When people talk about AI in marketing, the conversation often jumps straight to replacement. Can it write the press release? Can it build the campaign? Can it replace an agency or an entire team?
That is not how I think about it.
When I think about the real promise of AI, I think about the house robot we all wish we had: the one that vacuums the floor, folds the laundry, waters the plants and takes care of the repetitive jobs that somehow consume a disproportionate amount of the day.
Digital PR has its own version of that housework. We scan the news every morning. We search for journalists. We check whether an angle has already been covered. We verify claims, review spreadsheets, look for missed links and work through long lists of coverage. These tasks matter, but many of them are time-consuming rather than creatively demanding.
This is where AI and custom GPTs can genuinely improve a Digital PR strategy. They should not replace the strategist, the expert or the journalist relationship. They should do more of the vacuuming so the people in the team have more time to think.
AI should improve efficiency, not replace human intelligence
I approached this topic from both sides of the media relationship. Before running Cedarwood Digital, I qualified as a journalist and worked across broadcast and print, including in some extremely busy Australian newsrooms. I have written stories, received press releases and seen first-hand what happens when a pitch is irrelevant, overstated or sent without understanding how a newsroom actually works.
That experience makes me fairly sceptical of the idea that more automation automatically creates better PR. It does not. Using AI to produce hundreds of generic releases or blast the same pitch to a huge media list simply helps a team do the wrong thing faster.
AI should not be used to fabricate data, fake expertise or remove oversight from regulated claims. It cannot replace the instinct that tells an experienced PR professional whether an angle is genuinely interesting. It cannot build a trusted relationship with a journalist on your behalf and it should never become an excuse to send low-quality outreach at scale.
The opportunity lies in decision support. A well-designed custom GPT can organise evidence, identify risks, surface relevant opportunities and turn an overwhelming dataset into something a skilled person can act on. The human remains accountable for the judgement and the final output.
Custom GPT 1: Put a compliance and risk gate before publication
When most people discuss AI in Digital PR, they focus on speed. For teams working in finance, insurance, health, legal services, gambling or other regulated sectors, I think one of the most valuable uses is actually risk reduction.
A Compliance and Risk GPT acts as an extra pre-publication check. It asks whether a claim can be verified, whether the language overpromises, whether the content strays into regulated advice and whether a campaign could create reputational risk or what I call trust debt.
The GPT needs to be built deliberately. Start by defining the organisation’s risk appetite, then create a policy pack containing sector rules, prohibited or flagged language, claim-qualification requirements, evidence standards and brand constraints. The input should also be standardised: client, sector, claims, sources and draft copy. The output should be equally structured, with a risk score, a pass/revise/block recommendation, specific concerns, evidence requirements and safer wording.
Consider an insurance comparison campaign claiming that drivers could save up to £500 a year by switching provider. On the surface, the statistic sounds compelling. But is £500 the average saving, the maximum observed saving or a possible outcome for a particular customer profile? What period does the data cover? How many quotes were analysed? Does the sentence imply that most drivers will save that amount?
A trained GPT can flag those questions before the release reaches a client’s compliance team. It might recommend changing ‘switching could easily save motorists up to £500’ to: ‘Our analysis shows that some drivers could save as much as £500 by switching providers, although the amount varies according to individual circumstances.’ It can also prompt the team to disclose that the finding came from, for example, 50,000 UK quotes analysed between January and March 2026.
This does not replace formal compliance approval. It creates a more considered first draft, reduces avoidable back-and-forth and helps build confidence with clients because the agency is demonstrating that it understands both the story and the risk.
Custom GPT 2: Make reactive PR less dependent on manual monitoring
Anyone who has run a reactive newsroom knows how much time can disappear into scanning publications, social platforms, trend tools and forward calendars. The goal is to spot the right opportunity early, but a person can only read so much in a morning.
A Newsjacking Scout GPT can provide an additional layer of monitoring. When connected to appropriate browsing or source feeds, it can review current stories against a client’s topics, experts, locations and restrictions. Instead of returning a generic list of headlines, it can provide the source, a short summary, an explanation of why the story is relevant and a suggested expert-comment hook.
The quality depends heavily on the rules. Define the countries and publications to monitor, the date range, the number of outputs, excluded subjects and the client’s preferred sentiment. Add a clear topic universe and tell it what counts as a genuine connection rather than a forced one. It should avoid duplicated stories and distinguish between an immediate reactive opportunity and an upcoming event that needs preparation.
For an exterior-paint brand, that could mean identifying a live story about weather damage and suggesting an expert angle on protecting external surfaces. For a mattress retailer, it might identify a forthcoming clock change, heatwave or major sporting event and connect it to useful commentary about sleep. The GPT does not write the final opinion or decide whether the hook is strong enough. It gives the team a faster, broader view of where demand may already exist.
Used well, it can improve opportunity discovery and story relevance while reducing the hours spent checking the same sources. Used badly, it simply creates another noisy feed. Human filtering is what turns a surfaced headline into a credible pitch.
Custom GPT 3: Find the coverage your monitoring tools miss
Finding coverage sounds straightforward until a brand name has several variations, a journalist mentions a spokesperson rather than the company, a publisher removes the link or an article uses the campaign data without the expected wording.
A Coverage Scanner GPT can complement traditional monitoring by looking for a wider set of signals. Give it the brand name and common variations, product names, abbreviations and misspellings. Add spokesperson names, the client’s keyword and topic universe, and rules for identifying followed links, nofollow links, unlinked mentions, citations and quoted commentary. It can also cross-reference the publications and journalists that received the original outreach.
The output should be practical: publication, URL, date, type of mention, link status, spokesperson or data cited, and a short coverage summary. This gives teams a cleaner view of what has landed and where follow-up may be worthwhile—for example, requesting attribution when research has been used without a link.
It will not necessarily produce a complete coverage record, and it should not be presented as one. Its value is in finding additional evidence with relatively little extra manual resource. It can also reveal patterns: which angles are travelling furthest, which experts are quoted repeatedly and where negative or inaccurate coverage may need attention.
Custom GPT 4: Turn a basic link gap into an authority-gap strategy
Traditional link-gap tools are useful, but their starting point is usually simple: which domains link to competitors but not to us? In competitive and YMYL sectors, that is rarely enough. A long list of domains ranked by authority metric does not tell you which gaps matter, why competitors earned the links or what kind of story could close them.
A Link Gap Analyser GPT can combine backlink exports from platforms such as Ahrefs or Semrush with coverage data, article context and brand mentions. It can then classify opportunities by niche, topic cluster, publication type and journalist beat. Instead of treating every missing link equally, it can score opportunities using topical relevance, authority, editorial style, competitor presence and the likelihood that the publication would cover a credible angle from the client.
Imagine a finance brand with a large gap in technology publications, finance-and-lending sites and business media. The important insight is not merely that competitors have more links. The GPT can analyse whether those links were earned through fintech research, consumer commentary, financial-literacy resources or digital-transformation stories. That context turns a spreadsheet into a strategy.
It also helps teams distinguish visibility gaps from authority gaps. A national news link may generate reach, while a citation from a specialist lending publication may do more to reinforce what the brand is trusted for. The strongest Digital PR plans need both, but the balance should be intentional rather than dictated by raw domain metrics.
The result is a prioritised map of niches, missed publications, relevant journalists and potential story formats. The GPT accelerates the analysis; the team still decides which opportunities align with the client’s expertise and deserve investment.
Custom GPT 5: Build media lists around how journalists actually write
Even an excellent story can fail when it reaches the wrong reporter. Yet media lists are still often built through job titles, broad beats and previous contact databases. The result is predictable: too many journalists receive pitches that sit outside what they genuinely cover.
A Journalist and Outlet Intelligence GPT changes the starting point from ‘who is labelled as relevant?’ to ‘who has demonstrated an interest in this type of story?’
The workflow begins by collecting articles from journalist author pages, publisher archives, search discovery and RSS feeds. Store the headline, URL, date, publication, article content and category in a structured dataset. The GPT can then identify recurring topics, framing, sentiment, evidence preferences and story formats. It can build a profile for each journalist and match a new campaign to the reporters whose recent work suggests genuine relevance.
For example, two journalists may both sit on a personal-finance desk, but one regularly covers household bills using consumer data while the other focuses on policy and regulation. Sending them the same angle with the same introduction ignores the evidence already available about what they value.
The output can include the journalist, outlet, relevance score and a plain-English explanation of the match. This creates a stronger foundation for personalisation, but it should not become automated imitation. The aim is to understand the journalist’s interests and respect their time—not mimic their voice or pretend there is a relationship that does not exist.
The quality of the GPT depends on the quality of the system around it
None of these GPTs should be treated as a one-off prompt. The useful work sits in the system around them: clear instructions, reliable inputs, approved documents, defined outputs, examples of good and bad decisions, and a feedback loop.
They also need ongoing checking. News changes. Journalists move publications and switch beats. Regulation evolves. A client changes its risk appetite or messaging. Coverage-detection rules need refining. A model can produce a confident answer that is incomplete or wrong, so sources must remain visible and important claims must be verified.
Over time, the workflow improves because the team learns where the GPT adds value and where human review needs to be strongest. The objective is not full automation. It is consistent assistance at the repetitive points where people are most likely to lose time or overlook something.
The future of Digital PR is still human
The future of Digital PR is not AI-driven creativity. It is AI removing enough busywork for humans to build the stories that actually earn coverage.
A custom GPT can scan more sources than one person can read over breakfast. It can highlight an unsupported claim, cluster thousands of links or summarise a journalist’s recent coverage. But it cannot sit with a client and uncover the experience that makes their expert genuinely interesting. It cannot recognise every cultural nuance, take responsibility for a regulated statement or build mutual trust with a journalist over time.
The best use of AI therefore feels less dramatic than the replacement headlines suggest. It is a second pair of eyes. A research assistant. A monitoring layer. A way to turn large, messy inputs into a more useful starting point.
Let the GPT do more of the vacuuming. Let the Digital PR team do the thinking, questioning, relationship-building and storytelling. That is where the real competitive advantage still sits.


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