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<\/figure>\n<\/div><\/figure>\n\nThis article was written by: Prof. Dr. Uwe Seebacher<\/em><\/p>\n\n 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?<\/p>\n\n 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.<\/p>\n\n 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.<\/p>\n\n 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.<\/p>\n\n 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.<\/p>\n\n In our approach, Predictive Intelligence therefore uses an \u201cUnderstanding Model\u201d (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.<\/p>\n\n It\u2019s not just about the answer, but about how it\u2019s arrived at and how robust it is<\/strong>. That\u2019s why it\u2019s not enough to simply ask a language model a complex communication question.<\/p>\n\n In our methodological and structural model\u2014developed since 2017\u2014such a question is first broken down into concrete, subject-matter-verifiable sub-questions<\/strong>. These are assigned to the appropriate knowledge domains and analyzed in parallel. Specialized models\u2014selected and validated over the years\u2014serve as a frame of reference: They help categorize information, understand relationships, and derive possible next steps.<\/p>\n\n 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?<\/strong><\/p>\n\n The system doesn’t simply provide a plausible-sounding answer. It reveals why a recommendation is reliable \u2013 and where uncertainty remains. Information gaps<\/strong> are shown just as transparently as<\/strong> uncertainties<\/strong> arising from differing or contradictory information and results. <\/p>\n\n 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.<\/p>\n\n 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.<\/p>\n\n 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.<\/p>\n\n The next step is creating a digital twin of a target audience.<\/strong> 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<\/strong> as simple as it is far-reaching: What if we could examine potential reactions to a message before we actually publish it?<\/strong><\/p>\n\n Let\u2019s consider a restructuring involving several thousand employees. Instead of discussing three different message variations internally based on experience or gut feeling\u2014and only seeing what works after they\u2019ve been sent out\u2014different 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?<\/strong><\/p>\n\n 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<\/strong>.<\/p>\n\n Impact measurement is thus shifting to an earlier stage: from explaining after the fact to making forward-looking decisions.<\/strong><\/p>\n\n 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.<\/p>\n\n This is also a response to the \u201canalysis paralysis\u201d plaguing many organizations: yet another analysis, yet another dashboard, even more data\u2014all 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 \u201cnext best action\u201d?<\/p>\n\n 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 \u201cAI-ready.\u201d 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.<\/p>\n\n 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.<\/p>\n\n The possible applications are specific and diverse:<\/p>\n\n The competitive advantage thus does not come from having more data, but from the ability to use that data to make better decisions sooner.<\/p>\n\n 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\u2014but the communications system itself.<\/p>\n\n You don\u2019t 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.<\/p>\n\n 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.<\/p>\n\n The central question should therefore be less: \u201cWhat can AI formulate for us?\u201d<\/strong> and more: \u201cWhat communication decisions should we make\u2014and why?\u201d<\/strong><\/p>\n\n We don\u2019t need more and more answers. We need better decisions.<\/p>\n\n That\u2019s why the best way to get started isn\u2019t with <\/strong>a general software demo. Bring <\/strong>a real-world communication decision <\/strong>\u2014one where you\u2019re 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.<\/p>\n\n That is exactly how this technology should be evaluated. If you are currently considering a communication decision like this, please bring it along. We\u2019ll use your specific case to show you what additional insights Predictive Communication Intelligence can reveal.<\/p>\n\nFrom Reporting to Strategic Management<\/strong><\/h3>\n\n
A plausible answer does not necessarily mean it has an effect<\/strong><\/h3>\n\n
From Static Personas to Dynamic Target Audiences<\/strong><\/h3>\n\n
No certainties, but better decisions<\/strong><\/h3>\n\n
Simulate first, then communicate<\/strong><\/h3>\n\n
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