From Content Provider to Orchestrator: Communication in the Age of AI and FOLA

This article was written by: Christina Rettig

In many communications departments, one phrase is heard more often than before: “That’s much faster now.” They’re referring to generative AI: It creates texts, translations, and visuals at the push of a button in a matter of minutes. Operational workflows are being accelerated—massively.

This acceleration is taking place in an environment that is already becoming increasingly difficult to manage: faster, denser, more complex, and more confusing. As a result, it is not only expectations for efficiency that are growing. Another sentiment is also on the rise, one currently being discussed under the acronym FOLA: Fear of Losing Agency—that is, the concern that humans will lose their ability to control and make decisions to automated systems.

For communications departments, FOLA is therefore more than just a catchy buzzword. The term quite accurately describes the confusion many teams are currently experiencing: Not everything is being replaced, but a lot is shifting. When business units come up with AI-generated drafts and simply expect Communications to “just send that out,” it’s not just about faster text production. It’s about the role of the communications function itself. Will it become a mail carrier—or the entity that ensures direction, quality, and impact?

The discussion in the “Organization & Processes” cluster of the CommTech Working Group revealed that, while widespread substitution—in which other departments provide ready-made press releases or communication strategies in large numbers—is not yet taking place, the pressure is there. And it manifests itself in four ways.

Four Types of Pressure

First, there’s pressure to demonstrate competence. Communications departments are expected to be proficient in AI as a matter of course. If a result isn’t convincing, the unspoken accusation is often: “You don’t have a handle on the tool.”

Second, there is pressure to increase productivity. If an article used to be written in the past and, in theory, four are possible today, the expectation is obvious: then just deliver four. Some organizations already explicitly include efficiency targets in their annual goals.

Third, there is pressure to justify one’s legitimacy. When text production can be automated, it loses its exclusive value. Communication must then explain what its actual contribution is.

Fourth, there is pressure to deliver results. And this was particularly evident in the discussion. While there is internal talk of faster content, the entire communications landscape is changing on the outside: media consumption is becoming more fragmented, platforms are changing their algorithms in ever-shorter cycles, and organic reach is often no longer sufficient. For many communication goals, “pay-to-play” is no longer the exception—it’s the norm.

This shifts the real challenge. It’s not just a matter of whether the communications team can produce content more quickly. It’s about whether it still understands how content has an impact—via LinkedIn, search engines, newsletters, internal platforms, paid media, and, increasingly, AI-based information retrieval.

So the greater pressure doesn’t come from departments suddenly producing better texts. It comes from the fact that text alone is becoming less and less of a guarantee. Good content is of little help if it no longer reaches its audience. And it’s of even less help if it gets lost in a flood of interchangeable AI-generated content.

This is precisely the crux of the matter: For communicators, the AI issue is really a question of role.

It’s not that craftsmanship is disappearing—its status is changing

Writing, editing, translating, and reworking texts—all of these tasks remain important, but they are no longer exclusive. That’s because generative AI produces solid first drafts. These aren’t always brilliant, and sometimes they aren’t reliable, but they’re fast enough to change expectations about human work.

This brings to light what was often hidden before: How much of communication work involves strategic management? And how much is simply going through the motions?

That’s uncomfortable, but it brings clarity. AI brings honesty into the system, so to speak. Those who define communication primarily in terms of output come under pressure. Those who view it as a control function gain in importance.

However, “orchestration” must not remain a vague term in this context. This does not mean that communication will be vaguely “coordinated in some way” in the future. It refers to specific roles.

Communication becomes a strategic partner when it helps departments clarify their goals, target audiences, messages, and priorities. Many AI-generated texts don’t need better wording first—they need a better briefing.

It becomes a governance body when it defines quality standards, brand alignment, tone, approval processes, and risk limits. Then it’s no longer just a matter of whether a text sounds good, but whether it is coherent, robust, and justifiable.

And she becomes a distribution architect when she understands which channels are still effective for content. After all, the crucial question is less and less often: “How do we produce content?” Instead, it is: “How does relevant content still reach the right people?”

The bottleneck is counseling

One of the clearest takeaways from the discussion was this: The new role requires skills that are not yet widely available in many teams: facilitating messaging workshops, understanding key business metrics, interpreting platform data, and leading complex projects—these are no longer just nice-to-have skills, but the new basics.

This is precisely where a blind spot in the professionalization process to date becomes apparent. After all, many communications professionals were trained primarily in production: writing, editing, organizing, and publishing. That remains relevant. But it is no longer enough when AI accelerates operational work and the true value of the communications function must be demonstrated elsewhere.

Quality must be justified

Even if anyone can generate text, quality doesn’t become any less important. On the contrary: it actually becomes even more important, but it must be justified in a different way.

In the past, effort was often a silent indicator of quality. Those who spent a long time researching, went through multiple rounds of revisions, and crafted their words carefully could demonstrate quality through the process itself. AI is changing this logic. It’s now accepted that a text can be produced quickly. So communication must be able to specify more precisely when a text is truly good.

Quality doesn’t mean “looks elaborate and sounds professional.” Rather, it means: Does the content align with the strategy? Is the message clear? Has the context been correctly understood? Is the tone consistent with the brand? Have risks been identified? Can the text be integrated with other content? Can the message be defended?

This is the point at which communication must prove its value all over again. It is not output that justifies its function, but judgment.

This ability to exercise sound judgment becomes particularly important in areas where AI exacerbates risks: disinformation, deepfakes, unreliable sources, generic statements, legally sensitive wording, or thoughtless automation. Communication thus becomes increasingly important as a means of building trust—not because it seeks to control everything, but because it can set the standards by which organizations communicate.

The psychological dimension is not a minor issue

The discussion also showed that the frustration is not only organizational but also has a psychological dimension.

Many communications professionals are experiencing a loss of status. What was long considered the core of their expertise can suddenly be automated, at least in part. Younger employees feel additional pressure to measure up. They’re expected to deliver faster, respond more promptly, and provide more strategic advice—often without systematic training. This creates a competence paradox: expectations are rising faster than self-efficacy.

This is exactly where the success of transformation is determined. Teams that view AI primarily as a threat go on the defensive. Teams that view AI as a testing ground and a way to lighten their workload learn faster. The technology is the same; the difference lies in leadership, culture, and managing expectations.

Leadership must therefore do more than simply provide tools. It must clarify what is expected and what is deliberately not expected. It must define what constitutes quality, what competencies need to be developed, and how to prevent speed from becoming the sole measure of performance.

This is not a minor issue. Anyone who feels replaceable is unlikely to provide confident advice. And anyone who cannot provide confident advice will not be able to fulfill the new role of communication.

How Communications Departments Can Respond

The first reaction concerns how we approach efficiency. If AI speeds up the process of writing an article, that does not necessarily mean four articles will be produced. Nor should it automatically follow that the same output must be achieved with fewer people. Efficiency gains can be channeled into increased output, lower personnel costs—or into better consulting, more contextual work, clearer prioritization, and stronger quality assurance.

This distinction is crucial. Organizations tend to capitalize on efficiency gains immediately. If something can be done faster, it should be done more often. If something can be automated, it should be scaled up. And if something can be done with less effort, it quickly becomes a justification for cutting back on resources.

Under cost pressures, routines that can be automated are unlikely to remain untouched. But that is precisely why communication must clarify which activities are truly routine, and which capacities for consulting, impact measurement, risk assessment, and relationship-building need to be preserved or expanded.

Otherwise, there is a risk of a double loss: the organization reaps the efficiency gains, while the communications function loses its strategic substance.

The second response concerns learning. One-off training sessions are not enough. An AI “driver’s license” can be a useful starting point, but it does not solve the structural problem. The real question is: How can a communications organization become capable of learning?

The most effective approaches are closely tied to the work itself: role-based skill profiles, cross-functional teams, job shadowing in IT, strategy, or business units, joint workshops with functional departments, and project work with a safety net. In other words, learning opportunities in which younger colleagues take on responsibility but aren’t left to fend for themselves.

Learning doesn’t happen primarily in the classroom. It happens within the organization—in real projects, real conflicts, and real decisions.

This also applies to the next generation. When traditional entry-level tasks are automated, a new training challenge arises. How will young communications professionals learn to provide strategic advice if they no longer grow into this role step by step through production, research, and routine tasks? If the communications profession fails to answer this question, it will lose not only efficiency in the medium term but also the next generation of strategic communications expertise.

The Decisive Moment

AI does not make communications departments obsolete. However, it shifts the point at which communication must prove its value. It is not the speed of the text that matters, but the better decisions made before and after: What is our goal? What message are we conveying? What risks are involved? And through which channels do we actually achieve an impact?

As a result, communication becomes less of a delivery function and more of a management function. Those who use AI solely to produce more content are exacerbating the problem. Those who use it to better organize consulting, quality, and impact gain greater relevance.

So the goal isn’t to use AI to write faster. The goal is to use AI to communicate better.

This article is based on the discussion that took place during the AG CommTech Cluster’s “Organization & Processes” regular meeting on February 18, 2026, and the summary prepared from it; the statements were condensed and anonymized in accordance with the Chatham House Rule.

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