Reputation engineering; Building stakeholder trust in the age of AI

The mining industry has always operated under intense public scrutiny. Community expectations, investor pressures, regulatory obligations, and environmental oversight create a uniquely complex communications environment. In recent years, technological change, climate urgency, and geopolitics have reshaped how the industry communicates its role and value. The rise of artificial intelligence in the information ecosystem represents yet another fundamental shift. As AI evolves, an organization’s reputation will no longer be shaped solely through human interactions, media coverage, social activity, or corporate disclosure. Increasingly, public understanding depends on how AI systems interpret and represent company information. These systems influence search results, conversational tools, news aggregation, social discourse, and even investor intelligence platforms. As AI-generated summaries, answers, and narratives become the default interface for stakeholders, they also become authoritative in shaping trust. For mining companies, this creates both risk and opportunity. The organizations that recognize how AI reads, prioritizes, and amplifies information will be better positioned to build trust. Those that remain silent or rely on traditional communication methods may find their reputations being algorithmically defined without their participation. This paper explores how mining companies can proactively engineer their reputations in the age of AI. Changing foundations of mining reputation The traditional trust gap. The mining sector faces a well-documented trust gap. A 2023 ICMM report and other studies show that public confidence in mining lags other industries. Communities express concerns about environmental stewardship, climate impacts, operational transparency, and social contribution. While mining companies have significantly strengthened their ESG performance and reporting, public perception often trails behind progress. The behavior of mining companies also plays a role. Three persistent misconceptions commonly shape how mining leaders approach communications: Silence equals safety. Many companies assume that limiting public communication minimizes risk. Data from the 2025 Edelman report shows that 53 percent of people will assume a brand or organization is doing nothing or hiding something if it remains silent on societal issues. Silence does not create neutrality. It creates suspicion. Outputs equal impact. Mining companies dedicate significant resources to producing communication outputs, including press releases, sustainability reports, investor updates, conference attendance participation and social media channels. Despite this, many are unable to determine if these activities are reaching the right audiences or influencing perception in the right way. Visibility equals vulnerability. Some leaders believe speaking too often or too transparently will only serve to invite criticism. In reality, trust is built through consistency. Research shows that people typically need to encounter a message three to seven times before they fully understand it. Trust is shaped less by volume and more by repetition and relevance. Why this matters Three forces are now converging to fundamentally change how mining organizations build trust: Public narratives are accelerating, driven by real-time news and digital channel updates, 24x7 news and opinion cycles, and instant AI-generated commentary. Increasingly, AI systems now guide information discovery, in many cases before humans engage with original sources. Given this, the next 12–18 months represent a critical transition window to establish credible, machine-readable reputational foundations. To adapt, mining organizations must shift from communications as output to communications as infrastructure, a structured, continuous, data-driven system that supports both human understanding and AI interpretation. How artificial intelligence is reshaping reputation The era of no-click search. Search behavior is undergoing a profound shift. Increasingly, people are not looking for websites, they are looking for answers. According to 2025 research from Datos, U.S. zero-click rate reached nearly 27 percent in Q3 2025, up 3 percent year over year from Q3 2024, Zero-click search means stakeholders often see only the AI-generated synthesis of a company’s reputation when they perform an online search. Increasingly, what AI chooses to surface in response to an online query effectively becomes the truth. This is particularly consequential for mining, where stakeholders often seek quick answers to high-stakes questions on safety, environmental impact, or social responsibility. If AI cannot find structured, credible content from a mining organization, it will rely on other sources for that information. Put simply: AI-visible content now shapes public reality. The rise of AI-generated content. The volume of AI-generated online content is increasing exponentially. While this accelerates information flow, it also dilutes quality. In this environment of so-called “AI slop,” credibility becomes a premium asset. Mining companies with well-structured, verifiable, and consistent communication will stand out, not only to people but to the AI systems that surface and summarize information. How AI decides what to trust Common principles that influence how AI prioritizes information include: Relevance: How well the content matches the query or context Completeness: Whether the source covers the topic thoroughly Trustworthiness/Credibility: Authority, reliability, accuracy, and low bias Information density: Amount of useful, meaningful data per section of content Source quality/format: Clarity, structure, and how easily the model can parse the information Building a machine-ready reputation Reputation engineering is not spin. It’s creating a transparent record that people and machines can trust. Principle 1: Create content with AI in mindAI systems rely on structured data, clear language, and factual statements. Mining companies should prioritize: Content that answers real stakeholder questions Clear definitions, explanations, and contextual framing Consistent descriptions of operations, impacts, and commitments Figures, data tables, and metrics formatted in machine-readable ways Evergreen content that can be continuously updated Principle 2: Consistency builds credibilityIn the AI ecosystem, inconsistent information erodes trust. Key messages should be: Repeated across all public channels Presented with compatible wording and definitions Updated frequently to maintain recency signals Reinforced through third-party validation where possible Principle 3: Recency is a competitive advantageAI systems elevate the newest credible information. Mining organizations should create communication processes that allow: Regular factual updates, and not necessarily waiting for “official” news to happen Real-time corrections of outdated information Explore emerging, proactive AI applications To operationalize reputation engineering, mining organizations can apply AI directly to their communications workflow. Example: Stakeholder sentiment radar AI-powered monitoring systems can track and assess: News coverage Social discourse Community forums Local media commentary Influencer narratives Emerging misinformation patterns Such tools provide early warning signals for issues, allowing teams to respond before narratives solidify. Example: Financial story engineMining companies rely on clear investor stories. Quarterly results contain valuable insights but are often under-communicated. AI tools can: Convert financial data into clear narratives Highlight trends Identify investor-relevant storylines Produce multiple content formats (summaries, charts, talking points) This ensures financial performance is interpreted correctly — not lost in technical complexity. Example: ESG impact visualizerMany mining companies generate extensive ESG data but struggle to present it in ways that resonate with communities. AI can transform raw data into: Visual stories Automated impact dashboards Shareable community metrics Scenario or trend visualizations This makes ESG progress visible, understandable, and repeatable. Mini-case Chatbot-driven perception gap. Chatbots like ChatGPT and Claude often miss a mining company’s core message. PRA and AI partner Gutsy recently tested this for a producer, scoring how well its public narrative was reflected in chatbot responses and comparing the results against peers. Example. This analysis revealed a strong business story resonance, but also pointed to where the company’s message fell short, and where its strongest trust-building efforts are – ESG, Value and Community Short-term strategies for the AI era Mining organizations can build a durable AI-ready reputation by adopting four practices: Build a credible, structured information layer. This includes: Clear operational descriptions Third-party-verified data Regular updates tied to material events Easily discoverable web architecture Create stories that answer stakeholder questions. Structure and design content with likely Chatbot questions in mind. Provide it in easy-to-understand language, backed by data. Close the perception gap. Companies should routinely compare: Their official messaging AI-generated summaries Public sentiment Peer narratives This identifies where visibility or consistency is lacking and enables targeted corrections. Conclusion By making their information easy for machines to understand, using AI proactively, and communicating clearly and consistently, mining companies can strengthen stakeholder trust and lead in the new information ecosystem. Those that act now will build stronger trust today and a more resilient reputation for the future. References 2025 Edelman Trust Barometer. https://www.edelman.com/trust/2025/trust-barometerUnderstanding Perceptions of Mining Insights from General Public Respondents. https://www.icmm.com/website/publications/pdfs/icmm/2023-globescan-radar.pdf?cb=77113University of Maryland Baltimore. Marketing Rule of 7. https://www.umaryland.edu/cpa/rule-of-seven/

Reputation engineering; Building stakeholder trust in the age of AI

Authored by: Michael MacMillan

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