Key findings
- Among 602 full-time US marketing and PR professionals, 73% had invested in monitoring tools, while 59% could not confidently turn the data into action.
- Most were not analyzing competitor share of voice, sentiment, AI bot traffic or media sources surfaced by AI systems.
- Geolix.ai found two cited 404 pages without a complete owner-to-retest trail. One had 104 citations and received 26 more on August 5.
- A minimum GEO operating system connects the question library, evidence, diagnosis, action, approval and retest.
What the survey shows
Scrunch and Scribewise surveyed 602 US adults employed full time in marketing or PR from May 19 to June 2, 2026. The reported margin of error is plus or minus four points at 95% confidence. Because a GEO platform and an agency published the report, its recommendations are not neutral market consensus, and the sample does not represent every fintech team.
| Survey signal | Reported result | Operational meaning |
|---|---|---|
| Investment in monitoring tools | 73% had invested | Tool access is already common in this US marketing and PR sample. |
| Ability to use the data | 59% could not confidently translate data into action | Interpretation and task design lag behind collection. |
| Competitor share of voice | 71% were not analyzing it | Teams may see their own visibility without a market benchmark. |
| Brand sentiment | 70% were not monitoring it | A presence score can hide inaccurate or harmful framing. |
| AI bot traffic | 67% were not analyzing it | Machine retrieval is disconnected from website analytics. |
| Media sources surfaced by AI | 60% were not analyzing them | Teams miss the external evidence shaping answers. |
Sources: Scrunch and Scribewise 2026 AI search survey
The survey also reports that 58% were not updating existing content, 63% were not mapping content or campaigns to audience prompts, and 70% were not using prompt data in editorial calendars. Among GEO service respondents, 71% spent more time explaining AI search and managing expectations than executing. These are self-reported practices, but they show that a visibility number can arrive before a shared decision process.
Why monitoring stalls
No owner for the full problem
AI visibility crosses functions: communications owns media sources, content owns pages, engineering owns crawlability, product owns feature facts, compliance owns regulated claims and regional teams know local channels. A dashboard can identify a problem that no single team has authority to resolve.
The metric is broader than the decision
A global visibility score compresses different states. Missing citations, low recommendation rank, incorrect fees, negative framing and a cited dead page require different evidence, owners and risk treatment. A generic recommendation leaves the team to reconstruct the diagnosis.
Execution capacity was not budgeted
Some buyers fund software but not the content, technical, public relations or governance work that follows. The tool may work as designed while the organization has no approved way to change a page, engage an external source or correct a product fact.
Measurement ends before the business result
A task can be published without a comparable retest, and a visibility change can be reported without a conversion event. Both break the evidence chain. Teams need the before state, implementation date, after state and limitations to learn which actions deserve repetition.
A first-party execution gap at Geolix.ai
Geolix.ai audited 46 URLs on its own website and found two pages returning 404 errors while AI systems continued to cite them. One removed article had accumulated 104 citations and received 26 more on August 5; another dead page received 11 citations. The first article had been moved on July 24, and its redirect pointed to a slug that did not exist.
The monitoring data contained the URLs, dates and citation counts. It did not contain a complete task record with priority, responsible owner, approval, implementation and retest. As of August 6, the repair was incomplete. This is an execution failure in Geolix.ai’s own process, not a customer success claim. It shows why an issue can remain live even when the diagnostic evidence is clear.
A second gap appeared in analytics. GA4 recorded identifiable AI referral sessions, but no key events were configured for consultation, registration or form submission. The company could compare visibility and traffic, but it could not connect either one to conversion. Monitoring and web analytics existed; the business measurement design did not.
Monitoring maturity can hide task immaturity
Geolix.ai’s production export covered seven accounts and 13 projects with 91,073 valid answers and 708,307 citation records. Scheduled collection, classification, aggregation and repeated prompt runs were already operating across multiple markets. The same database could not report the average number of recommended tasks, completion rate or task-level retest rate because it did not contain a task system.
A provider can show sophisticated evidence collection while managing execution in messages, spreadsheets and meetings. That may work at small scale, but ownership and learning are difficult to audit. The next stage of the market will connect monitoring records to operational records.
The minimum viable GEO operating system
| Object | Minimum required record | Failure prevented |
|---|---|---|
| 1 Question library | Prompt, intent, engine, language, market, account state, cadence and repetition. | Uncontrolled tests that cannot be compared over time. |
| 2 Evidence record | Full answer, cited URLs, timestamp, success status, mention, recommendation, position and factual flags. | Recommendations based on a score without inspectable evidence. |
| 3 Diagnosis | Exact failure state, affected buyer intent, likely cause, risk and confidence. | Treating every visibility loss as a content problem. |
| 4 Action ticket | Specific change, asset or source, owner, due date, dependencies and expected observable result. | Insights that remain unowned or too vague to execute. |
| 5 Approval record | Fact source, reviewer, decision, approved version and publication authority. | Uncontrolled changes to regulated or market-specific claims. |
| 6 Retest result | Same question definition, observation window, collection success and before-and-after outcome. | Claims of improvement that cannot be reproduced or bounded. |
A project management tool can store several objects, but evidence must remain attached to the task and implementation to the retest. Without those links, the organization returns to reporting observations rather than operating a learning cycle.
What this means for fintech teams
Fintech adds controls that generic content workflows often omit. Product and legal teams must verify the relevant entity, licence, market availability, eligibility, fee conditions and risk language. Technical teams may need to repair structured data, redirects or documentation access. Communications teams may need to work with external sources that the company does not control. Regional teams must judge whether the recommended source and wording fit the local engine and language.
A workable ownership model gives one GEO lead responsibility for the full evidence trail while keeping approval with the function that owns the fact. The GEO lead coordinates the question library, diagnosis, action queue and retest. Content, engineering, product, compliance and regional owners accept or reject tasks within their authority. Rejections should be recorded with reasons so the system learns which recommendations are infeasible or unsafe.
How to allocate tool and service budgets
There is no universal ratio. Teams with strong operators may spend more on software and integration; teams lacking prompt methodology, source analysis or execution need service and internal owner time. Regulated companies also need governance resources. Buyers should price the full path from evidence to retest, not only the licence.
Where Geolix.ai fits
Geolix.ai positions its work as diagnosis plus managed execution across Western and Chinese AI ecosystems. The monitoring layer collects repeated answers, classifies visibility states, aggregates sources and schedules retests. The service layer turns those records into prioritized content, technical, source and brand-fact work while preserving human review for regulated claims.
Its own 404 and analytics cases create a concrete product requirement: owner, approval, implementation and conversion records must become as systematic as answer collection. Publishing that limitation makes the market argument stronger because it distinguishes a documented operating gap from a generic claim that other teams do not know how to act.
Method and limitations
Survey results come from a Scrunch and Scribewise report updated August 6, 2026 and cover 602 full-time US marketing and PR professionals. Geolix.ai examples use its own July and August records. Neither source measures how common these gaps are across fintech, and no customer-level data is included.
Frequently asked questions
Who should own GEO?
One operator should own the evidence trail and cadence, while content, engineering, product, communications, compliance and regional teams approve and execute within their domains.
How should tool and service budgets be divided?
Fund software for repeatable collection, service or internal labor for execution, and governance for regulated review. A lower software price does not reduce the cost of missing owners or unimplemented work.
How long does it take to establish a closed loop?
A team can define questions, evidence fields, owners and approvals during setup. A credible closed loop exists only after a real action is implemented and retested comparably, so timing depends on publication, engineering and compliance lead times.