The method behind the Recognition Benchmark
AI recognizes the reputation the public record makes clear.
Your organization knows what it stands for. AI encounters the public record, not your internal strategy.
When that record doesn’t support the reputation you want to own, AI may form weaker associations. Competitors with stronger public evidence can become easier to recommend.
The Recognition Benchmark examines how public evidence relates to AI representation. It shows what your communications team should strengthen and where resources should be redirected. The findings may also identify work that no longer deserves funding.
Better information for resource decisions
Don’t spend more before you know what can strengthen the position.
When teams don’t understand the evidence, they often respond to every visibility change by producing more work. They create additional content or launch another campaign without knowing whether either will improve the organization’s position.
That wastes communications resources on signals that don’t carry equal value. It can also reinforce a reputation the organization is trying to leave behind.
Funding work that reinforces associations AI already recognizes
Increasing activity without addressing the actual evidence gap
Treating every platform change as a communications priority
Copying a competitor’s tactics without understanding its advantage
Measuring visibility without connecting it to a business objective
Waiting while competitors build the evidence that will shape future answers
The goal isn’t more communications activity. It’s knowing which work can strengthen the intended position.
How the research reaches a recommendation
The methodology follows the path from organizational reality to communications decision.
The Recognition Framework begins with what is true about your organization. It then examines whether the public record supports that reality and how AI represents it.
The final stage turns those findings into a communications decision.
What is true?
We start with the business objective and the audience decision that matters. Then we define a reputation the organization can credibly earn and support.
Reputation shouldn’t be built for its own sake. It should help the organization achieve something important.
What the research establishes
- The business objective
- The reputation the organization wants to own
- The audience whose decision matters
- The question leadership needs answered
What can people and AI find?
AI doesn’t learn an organization’s reputation from its strategy documents. It encounters a public record built through media coverage and expert commentary. Third-party recommendations also influence that record.
We examine whether this evidence is strong and clear enough to support the reputation the organization wants to build.
How the evidence is evaluated
- Relevance: Does it support the reputation connected to the business objective?
- Credibility: Does it come from a source people and AI have reason to trust?
- Meaning: Does it establish a clear association that matters?
What picture does AI form?
We research how leading AI systems describe the organization. We also examine how those systems compare it with competitors and when they recommend it.
The purpose isn’t to collect isolated screenshots. It is to identify repeated patterns.
What we examine
- Where the organization is included and where it’s left out
- The attributes AI associates with the organization
- How those associations differ from its competitors
- Sources surfaced with the answer
- Changes across questions and AI systems
What should change next?
We identify which parts of the public record are helping the organization compete. We also locate weak or fragmented evidence. Separate attention goes to evidence reinforcing the wrong position.
The result is a documented basis for deciding where communications resources should go next.
What the recommendation can identify
- Work that deserves greater investment
- Resources that should be redirected
- Associations the organization should stop reinforcing
- Evidence gaps that require communications action
- Existing work that should continue without additional funding
How public evidence adds up
Volume creates visibility while alignment creates recognition.
One article rarely determines an organization’s reputation. The same is true of an executive interview or award. Individual online conversations are equally limited on their own.
AI forms a picture from the patterns those sources create together.
Visibility can reinforce the wrong reputation.
Consider an established company that wants to be recognized as an innovation leader. Its website emphasizes transformation, and its executives regularly appear in the media. But those executives are usually quoted about market conditions or operational performance. Independent coverage continues to describe the company as a reliable incumbent.
The company appears in AI answers, but not for the reputation it wants to build. Independent sources describe it as dependable, while the innovation story exists mainly in its own content. AI therefore has little public evidence connecting the company with innovation.
Independent evidence reinforces the intended position.
Independent sources repeatedly connect the organization with the same valuable attributes. The organization’s own content supports that position without carrying it alone.
The resulting pattern gives AI a clear basis for understanding when to recommend the organization.
The organization’s claims and independent evidence point in different directions.
The organization can remain visible without becoming strongly associated with the reputation it wants to own.
The Benchmark identifies whether the organization lacks sufficient independent evidence. It also shows when the available evidence reinforces the wrong reputation.
What the Benchmark examines
Recognition is more than whether your name appears.
The Benchmark evaluates seven dimensions that show how AI represents your organization and whether that representation helps you compete.
01VisibilityAre you present in the answer?
Visibility examines whether the organization appears when audiences ask questions connected to its business objective.
It identifies where the organization is consistently included and where it disappears.
02AssociationWhat does AI connect with your name?
Association examines the capabilities and ideas AI repeatedly connects with the organization.
The research determines whether those associations support the reputation the organization wants to own.
03DifferentiationDoes AI understand why someone should choose you?
Differentiation examines whether AI can articulate a meaningful reason to choose the organization over a competitor.
Being described accurately isn’t enough when every alternative receives the same description.
04CredibilityDoes independent evidence support the position?
Credibility examines whether trusted third parties reinforce the claims that matter to the organization’s reputation.
An association based only on content you own is weaker than one supported by independent sources.
05ConsistencyDoes the same picture appear across systems and questions?
Consistency examines whether the organization’s reputation remains recognizable when the system or question changes.
Big differences can mean there isn’t enough public information to form a clear view. They can also mean the evidence doesn’t agree.
06LeadershipIs the organization shaping the conversation?
Leadership examines whether the organization is recognized as a source of knowledge or a distinct point of view.
It also shows whether executive and subject-matter visibility contributes to the reputation the organization wants to build.
07RecencyIs newer evidence strengthening or complicating the position?
Recency examines whether recent public evidence supports the reputation the organization wants to build.
It also identifies newer information that benefits competitors or reinforces an outdated association. Momentum is evaluated separately when repeat research provides an earlier Benchmark for comparison.
The research process
We look for patterns over screenshots.
AI answers change as systems and sources change. The public record continues to evolve as well.
A single answer can’t support a communications strategy. The Recognition Benchmark uses repeated, recorded research to tell the difference between a lasting pattern and a one-time response.
Define the decision.
We identify the business goal and the reputation linked to it. This establishes the leadership question the research needs to answer.
Research and record the answers.
We examine a consistent core question set across five leading AI systems. The system, model, research date, search settings and relevant conditions are documented with each response. Any sources surfaced with the answer are recorded for comparison.
Interpret the patterns.
We compare results across systems and questions. This reveals recurring patterns and missing evidence. It also surfaces meaningful competitive differences.
Set communications priorities.
We connect the findings to work the communications team can influence. The recommendation determines what deserves investment and what should be redirected or stopped.
See the research standards
Consistent questions across systems
A consistent core question set is examined across five leading AI systems so the research can identify patterns beyond a single platform.
Repeated observations
Questions are tested more than once so one response isn’t mistaken for a lasting reputation pattern.
Documented answers
Responses are saved and compared under defined research conditions.
Source and public-record review
The research examines sources surfaced with the answers and compares them with the wider public record. This identifies evidence available to support the organization’s position without claiming that one source caused a response.
Relevant competitive comparison
Competitors are included when comparison helps explain where another organization has stronger evidence or a clearer position.
Defined research period
Every finding reflects a documented period of research. The report identifies the systems examined and the questions used during that period.
What the findings represent
The Benchmark documents a defensible position at a defined point in time.
AI systems change, and their access to sources varies. No research process can capture a permanent or universal answer.
The Benchmark doesn’t present one response as an objective truth about an organization. It documents repeated patterns across a defined research period. The systems and questions examined are also recorded.
Every finding is connected to the evidence available during that period. The report explains how that evidence supports the recommendation.
This gives leadership a credible baseline for making a decision now. A future review can repeat the relevant questions and compare the findings with the same starting point.
Human judgment
The research is systematic. The recommendation is led by a senior communications leader.
Every Recognition Benchmark is led by Holly Hansen, a senior communications leader with more than 15 years of experience.
Holly frames the research question and interprets the evidence herself. The recommendation reflects your business context rather than an automated output.
Make the next investment count
Find out which communications work can strengthen the position you need.
Establish a credible baseline before committing another quarter of communications resources.
The baseline shows whether your current work is supporting the intended position or reinforcing something else.
The Recognition Benchmark shows how AI represents your organization and whether the public evidence supports that position. Your team leaves knowing where to act next.
Prefer to ask a question first? Email Holly.
