How PR and GR agencies are adopting AI
How PR and GR agencies adopt AI: the short answer
How PR agencies adopt AI follows a clear pattern, and it helps to name it up front. Firms adopt AI in three waves: productivity tooling first, monitoring and intelligence second, and answer-engine visibility last and slowest. The early waves save time on repeatable work. The last wave creates a genuinely new deliverable, and that is where most firms stall.
Here is the core tension. Research, drafting and listening are being automated quickly. But the strategic, high-value work — managing how AI systems describe a client — is where most firms still lack a delivery capability. Knowing how to use a chatbot is not the same as being able to change what one says about your client.
This piece is written for principals and account leads at communications and government-relations firms who are deciding what to build in-house and what to partner for. The question is no longer whether to adopt AI. It is which parts to own.
For the hardest part (making sure answer engines describe a client correctly) Morris McLane operates as the digital execution layer behind comms and GR firms, delivering AI-search visibility built for communications and GR teams under your brand.
Why PR and GR firms are adopting AI now
The pressure is coming from the demand side. Audiences, journalists, and increasingly policymakers’ staff now use ChatGPT, Gemini and Google AI Overviews to research organisations and issues. The first thing many people read about a client is no longer an article. It is a synthesised answer.
That has changed what clients ask for. The question is now “what does ChatGPT say about us?”, and a traditional clippings report does not answer it. Agencies that cannot respond look a step behind.
There is also a margin and capacity story. Research-heavy, repeatable work has always eaten hours. AI promises leverage on exactly that kind of task, which is why adoption has moved fast in the back office.
Industry bodies have noticed. The Chartered Institute of Public Relations has published extensively on AI adoption and ethics in the profession, and trade titles such as PRWeek track how agency practice is shifting across PR and public affairs.
Where agencies are using AI well today
The reliable uses cluster in three areas, and they share a pattern worth naming at the end.
Research and intelligence
AI is strong at briefing prep, background synthesis, and stakeholder and issue mapping. A junior team can pull together a credible first picture of a sector, an opponent or a coalition far faster than before. The output still needs checking, but the starting point is better.
Drafting and first-pass content
Releases, briefing notes and messaging variants now begin as machine drafts. The skilled work moves to editing, sharpening and signing off. Human editorial control stays firmly in the loop, because a confident first draft is not the same as an accurate one.
Monitoring and information environment analysis
Listening has shifted from periodic sweeps to always-on coverage across news, social and search. Teams can watch how a story moves rather than catching up after the fact.
The pattern across all three is consistent. AI accelerates the inputs. Judgement, relationships and accountability stay human. This is also the through-line in how AI is changing PR and communications more broadly.
Where AI adoption stalls in PR and GR agencies
The wall most firms hit is a capability gap. Almost any team can use AI tools. Far fewer can deliver AI-search visibility work, because it is technical, ongoing and sits outside the classic comms skill set.
There is a measurement gap too. Reach and impressions do not tell you whether answer engines describe a client accurately. A campaign can earn coverage while ChatGPT still gets the basic facts wrong.
Risk and governance add another barrier. Hallucinations, unverified facts and disclosure questions all require a verification discipline that many teams have not yet formalised. Confident inaccuracy is the failure mode, and it is easy to ship.
Then comes the build-versus-partner decision. Standing up a generative-engine-optimisation function in-house is slow and specialist. Faced with that, many firms quietly under-deliver rather than admit the gap, which is the worst of both outcomes.
The new deliverable clients are asking for: AI-answer visibility
The shift is from “what did the media say” to “what do the machines say”. The asset is no longer a single placement. It is the source set that engines read across when they answer a question about your client.
That makes this a distinct discipline. Generative engine optimisation (GEO) and answer engine optimisation (AEO) draw on technical SEO, structured data and source-layer work — not media relations. If you want the detail, see what generative engine optimisation actually involves and the difference between SEO and GEO.
It is also among the most valuable, least commoditised work an agency can offer. Anyone can run a chatbot. Reliably changing what one says about a client is rare. It is also one of the hardest roles to staff, which is precisely why it tends to command a premium.
How this is executed digitally: the digital execution layer
This is the part that turns a good explanation into a delivered result. Here is how Morris McLane actually runs the work underneath an agency, grounded in our AI-search visibility service.
It starts with a baseline. We audit how ChatGPT, Gemini, Perplexity and Google AI Overviews currently describe the client, recording the exact prompts and the exact answers. That gives everyone a measurable starting point rather than an impression. Google has itself explained how AI Overviews synthesise information from across the web, which is why the source set matters so much.
Next comes source-layer work. We strengthen owned content, improve reference-source accuracy and add structured data so engines synthesise the client correctly. This is the technical, unglamorous work that actually moves answers. The practical playbook sits in how to get a client mentioned accurately in ChatGPT.
Then we keep watching. Always-on monitoring of the information environment runs alongside a re-run of the prompt set, so the team can see how the answers change over time rather than guessing.
When an engine gets something wrong, we respond promptly, correcting the underlying sources and, where useful, amplifying authoritative ones through paid and owned channels.
The partner model is simple. The agency keeps the client relationship, the strategy and the message. Morris McLane runs the digital mechanics underneath. We are the execution layer comms and GR firms plug into, rather than a function they have to rebuild from scratch.
A practical adoption roadmap for agencies
For firms deciding what to do next, a measured sequence works better than a rush.
- Audit current AI tool use and information-governance gaps, so you know what is already happening across teams.
- Standardise a human-in-the-loop verification routine, applied at speed but never skipped.
- Baseline how engines describe your priority clients, documenting prompts and answers.
- Decide build-versus-partner for AI-search visibility, honestly, against whether you can staff and sustain it.
- Add AI-answer accuracy to client reporting, so the new deliverable is visible and measurable.
Keep the work structured and scaled to the matter. The aim is a routine your teams can actually sustain, not a one-off project, and a clear view of where partnering for execution beats building from zero.
The bottom line
Agencies adopt AI fastest where it saves time and slowest where it creates new technical deliverables. The first part is nearly settled. The second is wide open.
The competitive edge in 2026 is owning AI-answer visibility for clients — whether you build it or partner for it. The firms that name this as a deliverable, and can actually deliver it, will pull ahead of those still filing clippings reports.
The short version
PR and GR agencies adopt AI in waves: productivity tooling first, monitoring second, and answer-engine visibility last and hardest. The first waves save time; the last creates a new, high-value deliverable that few firms can staff. If you want that capability delivered under your brand without rebuilding it from scratch, see AI-search visibility built for communications and GR teams.
Frequently asked questions
How are PR agencies adopting AI?
Most PR agencies adopt AI in waves: first for research and drafting, then for monitoring and information-environment analysis, and last for managing how answer engines describe their clients. The early uses save time on repeatable work, while the later, higher-value work (AI-search visibility) requires technical capability that many firms still lack. The mandate of building trust stays human; AI accelerates the inputs.
Will AI replace PR and public affairs professionals?
No. AI automates parts of research, drafting and monitoring, but judgement, relationships, crisis decision-making and accountability remain human. The practitioners who benefit are those who use AI to work at speed and then apply editorial, ethical and legal oversight to what it produces.
What can PR agencies actually use AI for today?
Reliable uses include briefing and background synthesis, stakeholder and issue mapping, first-draft content and messaging variants, and always-on monitoring across news, social and search. Each works best with a human-in-the-loop verification step, because confident inaccuracy is the main risk. AI changes the speed and scale of these tasks, not the need for editorial control.
What is AI-search visibility and why does it matter for agencies?
AI-search visibility is the practice of making sure answer engines such as ChatGPT, Gemini and Google's AI Overviews describe an organisation accurately and authoritatively when asked. It matters because audiences, journalists and policymakers' staff increasingly research through these systems rather than a list of links. It is a distinct discipline — generative engine optimisation — that draws on structured data and source-layer work rather than media relations.
Should an agency build AI capability in-house or partner for it?
Productivity tooling and a verification routine are sensible to build in-house. AI-search visibility is harder: it is technical, ongoing and specialist, so many firms partner for execution while keeping the client relationship, strategy and message. The deciding factor is whether you can staff and sustain the work to the standard clients expect.
How do you measure AI's impact on PR and GR work?
Reach and impressions no longer capture the full picture. A growing measure is whether answer engines describe a client accurately and favourably when asked. Teams record a baseline of documented prompts across engines, improve the underlying sources, then re-run the prompts to track how the answers change over time.
What are the main risks of using AI in communications?
The main risks are confident inaccuracy, unverified facts reaching the public, and over-reliance on automation for sensitive or contested matters. Synthetic content also raises disclosure and ethics questions. The safeguard is to keep human verification, message discipline and legal review in the loop before anything ships.
