AI is transforming everything—PR included.

Its applications range from strategy development and content creation to the evaluation of communication impact. In 2018—long before ChatGPT became widely known—the Chartered Institute of Public Relations published a white paper titled “Humans Still Needed: An Analysis of Skills and Tools in Public Relations.” Its conclusion remains valid today.
 
Since then, and especially in the past two years, AI applications have evolved rapidly. In particular, Large Language Models (LLMs) have opened up new dimensions for AI-based tools. These advances now allow PR professionals not only to gauge public sentiment more quickly and accurately but also to predict trends, assess reputational risks in real time, and generate data-driven insights to refine communication strategies.

Data is the Key …

Successful PR strategies rely on ongoing, transparent evaluation to ensure that efforts are aligned with business objectives and resonate with key audiences. AI can make this evaluation much more efficient—but only if communications professionals remain critical and informed. Key questions include:
Which data sources are relevant? Are the algorithms transparent and fit for purpose? How can results be meaningfully translated into strategic action?

BLACK BOX WAS YESTERDAY

Five Questions to Ensure Data Transparency:

1. Which data sources are included – and which are missing?
   For example, does the tool incorporate traditional media, social media, or trade publications? A model lacking industry-specific data may miss crucial trends or competitor moves.
2. How were the models trained – are there bias risks?
   A model trained primarily on general consumer data may misinterpret technical or regulatory language, leading to irrelevant or misleading results. Have demographic imbalances or ethical risks been addressed?
3. How accurate is the model in terms of recall and precision?
   High precision means fewer false positives, but low recall means missing relevant content—potentially skewing insights and leading to flawed decisions.
4. How customizable is it and at what cost?
   Can custom keyword lists or topics be defined? This can be especially crucial in niche industries.
5. Are the processes fully automated, or where are human analysts involved in the process?
   If automation is total, how does the tool ensure accuracy, contextual relevance, and ethical compliance?

 
Regardless of whether companies rely on tools, platforms, or service providers, one principle remains: data quality is essential.
 
AI has undoubtedly revolutionized the way PR data is collected, processed, and analyzed. But it is important to make a clear distinction between the tools used, the data, the metrics, and the actual goal of the evaluation.

… But Good Data Doesn’t Happen by Chance

 
PR evaluation belongs to the broader field of media intelligence, which includes media monitoring, press distribution, social media tracking, and consumer analytics. While media clippings were once compiled manually—using scissors and highlighters—AI functionality is now embedded in most tools, enabling faster and more cost-effective data collection.
 
Still, even the smartest tools rely on a solid foundation—namely, the articles, posts, and websites they analyze. Communications professionals aren’t just buying tools—they’re investing in the data behind them. Only high-quality data enables reliable metrics like reach, sentiment, or share of voice, and meaningful conclusions about communication effectiveness.
 
To ensure quality, transparency is critical. Don’t settle for vague claims. Evaluate:

• Are key sources missing (e.g., trade media)?
• Are stakeholder groups represented fairly?
• How was the model trained? Was the training data balanced and industry-relevant?
• How well is the model working? Are reliability and recall rate sufficient?
• Can custom taxonomies be built easily to ensure that your key topics and keywords are accurately recognized?
• Is the analysis fully automated or is human expert review integrated?

 
Only with clear answers to these questions can companies rely on the validity of results—particularly for AI-generated metrics such as sentiment analysis or topic segmentation. Results in these areas can vary significantly between providers.

People Make the Difference

Despite technological advances, one constant remains: human judgment. AI tools can process vast volumes of data faster than any team, but interpreting the findings and aligning them with business objectives still requires human insight.
 
A comprehensive PR evaluation examines how communication efforts relate to business outcomes:
Did visibility lead to trust, enhance reputation, or influence stakeholder behavior? What worked? What didn’t? Where is there room for improvement?
 
AI can support this process—but only if it is treated as a dynamic tool that is continuously adapted, refined, and optimized.

Disruptions and Trends

One of AI’s most visible impacts in PR measurement is the automation of routine tasks. Media spikes can now be flagged in near real-time—via alerts or summaries that highlight the cause, be it a product launch, social trend, or competitor action. While not revolutionary, these functions save time, increase clarity, and support quicker strategic response.
 
AI also enables the analysis of large text corpora—for instance, to identify communication patterns among stakeholders. These insights are particularly valuable when used as a basis for in-depth analysis and strategic decision-making.
 
Yet this remains an iterative process that requires ongoing interpretation and quality control. Only then are results both technically and contextually reliable.
 
A promising but still emerging trend is predictive modeling: using AI to forecast media response or stakeholder reactions to future events. While current reliability is limited, the trajectory is clear. As data quality improves and media ecosystems become more algorithmically driven and predictable, the simulation of future communication scenarios will become a core element of strategic PR planning.

The Future of AI in PR Measurement

AI has irreversibly transformed PR measurement. But it remains a tool—not a cure-all. As the number of AI-powered solutions grows, communication professionals must stay vigilant, ask critical questions, and demand transparency about both the underlying data and how the tools function.
 
When technology, data, and human expertise are balanced effectively, AI holds tremendous potential. Yet future success will depend on how wisely these tools are used—and whether they generate actionable insights rather than simply producing more data.
 
The fable of the hare and the hedgehog reminds us: it’s not the fastest who wins, but the one who acts with strategy and foresight. The same applies to PR measurement. Speed alone is not enough. Those who, like the hedgehog, remain focused on their strategy, maintain a clear overview, use their tools purposefully, and integrate human judgment at the right points will ultimately lead the field.

Authors: 

Dr. Maya Koleva:
Dr Maya Koleva is Head of Research and Analysis at Commetric, a consultancy specializing in communication controlling, and a board member of AMEC (Association for the Measurement and Evaluation of Communication). She develops methods for PR and media measurement with a focus on AI, media analysis and social media discourse.
 
Steffen Rufenach:
Steffen Rufenach is the founder and CEO of R.A.T.E. GmbH, a strategy consultancy specializing in rankings management, sustainability, and communication controlling. In addition, Steffen teaches communication controlling at Hanover University. Through his engagement with the International Controller Association (ICV) and AMEC (Association for Measurement and Evaluation of Communication), he contributes to advancing the practice of communication measurement and the application of AI.