How AI Is Changing PR and Communications
How AI is changing PR: the short version
How AI is changing PR comes down to one shift: the work is moving from pitching journalists for placements toward managing the whole information environment that shapes what people, and now machines, say about you. The pitch and the clipping still matter. But they are no longer the unit of work.
The new unit is the source set. Answer engines such as ChatGPT, Gemini and Google AI Overviews read across many sources at once and synthesise an answer. Your audiences read those answers. So does the next model.
That changes the job. Instead of chasing one article at a time, communications teams now shape the standing body of evidence that both humans and machines draw on. A placement still counts, partly because of who reads it, and partly because of how it feeds the AI answers downstream.
This is where Morris McLane operates as the digital execution layer: turning this shift into strategic communications built for the information environment for comms and government-relations teams who need it done, not just described.
From pitching journalists to managing the information environment
The old model was relationships, pitches, placements and clippings. You knew the right reporters, you made the case, you measured the coverage. It worked because a handful of outlets shaped what most people believed.
The new model is different. A single placement now matters partly for its direct readers, and partly because engines treat it as a source when someone asks about you later. One authoritative article can inform thousands of machine answers over time.
We call the discipline that replaces pure media relations information environment management. It treats the entire source set, owned content, third-party coverage and reference material, as the asset to manage, rather than any one story.
The skills carry over. The frame is wider. You are no longer only earning attention; you are shaping the sources that both people and models read.
Where AI now sits in the PR workflow
AI has moved into three parts of the workflow, each with clear limits.
Research and intelligence
AI speeds up information-environment analysis. It can monitor across channels, surface emerging narratives, and map how a topic is being framed before it hardens into consensus. The work that once took extensive reading can be drafted quickly, then checked by a human.
Drafting and iteration
AI accelerates first drafts, message testing and localisation. It is useful for variations and speed. Human judgement still governs facts, nuance, tone and legal exposure, because a confident draft can still be a wrong one.
Measurement
Measurement is moving beyond reach and advertising value equivalent. The newer question is whether answer engines describe you accurately when asked, and whether that picture is improving.
The limits
The risks are real: hallucination, confident inaccuracy and over-reliance on automation. Nothing should ship without verification. AI drafts faster; it does not decide what is true.
Answer engines are the new front page
People increasingly ask an assistant instead of reading a homepage or a single article. They type a question into ChatGPT, Gemini or Google AI Overviews and accept the synthesised reply. The Reuters Institute Digital News Report tracks this move toward AI-mediated discovery, and Pew Research Centre documents how quickly public use of these tools is growing.
That makes the answer engine the new front page. The practices that make your organisation legible to it have names: generative engine optimisation (GEO) and answer engine optimisation (AEO). Both aim at the same outcome, an accurate, well-sourced answer when a model speaks about you.
How LLMs decide what to say about your organisation
Models do not read a profile you control. They synthesise from training knowledge and live retrieval, weighing which sources look credible. We explain the mechanics in how LLMs decide what to say about brands.
When the engine gets it wrong
Sometimes the answer is confidently inaccurate. Usually that traces back to thin, dated or contradictory sources. The durable fix is reference-source accuracy and clear structured data, which is the subject of what to do when ChatGPT gets your company wrong. OpenAI’s own announcement of ChatGPT search confirms these systems now retrieve and synthesise live sources, so the sources you can influence shape the reply.
What stays the same in PR
For all the change, the mandate has not moved. Trust, credibility and relationships still decide outcomes. AI changes the medium, not the mission.
Authoritative third-party coverage and a coherent owned narrative remain the inputs that both humans and models reward. Earn credible sources and keep your own story consistent, and you serve both audiences at once.
Crisis judgement, message discipline and stakeholder relationships stay irreducibly human. A model can summarise; it cannot decide what your organisation should stand for under pressure.
Ethics matter more, not less. As generation gets cheaper, disclosure, accuracy and avoiding synthetic manipulation become the line that separates credible communications from noise.
How AI is changing crisis and high-stakes communications
In a crisis, AI compresses the window to respond. Narratives spread faster, and AI-summarised coverage can lock in a framing before you have spoken. The answer is to act promptly and at speed, with a structured response scaled to the matter.
Monitoring now has to span engines and platforms, not just headlines. A story can be mischaracterised inside a machine summary while the original coverage stays fairly neutral, and you need to catch that early. This is the difference between proactive versus reactive crisis communications, and it sits at the centre of our crisis communications work.
The stakes rise in litigation and contested matters. AI summaries can amplify one-sided framing, repeating the loudest version of events as if it were settled. Managing the source set is part of keeping the record fair.
What PR and communications teams should do now
The shift is manageable if you treat AI visibility as a channel you run, not a thing that happens to you.
First, audit how answer engines currently describe your organisation. Capture a baseline of documented prompts and the exact answers across ChatGPT, Gemini and Google AI Overviews. You cannot manage what you have not measured.
Second, strengthen the inputs. Improve owned content, reference-source accuracy and structured data so engines synthesise you correctly. This is the slow, compounding work that pays off across every future answer.
Third, build the measurement loop. Re-run the prompts, compare against your baseline, and track how the picture moves. Our guide to how to rank in AI search sets out that routine in practice.
Fourth, decide what to automate and what to keep human. Pair AI speed with editorial and legal oversight. The teams that win use AI to move faster, then apply human judgement to what it produces.
How Morris McLane executes this
Most of the above is advice. This is the part we actually run as a digital execution layer for comms and government-relations teams.
We start by mapping the information environment: monitoring across search, social and answer engines to see how a topic is framed and how ChatGPT, Gemini and Google AI Overviews currently describe the organisation. That baseline of documented prompts and answers becomes the thing we measure against.
Then we work the source layer. We strengthen owned content, fix reference-source accuracy and add structured data so engines synthesise the organisation correctly, and we shape the third-party coverage and citations those engines lean on. Where an answer is wrong, we trace it back to the thin or dated source behind it and correct at the root.
Finally we keep humans in the loop on anything sensitive: drafting and monitoring move at machine speed, while message discipline, editorial judgement and legal review stay with people. This is the operational spine of our strategic communications work.
The short version
AI is changing PR by moving the work from individual placements toward managing the information environment that shapes human and machine perception. The mandate, trust and credibility, stays the same; the medium and the measurement change. Audit how engines describe you, strengthen the sources they read, and run AI visibility as a managed channel with humans in the loop.
If you want this operationalised rather than just explained, that is what we do: strategic communications built for the information environment, supported by managing your visibility across AI answer engines.
Frequently asked questions
How is AI changing PR and communications?
AI is shifting PR from one-to-one media pitching toward managing the whole information environment that shapes perception. Tools accelerate research, drafting and monitoring, while answer engines such as ChatGPT and Gemini increasingly mediate what audiences learn about an organisation. The mandate of building trust and credibility stays the same; the medium and the measurement change.
Will AI replace PR 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 faster and then apply editorial, ethical and legal oversight to what it produces.
What is information environment management in PR?
It is the practice of shaping the standing body of sources, owned content and third-party coverage that both people and AI systems read about an organisation. Rather than chasing individual placements, it treats the entire source set as the asset, because answer engines synthesise from many sources at once.
How does AI affect media relations?
Media relations still matters, but a placement now also feeds the AI answers that summarise an organisation downstream. Coverage is valued not only for its direct readership but for how authoritatively it informs engines. Practitioners increasingly think about which sources models trust, alongside which journalists they know.
What are the risks of using AI in communications?
The main risks are confident inaccuracy (hallucination), 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.
How do you measure PR success in the age of AI?
Reach and impressions are no longer enough. A growing measure is whether answer engines describe your organisation accurately and favourably when asked. Teams capture 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 should communications teams do first to adapt to AI?
Start by auditing how ChatGPT, Gemini and Google AI Overviews currently describe your organisation, and record the exact prompts and answers as a baseline. Then strengthen owned content, reference-source accuracy and structured data so engines synthesise you correctly, and build a routine to re-check and measure progress.
