the outcome that motivated the work, such as recurring enquiries or candidate understanding. Note changes in scope, unavailable answers and other interventions rather than attributing every movement to the content update.

Can stronger AI visibility be linked directly to business, sales recruitment or service performance?

Visibility evidence alone cannot establish that AI visibility work caused a business result. Sales, recruitment and service performance have many influences, including product changes, campaigns, staffing and market conditions. Leaders should define the intended outcome before acting and retain the relevant business measures alongside the AI observations. Where feasible, use a suitable comparison and record other changes that could explain the result. AVER Impact can support implementation and review within the agreed engagement, but a visibility score is not a measure of revenue or return on investment. A useful conclusion may be improved information accuracy even when commercial impact remains unproven.

About Denise Au-Yeung

Denise Au-Yeung is Cofounder of AVER Solutions. With more than 20 years in the insurance industry and substantial experience in banking, she has led customer strategy, distribution growth, brand transformation, digital innovation and ecosystem partnerships across Hong Kong, Mainland China and Asia. She cofounded AVER Solutions to help financial institutions turn AI from a technology discussion into practical business value, strengthening how organisations understand markets, make decisions, serve customers and are understood, trusted and recommended by AI platforms.

Connect with Denise Au-Yeung on LinkedIn