When AI rewrites your story: 5 reputational risks companies can’t ignore

Conversational AI has quickly become a natural tool inside many companies. It helps teams  refine presentations, draft difficult emails or structure messages more effectively. The same technology, however, has also entered newsrooms. More journalists, including business reporters, now use AI to edit texts received from companies, fill in missing information, translate technical conferences or generate context where none is readily available.

This shift has direct implications for corporate reputation. An apparently harmless paragraph, an ambiguous sentence or an unfair label can become raw material for algorithms. And at this point, the experience of a PR agency can make the difference between a message that travels as intended and one that is reshaped by technology.

In this environment, where humans and algorithms together shape public opinion, companies need clarity and a solid understanding of the risks. Below are five reputational challenges associated with the use of AI in the media, and several practical ways an organization can ensure it continues to be accurately understood through effective communication. In all these situations, the experience of a communication team is essential – not only to correct distortions, but also to prevent them and turn technology into an ally rather than an obstacle.

1.     Losing control of the context

The core risk is that unfair labels or hostile information may become a permanent part of the “context” that AI systems pick up and repeat.

Errare humanum est – to err is human, as our Latin teachers liked to remind us. Mistakes occur in both business and journalism. That is precisely why, for decades, we have had mechanisms for correction: errata, rights of reply, and direct dialogue between companies and newsrooms. These tools have been, and still are, effective ways to deal with incomplete or inaccurate information.

The change comes when content creators are no longer only reputable media outlets. An obscure blog, a partisan platform or a malicious article can generate material which, although lacking editorial credibility, circulates widely online and is then treated by AI systems as “useful context”. Models do not always distinguish between a professional article and a manipulative one; they use what they find and recombine reality based on those sources.

Anyone who has worked in communications has seen at least one case where an executive or a company was attacked online. In some situations, those negative pieces ended up dominating the first pages of Google results, and the process of rebuilding reputation was long and complex. In the past, you could address a specific article or speak directly to an editor. Today, the challenge is to prevent an unfair label from becoming part of how technology itself “understands” your company. Nuance is the first casualty.

This is where an experienced communication partner matters. Their role is not just to push out information, but to build dialogue, consistency, coherence and a unified tone over time. In a world where AI automatically fills in what is not explicitly stated, clear, well-structured and contextualized messaging becomes the best way to protect reputation. It is no longer about “communicating” in general, but about communicating intelligently so that algorithms have solid, accurate material to work with.

2.     The amplification of speculation and ownerless opinions

The risk here is that speculation may start to look like solid analysis and shape perceptions about a company, even though nobody has truly taken responsibility for those conclusions.

AI does not stop at filling gaps. It can generate opinions and interpretations that look very much like carefully researched analysis. Major publications – including in Romania – already ask AI models to explain markets, anticipate potential deals or summarize complex events. The speed is impressive: data gathered in seconds, organized in an apparently flawless logic, wrapped in a fluent, confident tone. To many readers, this looks like expertise.

AI tools are also used to translate technical conferences into accessible language, which in practice allows a very junior reporter to file a seemingly coherent story from, say, a National Bank press conference without a real grasp of macroeconomics. For pressured newsrooms, this is an efficient solution. But efficiency is not the same as rigor.

That is where the vulnerability lies: AI models do not distinguish between hypothesis and certainty and have no professional instinct for caution. A suggestion can turn into a “conclusion”, a question into a “diagnosis”, and a speculative scenario can enter the market as an informed opinion. This means that certain narratives about a company may form without an identifiable author, without clear intent and without proper verification.

This is why companies need a constant, credible leadership voice that offers clear reference points and reduces the space for misinterpretation. When the organization is in the public eye, its messages must be firm and easy to understand, leaving as few gaps as possible for technology to fill on its own. If you do not provide clarity, the tools will improvise for you.

3.     Error inflation through automated replication

In this case, the risk is that a simple factual mistake gradually hardens into a “reality” about the company, simply because it is repeated and replicated automatically.

Not every vulnerability stem from malice or deliberate attacks. Very often, a straightforward factual mistake in an article can trigger a domino effect. In the traditional media model, such errors had a limited life cycle: an editor would step in, a correction would be issued, or the piece would remain marginal. AI models fundamentally alter this dynamic.

If an inaccurate piece of information is picked up by a model, it does not stay isolated. It can resurface in automatically generated background paragraphs, translations, summaries or new articles built on the same flawed sources. Without any intent and without any hostility, the error becomes structural. It gains strength through repetition.

This means that correcting information can no longer be treated as a one-off action. What is needed is an ongoing strategy: regular checks, updated official materials and a consistent presence in the public domain. An experienced agency can act not only reactively, but proactively, by building a coherent public archive – press releases, statements, reference pages – so that AI systems naturally find and use the accurate version of the facts.

4.     The erosion of editorial accountability

Here, the risk is that a media error can no longer be clearly tied to one author or one newsroom, making the correction process slower and more difficult.

Today, some articles are written by journalists, some are augmented by algorithms and others are generated almost entirely by AI models. In this mix, accountability becomes harder to pin down. When a misinterpretation appears, a natural question arises: whom do you ask for a correction? The newsroom? The individual reporter? The technology that silently completed the text? The traditional chain of editorial responsibility is fragmented.

This does not mean, however, that the process becomes unmanageable. It simply means that responsibility is being redistributed: serious media outlets remain responsible for what they publish, and companies need a more structured approach when they challenge inaccuracies. In practice, interventions can no longer be occasional or improvised.

An agency or communication specialist with long-standing media relationships knows how newsrooms work, who the editors are and how decisions are made. The trust built over years can unlock not just a quick correction, but a real conversation. In many situations, the difference between an error that fades away and one that escalates comes down to this relationship: the ability to pick up the phone, speak directly to the journalist, explain the situation clearly, provide the right information and rebuild trust.

5.     Increased pressure on Corporate Communication

The risk here is that neutral or incomplete messages may be rewritten, expanded or simply ignored in ways the company cannot control.

In many organizations, the first drafts of press releases are already being written with the help of AI. It is tempting, fast, coherent, grammatically correct. Yet companies are often surprised when such texts are not picked up by the media, or when they lead to unwante angles. Generative models write correctly, but not strategically. They have no editorial instinct, do not understand what constitutes real news and are unaware of industry nuances. Journalists recognize an impersonal text instantly – and treat it accordingly.

At the same time, newsrooms are themselves using AI to enrich the releases they receive, automatically filling in what is not explicitly stated. Models search online and “paste in” context from previous sources, sometimes incomplete or outdated. The result can be an article that starts with a harmless corporate announcement and ends with conclusions the company never intended. Any ambiguous sentence becomes an invitation for the algorithm to fill in the blanks.

To avoid this, corporate communication needs to be designed more carefully, not just written correctly. Releases require clear context, verifiable data and a logical narrative that leaves AI with as little as possible to invent. An experienced communicator knows what the media is looking for, what is worth saying and what requires extra explanation.

If you want to go fast, go alone; if you want to go far, go together

Artificial intelligence is bringing greater efficiency to the media, but also a new level of unpredictability. The five risks outlined here – from losing control of the context to seeing corporate messages automatically rewritten – are signals that the information ecosystem is operating under new rules. Reputation is no longer protected only through occasional rights of reply; it requires a continuous process of clarification, verification and strategic communication.

This is why companies need a thoughtful, experienced partner who understands both how the media works and how AI reshapes information. The role of that partner is not to fuel anxiety, but to bring structure, calm and direction. When communication is well managed, technology stops being just a source of risk and becomes a multiplier for accurate messages and well-designed positioning.