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Beyond Rankings: Selecting the Right tools for AI Search Citations

Doug Johnstone

Author Perspective

“We used to rely on clicks to verify success. But today, buyers research anonymously inside AI interfaces, leaving no digital footprint. If you are selecting tools based on traditional rankings, you are optimising for the past. Here is how to choose an approach that measures what actually matters now – influence.”

Outline

  • Why buyer research is increasingly rep-free
  • Why clicks and rankings under-report influence
  • Four measurement categories that matter now
  • Cross-platform visibility, not just Google
  • AI share of voice against real competitors
  • Citation source mapping to reveal trust signals
  • Full-funnel coverage to find drop-offs
  • A practical vendor selection scorecard

Key Takeaways

If your evaluation criteria are still built around clicks, rankings, and keyword positions, you will select tools optimised for yesterday’s problem. Modern criteria must address citations, cross-platform visibility, and the trust network shaping AI answers.

  • Rankings can stay high while influence declines
  • Measure citations, not only traffic and clicks
  • Evaluate presence across multiple AI experiences
  • Map which sources AI uses to validate claims
  • Track visibility by buying stage and intent
  • Avoid prompt-volume theatre and vanity dashboards
  • Demand reproducible methods and governance
  • Run a short proof with real buyer questions

Introduction

B2B discovery has shifted from a click-based economy to an influence-based economy, where being present inside synthesised answers matters as much as being present in links. Buyers are increasingly completing research without engaging suppliers, and a material share of search behaviour ends without a click to the open web, which weakens rankings and CTR as reliable proxies for pipeline influence. (Google for Developers)

When you are selecting an approach to AI search visibility and generative optimisation, the core question is not “Which tool tracks my SEO best?” It is “Which approach can measure and improve how AI systems represent my brand while buyers research anonymously?”

What follows is a practical, buyer-aligned way to evaluate options without falling into the trap of measuring the visible funnel while the real decisions are happening in the dark funnel.

Why SEO tool selection criteria must change

The traditional SEO tool selection playbook was built on an assumption: discovery creates clicks, clicks create sessions, and sessions can be attributed to pipeline.

Two things have disrupted that assumption:

  1. Zero-click behaviours at scale
    Independent research suggests that for every 1,000 Google searches in the US, only a minority of clicks go to the open web, with the remainder staying inside Google’s own surfaces or ending without a click. (Google for Developers) When the interface increasingly answers the question directly, the “best” content can influence decisions without generating measurable sessions.
  2. The rep-free research reality
    Gartner has long pointed to the growth of digital-first buying, where a large share of B2B interactions occur in digital channels. (Perplexity AI) More recently, Gartner has also reported that many buyers prefer a rep-free buying experience, which further concentrates learning and preference formation into self-serve research. (arXiv)

So if your evaluation criteria are still anchored on rankings, impressions, CTR, and on-site engagement alone, you risk choosing a solution that optimises your measurement system rather than your market influence.

What to consider in the selection phase

When considering SEO and AI Search tool selection, buyers are doing four things in parallel:

  • Shortlisting: building a mental list of “credible” options
  • Validating: looking for proof, third-party confirmation, and implementation confidence
  • Comparing: scanning for differences, trade-offs, and risk
  • Aligning: socialising a decision internally, often with partial information

Much of this happens without form fills, without demo requests, and without any reliable attribution trail. That is the dark funnel problem: a large portion of learning and consensus-building is invisible to traditional analytics.

Your selection criteria therefore need to measure what is shaping shortlists and confidence, not just what is generating clicks.

The four measurement categories that separate “modern” from “legacy” approaches

1) Cross-platform presence (not just Google)

If your buyers use multiple AI and search experiences, measuring only Google performance gives you a partial picture.

What to evaluate:

  • Platform coverage: Can the approach measure visibility across the AI experiences that matter to your ICP?
  • Consistency: Does your brand appear consistently, or only on one surface?
  • Query handling: Does it handle long, natural-language queries (the way buyers actually ask questions)?

A practical way to test this without any vendor claims is to create a small set of buyer questions (more on that below) and manually compare results across the platforms your market uses.

2) AI share of voice (relative visibility, not absolute mentions)

When considering AI Search tool selection, consider that buyers are comparing relative strength of competing providers. Your measurement must support comparison too.

What to evaluate:

  • Competitive framing: Does the approach show who is recommended, who is cited, and who is absent for the same buyer question?
  • Segmentation: Can results be segmented by persona, industry, and region if your GTM depends on it?
  • Stability over time: Can you see whether your visibility is improving or being displaced?

Be cautious of approaches that report “you were mentioned” without context. Mention frequency without comparative share is rarely actionable.

3) Citation source mapping (who AI trusts, and why)

In generative answers, the most important question is often not “Are we mentioned?” but “What sources are being used to justify the answer?”

AI systems frequently cite or draw from third-party sources, and those sources shape perceived authority. Your evaluation should demand:

  • Source-level transparency: Which domains and assets are being used as supporting evidence?
  • Source quality signals: Are those sources credible for your category (industry bodies, standards, recognised publishers, authoritative explainers)?
  • Actionability: Can you identify which sources you should improve, update, or earn presence in?

This is also where your content strategy shifts from “publish more” to “become citable”. For AI Overviews and related experiences, Google has explicitly positioned these surfaces as providing quick understanding with links to supporting pages, reinforcing the importance of being a trusted cited source, not merely a ranked result.

4) Funnel-stage coverage (where you disappear)

In modern B2B discovery, visibility is not one moment. It is stage-by-stage.

Your selection criteria should test whether an approach can map visibility across stages such as:

  • Problem: “What is happening and why?”
  • Business case: “Is it worth fixing?”
  • Selection: “What should we choose?”
  • Implementation: “How do we do it safely?”
  • Optimisation: “How do we sustain it?”

The practical reason this matters is that brands often show up late in selection queries but are absent earlier when criteria and budget expectations are set. Your buying journey framework explicitly calls this out as a core evaluation need.

Common vendor pitfalls (and how to spot them fast)

Pitfall 1: Prompt volume theatre

Some approaches over-emphasise how many prompts they run. Volume sounds impressive, but volume does not equal insight.

Warning signs:

  • Results change materially without explanation
  • No clear query library, taxonomy, or governance
  • “More prompts” is positioned as the advantage rather than better measurement design

What to demand instead:

  • A defined, version-controlled set of buyer questions
  • Clear methodology for how queries are constructed
  • Repeatability across weeks, not just one-off snapshots

Pitfall 2: Surface-level mention tracking

A mention can be neutral, negative, or irrelevant. It can also be incidental.

Warning signs:

  • Reporting focuses on raw counts only
  • No visibility into the context in which the brand was included
  • No differentiation between being recommended vs being referenced

What to demand instead:

  • Contextual classification (recommended, compared, cited, incidental)
  • Stage and intent segmentation
  • Evidence and traceability back to citations or source patterns

Pitfall 3: Vanity dashboards

Dashboards are easy to sell. Decision-grade measurement is harder.

Warning signs:

  • Beautiful UI, weak method
  • Metrics that do not connect to action (what to change, where, and why)
  • No integration into operating cadence (weekly review, content backlog, governance)

What to demand instead:

  • Clear output-to-action pathways
  • Ability to prioritise work by stage impact
  • Support for reporting authority indicators in business terms, not only marketing terms

A practical selection scorecard (questions to ask vendors)

Use the scorecard below in demos and proofs. If an answer is vague, treat it as a risk.

A. Method and reproducibility

  1. How do you define a “citation” vs a “mention”?
  2. Can we lock a query library and rerun it weekly unchanged?
  3. How do you handle location, language, and personalisation effects?
  4. What controls exist to ensure repeatable results over time?

B. Cross-platform visibility

  1. Which AI and search experiences are supported today?
  2. Do results show platform-by-platform differences clearly?
  3. Can we tag queries by persona, industry, and region?

C. Share of voice and competitive context

  1. Do you report share of voice by query, stage, and theme?
  2. Can you show who is recommended and who is excluded?
  3. How do you handle competitor naming constraints and categories?

D. Citation source mapping and trust signals

  1. Can you show which domains/sources support the answer?
  2. Can you identify recurring “trust sources” in our category?
  3. Can you distinguish authoritative sources from low-quality ones?

E. Funnel-stage coverage

  1. Can we map visibility across buying stages and intent types?
  2. Can we identify where we disappear and why?
  3. Can we tie recommended actions back to specific stage gaps?

F. Actionability and workflow integration

  1. Do outputs translate into a prioritised optimisation backlog?
  2. Can we export evidence for stakeholders (marketing, sales, leadership)?
  3. How does this fit into a 30-60-90 day implementation plan?
  4. What does ongoing optimisation cadence look like in practice?

How to run a 2-week proof without creating a content treadmill

If you do only one thing before selecting a solution, do this:

  1. Build a “buyer question pack” (10 to 15 questions)
    Include questions across Problem, Business Case, and Selection. Make them the questions your buyers actually ask in natural language, not keyword fragments.
  2. Run the pack across the AI experiences your buyers use
    Record which brands are recommended, which sources are cited, and which themes repeat. This anchors your evaluation in market reality rather than vendor demos.
  3. Score each approach against the four measurement categories
    Cross-platform presence, share of voice, citation source mapping, and funnel-stage coverage.
  4. Choose based on decision-grade outputs
    The best approach will not just report. It will tell you what to fix, where to fix it, and why that fix should shift visibility in the stages that matter.

This method also protects you from chasing AI trends. You are building a measurement operating system that can adapt as interfaces change.

Next Steps

  1. Create a vendor evaluation scorecard using the four measurement categories above and test it against your current tooling.
  2. Select 10 to 15 high-intent buyer questions and run them across the AI experiences relevant to your market to see who is cited and why.
  3. Once you have selected an approach, develop your own implementation playbook focused on content extractability, structured formats, and operational cadence rather than “more blogs”.
  4. Before purchasing consider our next article in this series Blog 5 covering how to Maintain Citation Authority to keep your visibility momentum going.

FAQs – How do Optimise for AI Search?

About the author

Doug Johnstone advises B2B teams on how to evaluate AI search visibility and citation tooling without getting trapped by legacy SEO thinking. In this article, he shares a buyer-first lens for tool selection – because buyers increasingly research inside AI interfaces, and clicks no longer tell the full story. Doug is particularly focused on governance and decision hygiene: choosing measurement methods that are reproducible, comparable over time, and grounded in real buyer questions across the buying journey. He brings a structured approach to vendor selection, helping organisations assess cross-platform visibility, AI share of voice against real competitors, citation source mapping, and full-funnel coverage to find drop-offs that rankings hide. Doug’s goal is to help you invest in the tools and operating approach that measure influence – not just activity – so you can defend outcomes internally and improve authority in AI answers.

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