PR shapes public perception of AI by replacing abstract claims with named, verifiable uses, explaining how a system works and where it fails, and putting accountable people in front of journalists. The task is harder than it looks. A Pew Research Center survey conducted in June 2025 found that 50% of U.S. adults were more concerned than excited about AI, while 10% were more excited than concerned.
Last updated October 21, 2026.
What does the public currently think about AI?
The public is wary of AI in daily life even as it uses AI tools. Pew's survey, which polled U.S. adults from June 9 to 15, 2025, found that concern outweighs excitement five to one, which sets the starting point for any AI company's communications.
That gap changes the job. An AI company cannot assume goodwill, so every claim needs evidence attached and every message needs to answer a worry the audience already has. 5W's guide to ethical AI in PR covers the transparency standards behind that approach.
How should PR counter science-fiction stereotypes about AI?
PR counters stereotypes by showing named, real deployments in place of generic promises. Fear of AI draws on fiction, from HAL 9000 in 2001: A Space Odyssey to the machines of Terminator, and a concrete example of AI doing a bounded, useful job gives audiences something real to weigh against those images.
| Audience worry | What PR shows instead | Evidence to attach |
|---|---|---|
| AI will act on its own and escape control | A named deployment where people review and approve every output | A description of the human review step and who performs it |
| AI will replace workers | A named team that uses AI for one task and keeps its staff | The task, the tool, and the people who still own the decision |
| AI is a black box | A plain-language account of what data goes in and what comes out | A published description of inputs, outputs, and known limits |
Pick applications where the benefit is measurable and the audience can check it, such as an AI-assisted screening tool tested in a clinical study or a grid-optimization system run by a utility. Name the deployment, the measure, and the source.
How should an AI company explain what its product does?
An AI company should explain its product by defining terms once, in everyday words, and then describing what the system takes in, what it produces, and where a person steps in. Most audiences know AI only through broad labels such as "machine learning" and "deep learning," so a message built on those labels explains nothing.
Several formats carry that explanation. A press release that defines each technical term on first use, an interview with a named researcher who answers a reporter's skeptical question directly, an explainer video, and an infographic that follows one piece of data through the system all work. Each should answer the same three questions: what the system does, what it cannot do, and who is accountable when it is wrong.
What does transparency look like in practice for AI developers?
Transparency in practice means publishing documents the public can read and check: how the company collects and uses data, how the model was tested for bias, what its known limits are, and how a person can contest an automated result. A promise to be "responsible" without those documents gives skeptics nothing to verify.
Ethical guidelines for developers belong in the same place as the product information, written for a general reader and updated when the product changes. Discriminatory outputs and privacy breaches are the two concerns audiences raise most, so the guidelines should address both by name and describe the process used to catch them.
How should AI companies work with journalists?
AI companies should work with journalists by pitching specific, checkable stories and by preparing their researchers and engineers to explain those stories accurately. Reporters covering AI are asked to separate real capability from marketing, so the companies that earn balanced coverage supply evidence on request.
| What a reporter needs | What the company supplies |
|---|---|
| A claim they can verify | Documentation, test results, and a named person who stands behind them |
| A source who can explain it plainly | A researcher or engineer who has completed media training |
| The other side of the story | A direct statement on known risks, limits, and open questions |
Build relationships before a launch, not during one. Regular story pitches and expert commentary on AI news position a company as a reliable source, which is the foundation of the work described in 5W's guide to media relations.
Which stakeholders need their own AI messages?
Four groups need separate messages: the general public, policymakers and regulators, industry customers, and advocacy organizations. Each starts from a different concern, and a single statement written for all four usually answers none of them.
| Audience | Main concern | Channel that reaches it |
|---|---|---|
| General public | Privacy, jobs, and whether the product can be trusted | News coverage, explainers, and social video |
| Policymakers and regulators | Safety, bias, and accountability | Briefings, public comment filings, and expert testimony |
| Industry customers | Reliability, data handling, and return on investment | Trade media, case studies, and direct account contact |
| Advocacy groups | Harm to specific communities | Direct meetings and published responses to their findings |
The same facts should appear in every message. Only the emphasis and the evidence change.
What belongs in an AI company's crisis plan?
An AI company's crisis plan should name the incidents most likely to hit it, such as a biased or harmful output, a data privacy breach, or a model failure that affects customers, and assign a spokesperson and an approval path for each. A swift, specific response limits the damage when concern becomes news.
The plan should also state when the company will pause a feature, how it will notify affected users, and who will explain what happened in technical terms. The crisis PR guide sets out the response sequence, including the section on B2B tech companies.
PR and AI perception FAQ
Why does public perception matter for an AI company?
Customers, regulators, and employees all decide whether to trust a product partly on what they have read about it, and skepticism is widespread, with 50% of U.S. adults in Pew's 2025 survey more concerned than excited.
What is the best way to counter fears about AI?
Show a named, real deployment with a measurable result and a visible human review step, and publish the evidence.
Should an AI company discuss its product's limits publicly?
Yes. A stated limit gives journalists and customers a reason to believe the rest of the claims.
How is this different from using AI inside a PR team?
This guide covers communicating about AI products. For how communications teams use AI in their own work, see how AI is transforming corporate communications.
What should an AI company do first?
An AI company should write down what its product does and cannot do in plain words, publish the evidence behind its claims, and prepare a named spokesperson for the hardest question a reporter is likely to ask. 5W's AI, digital marketing, and PR practice builds communications programs for AI and technology companies.
