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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsReputation data used to belong to marketing. Reviews were answered to protect the brand, and listings were updated when a location changed. AI has widened the audience for that information in two directions at once. Customer-facing AI search and answer engines outside the company read public signals such as reviews, listings, and location details to understand a business. Enterprise AI systems inside the company can read customer comments and feedback. In a September 16, 2026 sponsored BrandPost published on CIO, Kristi Melani, Chief Marketing Officer of Reputation, argues that because the same data feeds both directions, marketing and technology leaders now share the job of managing it. This article explains the two directions, where the responsibilities meet, and the governance questions a joint CMO and CIO team can work through.
What reputation data covers
The argument concerns a broader set of information than star ratings. In the BrandPost’s framing, the relevant inputs fall into three groups:
- Public signals: reviews, business listings, and location information such as addresses, hours, and services offered.
- Customer feedback: comments and survey responses that customers give the company directly or through review channels.
- Operational context: the location, product, transaction, and time attached to a piece of feedback, which tells a company what the feedback is actually about.
The first group is what outside AI systems can read. The second and third are what internal AI systems can use. The same underlying records often appear in both, which is why the ownership question no longer stays inside one department.
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The external direction: AI reading public business information
When a customer asks an AI assistant or answer engine where to get a repair done or whether a store is open on Sunday, the system assembles an answer from public information it can find. The BrandPost’s central claim is that reviews, listings, and location details are among the inputs those systems use to understand a business. The author presents this as an account of how these systems work, not as documented system behavior.
Why multi-location businesses feel this first
For a single storefront, a stale listing is an irritation. For an organization with dozens or hundreds of locations, it becomes a consistency problem. The BrandPost points to stale or inconsistent hours, services, and location information as factors that can make a company’s public representation less reliable. A simplified illustration of the pattern:
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- A regional team changes Saturday hours for one branch and updates its own website.
- A third-party directory still shows the old hours, and a review mentions the branch being closed.
- An answer engine that draws on several of these places may present one version, another version, or a blend of both.
This example is illustrative. The article does not measure how often such conflicts occur or how any particular system resolves them.
Where the failure usually starts
The failure is rarely a single bad edit. It usually begins with unclear ownership: nobody is certain which record is authoritative, so a change made in one system never reaches the others. That is why the public-data side of the argument is as much about data plumbing as about brand messaging.
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The internal direction: customer feedback as enterprise AI material
The second direction runs inward. The BrandPost presents customer comments and feedback as potentially useful material for enterprise AI, particularly when they are connected with operational context. A comment that says “the line was too long” is a sentiment signal. The same comment linked to a specific location, a product line, a time window, and a transaction type becomes something an operations or product team can act on.
That linkage is where the technology side of the organization becomes essential. Connecting feedback to context requires consistent identifiers across the CRM, point-of-sale, survey, and review systems, and it requires data structures that let a model distinguish one branch from another. Without that work, an enterprise model trained or prompted on feedback may summarize sentiment accurately but attach it to the wrong place or time.
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Three questions internal AI raises
- Provenance: Where did each piece of feedback come from, and was it collected with the customer’s permission for this use?
- Linkage: Which location, product, transaction, and time does each comment belong to, and how confident is that link?
- Access: Who may see the underlying feedback, and who may see the model outputs built from it?
The BrandPost identifies these as open questions for enterprise use. They are not questions the source answers with a specific control or product feature.
Where marketing and technology responsibilities meet
The BrandPost’s most useful point for organizational design is that neither function can own the whole problem. It describes complementary roles rather than a handoff:
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| Area | Marketing contributes | Technology contributes |
|---|---|---|
| Public signals | Knowledge of which reviews, listings, and profiles shape how customers see the brand | Mapping where each public record lives and which system is authoritative |
| Customer perception | Interpretation of what feedback means for brand and customer experience | Structuring feedback so it can be joined to location, product, transaction, and time |
| Integration | Priorities for which public records must be accurate first | Connectors and update workflows that propagate changes across platforms |
| Security and access | Input on which customer-derived data is appropriate for which audience | Role-based controls over feedback data and model outputs |
| Governance | Brand standards for how the company is represented publicly | Audit trails, data retention rules, and documentation of how outputs are produced |
The BrandPost explicitly argues for shared attention rather than moving reputation ownership from marketing to IT. A practical reading is that marketing sets what accurate and on-brand means, and technology makes that standard enforceable across systems.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A joint review for CMOs and CIOs
A joint review can start from the data rather than from the org chart. The sequence below is one workable approach, built from the BrandPost’s operational dimensions.
- Inventory every public record that describes a location or product: listings, review profiles, maps entries, and the company’s own location pages.
- Name one authoritative owner for each record type and document which system holds the master copy.
- Test consistency by comparing hours, services, and addresses across platforms for a sample of locations, including the multi-location sites most likely to drift.
- Trace how an update made in the master system reaches each public platform, how long that takes, and what happens when a platform rejects or ignores the change.
- For internal feedback, confirm that every record carries provenance, location and time context, and a traceable link from any generated conclusion back to the source comments.
- Set access rules for both the raw feedback and the model outputs, and review them whenever a new team or tool is given access.
Public-data readiness checks
| Dimension | Question to answer | Warning sign |
|---|---|---|
| Accuracy | Does each public record match the company’s current facts? | Hours or services differ from the master record |
| Freshness | How quickly do changes appear after they are made? | Updates from the last change cycle are still missing |
| Consistency across platforms | Do the same facts appear on every platform where the business is listed? | One location shows different addresses on two directories |
| Authoritative ownership | Is there one named owner and one master source for each record type? | Two teams edit the same listing independently |
| Reliable propagation | Does an update reach every platform, and is failure detected? | No one knows which platforms rejected an update |
Internal-feedback readiness checks
- Contextual linkage: each comment is tied to a location, product, transaction, or time, with a known confidence level.
- Provenance: the collection channel and permission basis are recorded alongside the feedback.
- Access controls: raw feedback and model outputs have separate, reviewed permission sets.
- Traceability: any conclusion a model produces can be traced to the specific source comments behind it.
What the argument does and does not establish
The BrandPost is sponsored content by Reputation, a vendor of enterprise reputation-management software, and it was written by that company’s CMO. It is an executive argument supported by practical examples. It is not independent research, documentation of how any AI system works, or a measurement of outcomes.
- It does not quantify how AI search or answer engines weigh reviews, listings, or location details, and it does not establish that all systems use the same signals.
- It does not provide a ranking formula or a guaranteed visibility effect from cleaning up public records.
- It contains no attributable statistics about AI recommendations, reputation data, or customer-feedback results.
What the argument does support is the organizational point: public records and internal feedback draw on the same data, so the teams that govern that data need to work from one shared set of questions about accuracy, ownership, provenance, and access.
As Melani writes in the BrandPost, “The data doesn’t respect the org chart.”
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