Hiring a Reputation Management Expert – What to Look For 

If you’re hiring a reputation management expert in 2026, you’re not hiring someone to “tidy Google”. You’re hiring someone to protect trust across search results, reviews, news coverage, and increasingly, AI-generated summaries. 

That last bit matters. Google’s AI Overviews have already been pulled back for some health-related searches after reporting and expert criticism about inaccurate outputs. Even when the AI answer isn’t outright wrong, it can be incomplete, missing context, or quoting shaky sources. That’s reputation risk, because people often make up their mind before clicking anything. 

Google has also been clear that AI features like AI Overviews and AI Mode are part of Search, and it has published guidance for site owners about how inclusion works and what “good” looks like in this new environment. 

So here’s how I’d hire well, and avoid expensive mistakes. 

What a real reputation management expert does in 2026 

The strongest operators don’t start with “push this down”. They start with diagnosis and fundamentals: 

• Find where the story lives: search results, news, review platforms, social, business listings, knowledge panels, AI answers. 

• Fix accuracy at source: corrections, platform processes, policy-led removals where justified, and tightening public-facing facts so misinformation has less room to spread. 

• Build an authority footprint: credible coverage, consistent entity information, clear authorship and credentials, and content that’s genuinely useful, not filler. 

• Put monitoring and response into a system: escalation paths, response templates, stakeholder comms, reporting that a board can actually read. 

This aligns with how Google describes “helpful, reliable, people-first” content and the wider E-E-A-T thinking behind quality evaluation (experience, expertise, authoritativeness, trust). 

The hiring checklist (what to look for) 

They talk about AI visibility without claiming they can “control the model” 

You want someone who understands that AI answers tend to synthesise from multiple sources, and that the way you show up is influenced by what the web says about you, consistently, across credible places. 

Good signs: 

• They reference Google’s own AI guidance for site owners, and build a plan around improving clarity, originality, and reliability. 

• They talk about testing brand queries and tracking what sources AI answers cite, then strengthening those “trusted references” ethically. 
   
• They may mention GEO (Generative Engine Optimisation) as the emerging discipline around visibility in generative answer engines. There’s even academic work formalising it as a framework for improving visibility in generative engines. 

What you don’t want is anyone selling “AI manipulation” as if it’s a guaranteed lever. If the pitch sounds like gaming the system, it usually ends with platform action, reputational blowback, or both. 

Their review strategy is compliance-first, not volume-first 

Reviews are one of the fastest ways to damage trust if handled badly, because enforcement is getting tighter. 

In the UK, the CMA’s guidance tied to the DMCC Act covers fake reviews and concealed incentivised reviews as banned practices, and sets expectations for compliance. 

On Google specifically, “fake engagement” and incentivised content are prohibited, and violations can trigger restrictions on Business Profiles. 

If you operate in the US at all, or you’re a global brand, it’s also worth knowing the FTC has a final rule banning fake reviews and testimonials, including a focus on deterring AI-generated fake reviews. 

Good signs: 

• They build a review programme around service recovery, customer experience fixes, and legitimate review generation. 

• They train staff on responses, escalation, and what not to request. 

Bad signs: 

• Anything that sounds like “review drops”, bulk accounts, or incentives that aren’t properly handled. 

They understand entity reputation, not just rankings 

Reputation in search isn’t only ten blue links anymore. It’s knowledge panels, “People also ask”, business listings, and AI answers. 

Ask how they handle your entity data: 

• Consistency of brand name, leadership, addresses, phone numbers, and public facts across authoritative sites. 

• Knowledge panel hygiene. Google explains that knowledge panels update automatically and can be influenced via official entity feedback and user feedback processes. 
   

The best experts treat this like technical reputation infrastructure, not a one-off fix. 

They can show evidence, not just claims 

A serious agency should be able to show: 

• Before/after brand SERP screenshots (dated). 

• Reporting on query sets that matter to you. 

• Review metrics that aren’t vanity (response rate, sentiment trend, velocity, keyword themes). 

• A written methodology and audit trail. 

If everything is vague because it’s “proprietary”, you’re buying hope. 

They’re careful with Wikipedia, and they say so upfront 

Wikipedia is a reputational flashpoint. Editing with a conflict of interest is strongly discouraged, and paid editors are expected to disclose. 

This isn’t theoretical. There are still very public stories about PR activity around Wikipedia edits and the reputational fallout that follows. 

A good expert will either: 

• Avoid it unless there’s a clear, policy-compliant path, or 

• Use talk pages and disclosure properly, and keep expectations realistic. 

They have a crisis process, even if you’re “not in crisis” 

Reputation work goes wrong when something spikes and nobody knows who’s doing what. 

Look for: 

• A holding statement process. 

• Who signs off comms. 

• How legal and comms coordinate. 

• A plan for misinformation that appears in AI summaries or reposted snippets. 

This is where experience shows. 

Their plan improves your web presence, not just your search appearance 

Google’s guidance on succeeding in AI-powered search leans hard on creating unique, non-commodity content that actually satisfies people, because users are asking longer questions and digging deeper with follow-ups. 

So a modern reputation plan should include: 

• Clear “about” pages, leadership bios, editorial standards, and proof points. 

• Content that demonstrates lived experience and authority, with sources. 

• Third-party corroboration where appropriate. 

It’s unglamorous. It works. 

Interview questions that separate the good from the glossy 

Use these in your first call: 

1. “What would you do in week one?” 
   You want to hear audit, risk mapping, priority queries, and quick wins based on policy and facts. 

2. “What won’t you do?” 
   Listen for boundaries around reviews, Wikipedia, and anything that smells like fake engagement. 

3. “How do you measure progress if rankings don’t move, but AI answers do?” 
   A good answer includes tracking visibility across AI summaries, knowledge panel accuracy, and sentiment shifts in reviews. 

4. “Show me a case where you couldn’t remove something. What happened next?” 
   You’re hiring judgement, not just tools. 
    

What outcomes should look like after 90 days 

Not magic. Not perfection. Something like: 

• Your brand facts are consistent across major sources. 

• Review handling is disciplined and compliant. 

• Negative items are addressed through documented processes where justified, and where they can’t be removed, the overall footprint is stronger and more credible. 

• Monitoring and response is a routine, not panic. 
   

That’s what “reputation management” means now: clarity, credibility, and resilience across search and AI surfaces.