- 31. August 2026
- Posted by: Uwe Seebacher
- Category: NEWS
Communication doesn’t need more data. It needs better decisions.
Today, communications departments have more data than ever before. They measure reach, interactions, and sentiment, monitor social media, and consolidate insights into dashboards. Yet a fundamental problem remains: Most tools explain what has already happened. We’re perfecting our view in the rearview mirror.
For strategic communication, another question is crucial: What should we do next? This is exactly where predictive intelligence comes in. The goal is not to collect even more data, but to use existing information to determine which communication decision is most likely to achieve the desired effect. The key output is not the next analysis, but the next best course of action.
Specifically, this means that a communications manager can, for example, assess—before releasing a message—which line of reasoning is likely to build trust among different stakeholder groups, where resistance might arise, and which alternative appears more robust. Analysis thus becomes a basis for decision-making—before the budget, reputation, or trust are put at risk.
This article was written by: Prof. Dr. Uwe Seebacher
From Reporting to Strategic Management
Communication controlling describes and evaluates what has happened. Predictive analytics forecasts what is likely to happen. Predictive intelligence adds the crucial next step: What should I do based on this forecast?
This changes the role of communication. Instead of explaining to management after a campaign what worked, we can predict in advance which message is likely to have what effect on which target audience. Communication no longer merely documents the past; it becomes a strategic management tool.
This is particularly relevant for C-level executives. Reach and engagement rates provide only limited insight into value creation. What matters most is the impact that communication has on relevant stakeholders and how that impact can be improved.
A plausible answer does not necessarily mean it has an effect
Can’t large language models already do that? Generative AI is impressive at creating text, summarizing information, and formulating plausible answers. But linguistic plausibility and communicative effectiveness are two different things.
A Large Language Model (LLM) generates a statistically probable response based on learned language patterns. This does not mean that a particular message will have the desired effect on a specific target audience.
In our approach, Predictive Intelligence therefore uses an “Understanding Model” (UM). Simply put, an AI-generated hypothesis is not accepted as fact, but is cross-checked against data, evidence, domain models, and alternative explanatory pathways. The more closely independent lines of analysis align, the more robust the basis for decision-making becomes. Where they diverge, uncertainty and the need for further information become apparent.
It’s not just about the answer, but about how it’s arrived at and how robust it is. That’s why it’s not enough to simply ask a language model a complex communication question.
In our methodological and structural model—developed since 2017—such a question is first broken down into concrete, subject-matter-verifiable sub-questions. These are assigned to the appropriate knowledge domains and analyzed in parallel. Specialized models—selected and validated over the years—serve as a frame of reference: They help categorize information, understand relationships, and derive possible next steps.
The individual results are then recombined and compared with one another: Where do they agree? Where do they contradict each other? And where is information still missing?
The system doesn’t simply provide a plausible-sounding answer. It reveals why a recommendation is reliable – and where uncertainty remains. Information gaps are shown just as transparently as uncertainties arising from differing or contradictory information and results.
From Static Personas to Dynamic Target Audiences
Personas aren’t inherently wrong. The problem with them is their static nature. They are developed at a specific point in time, are partly based on assumptions, and quickly become outdated. The reality of communication is more dynamic: attitudes change, people move between groups, and public debates can shift perceptions at short notice.
Predictive intelligence can derive dynamic target audiences from social listening, touchpoints, and other data. The group is defined not only by demographic characteristics, but also by attitudes, values, and actual communication responses. This results in target audience profiles that can be continuously updated.
Personalization, then, does not mean simply substituting different first names into the same text, but rather understanding the arguments that are relevant to a particular group.
The next step is creating a digital twin of a target audience. The idea comes from the industrial sector, where machines are digitally modeled to simulate their behavior under different conditions before any real changes are made. Applied to communication, this raises a question that is as simple as it is far-reaching: What if we could examine potential reactions to a message before we actually publish it?
Let’s consider a restructuring involving several thousand employees. Instead of discussing three different message variations internally based on experience or gut feeling—and only seeing what works after they’ve been sent out—different formulations can be tested in advance against various target audience models. Which line of reasoning builds trust? Which one generates resistance? Where are misunderstandings likely to arise? And through which channel does a message reach the respective group most effectively?
This is explicitly not about predicting human behavior with precision. Communication remains a complex social system. The key advancement lies instead in making potential effects, risks, and differences visible before they arise in reality.
Impact measurement is thus shifting to an earlier stage: from explaining after the fact to making forward-looking decisions.
No certainties, but better decisions
Predictive intelligence does not mean that human behavior becomes precisely predictable. Communication takes place within complex social systems. We are talking about probabilities, not certainties. The technology does not take decision-making away from communication professionals. It improves the evidence base.
This is also a response to the “analysis paralysis” plaguing many organizations: yet another analysis, yet another dashboard, even more data—all in the hope of achieving complete certainty. That will never happen. The better question is: Is our information base good enough to make a sound decision right now? A sound answer to this question therefore consists of more than just a score or a dashboard. It should show: What evidence supports a particular option? What evidence speaks against it? Where are the contradictions? What assumptions underlie the decision? How high is the level of uncertainty? And what is the resulting “next best action”?
This does not necessarily require a perfect data infrastructure. Companies do not have to first complete a years-long project to produce data that is fully “AI-ready.” Conceptual models can be designed to identify data gaps, redundancies, and ambiguities; to contextualize and interpret information; and to ask follow-up questions when necessary.
The starting point should therefore not be the data, but the problem: Which decision do we want to improve? Only then can we determine what data is actually needed.
Simulate first, then communicate
The possible applications are specific and diverse:
- Campaign: Which of the three messages should we publish—and for which target audience?
- Crisis: Which statement minimizes the risk of escalation without losing credibility?
- Employer Branding: What approach actually resonates with the talent we want to attract?
- Change: Which employee groups are likely to resist—and why?
- Reputation: Which signals require a response today, and which can we deliberately ignore?
The competitive advantage thus does not come from having more data, but from the ability to use that data to make better decisions sooner.
The learning cycle is also relevant. A communication initiative is implemented; its results are fed back into the model and taken into account in the next decision. For the first time, it is not just the communications department that learns—but the communications system itself.
You don’t have to overhaul your entire communication strategy to get started. It makes more sense to focus on a clearly defined use case: a campaign, a change initiative, or a reputation issue. The more precise the question, the clearer it becomes what information is needed and what additional insights predictive intelligence can provide.
The real paradigm shift, then, does not lie in yet another AI tool. Generative AI can massively accelerate the production of communication. But speed alone does not create impact. If our strategy or decision-making framework is flawed, we may end up simply scaling the wrong things faster.
The central question should therefore be less: “What can AI formulate for us?” and more: “What communication decisions should we make—and why?”
We don’t need more and more answers. We need better decisions.
That’s why the best way to get started isn’t with a general software demo. Bring a real-world communication decision —one where you’re currently unsure which option is the right one. A message. A campaign. A situation requiring change. A reputation issue. Then, using a specific case, we can demonstrate whether Predictive Intelligence actually provides additional insights compared to traditional analytics, dashboards, and generative AI.
That is exactly how this technology should be evaluated. If you are currently considering a communication decision like this, please bring it along. We’ll use your specific case to show you what additional insights Predictive Communication Intelligence can reveal.
