Reputation Management in the Age of AI – Rethinking Corporate Rankings
In a digital world defined by growing complexity and shrinking attention spans, simple stories capture attention. That’s why corporate rankings are increasingly critical to reputation management. As generative AI tools such as ChatGPT replace the traditional internet search, they fundamentally change how people access information. This article outlines why rankings management is now essential to every company’s reputation strategy and presents a four-step approach to boost visibility in AI-generated results.
By Steffen Rufenach and Shahar Silbershatz
The power of simplification: how rankings shape perception
Rankings offer simple answers to complex questions like “Who is the best employer?” or “Which companies are innovation or sustainability leaders?”. By placing firms in league tables, they impose structure and signal distinctions – good versus bad, trustworthy versus untrustworthy. Few stakeholders question the validity of the results when reputable media outlets or influencers publish them.
In response to the increase in rankings designed primarily to sell accreditations, many communicators neglect rankings or focus on a few hand-picked ones. However, this narrow approach is risky. Just as media monitoring can’t be limited to a few top-tier outlets, having tunnel vision on the rankings landscape comes with risks and missed opportunities. The biggest risk concerns the impact of AI-generated responses.
A global study on the reputation effect of rankings
To examine the impact of rankings on corporate reputation, we surveyed +4,000 randomly selected stakeholders representative of the national population in 13 markets, asking whether they had seen ranking results for companies they knew during the past year. We measured those companies’ reputation attributes – such as perceptions of their authenticity, integrity and leadership – as well as their Trust & Like Score.
Results show that rankings have a positive impact on how much people trust and like a company. Among respondents who had seen a familiar company in a ranking, Trust & Like Scores were at least 10 points higher (on a scale of 0-100) in each sector. In sectors like oil & gas, industrial & machinery and banking, the uplift exceeded 20 points.
Other key findings include:
Ranking visibility is much higher in emerging markets (over 50% of people in India, China and Brazil) and around tech-related sectors (MedTech, automotive, BigTech).
Scores for every attribute increase, particularly for inspiration, relevance and ESG.
The paradigm shift: from keyword search to answer engines
The way people find information online is fundamentally changing. Gartner projects traditional search engines will lose more than half their global traffic to AI tools by 2028. Unlike search engines that provide a list of links, AI chatbots deliver specific, tailored answers. To generate their answers, AI tools rely on data sources that fulfill trust criteria broadly aligning with Google’s E-E-A-T (Experience, Expertise, Authority, Trust) framework. Additional signals include external validation, and content quality, e.g. timeliness and semantic relevance.
Rankings perform particularly well against these trust signals due to their regular updates, structured methodologies, transparent processes based on verifiable data from public sources or stakeholder surveys, and high credibility backed by trusted publishers or research institutes. Thus they offer clear advantages over other content: their structured format easily integrates into generated answers, their data remains valid longer due to periodic updates, and their quantifiable metrics (e.g. ranking positions and evaluation criteria) allow comparisons over time. Rankings thus enable precise responses to queries like “Is Company X a good employer?”.
Strategic realignment: a four-step approach to managing rankings
Companies are adapting their communications strategies, optimizing content for AI algorithms. As stakeholders increasingly rely on AI assistants, metrics such as “share of search” and “share of voice” are supplemented by new metrics like “share of model” (SOM). SOM measures the frequency of positive brand mentions across AI models. Given the influence rankings have on AI algorithms, it makes sense to explicitly include them into the SOM framework.
Step 1: Inventory
Identify relevant rankings in core markets and document current standings.
Step 2: Implement AI monitoring
Set up software-based tracking of AI models (e.g., ChatGPT, Gemini).
Run search prompts based on strategic priorities.
Step 3: Assessment and prioritization
Prioritize rankings based on trust signals and strategic alignment.
Step 4: Communication optimization
Amplify positive rankings and provide context for negative results through owned channels.
Looking ahead: AI as the gatekeeper
As technology evolves, AI will increasingly act as the gatekeeper for online information, replacing traditional web browsing. Simultaneously, AI tools and broader reporting requirements enable more stakeholders to compare companies’ performance – even in real-time. Effectively managing ranking outcomes will thus become critical to maintaining public perception and communication effectiveness.
Steffen Rufenach
Steffen is CEO of R.A.T.E. GmbH – “The Rating Experts”. The firm supports global clients in strategically managing their performance in international rankings and ratings. Steffen also serves on the Expert Panel of CCR and teaches Communications Controlling and Sustainability at the University of Hannover.
Shahar Silbershatz
As CEO of Caliber, a Copenhagenbased stakeholder intelligence firm, Shahar leads a global team helping businesses listen to stakeholders, manage their reputation, and build trust. He holds an MBA from Columbia and frequently comments on corporate reputation and branding for CNBC, Financial Times, Reuters, and others.