Misinformation and disinformation are usually treated as societal, political or geopolitical risks. In an AI-enabled information environment, they are also becoming workforce risks. As generative tools make persuasive synthetic content easier to produce and harder to verify, employers face growing challenges around reputation, employee relations, data governance, legal exposure and AI-assisted decision-making.

The old problem of new information technologies

A donkey’s body, fish-like scales, and a distorted human face: this was the ‘papal ass’, one of early modern Europe’s stranger media sensations. Supposedly discovered in the River Tiber in Rome in 1523, the creature was presented in pamphlets as a divine warning against corruption in the Catholic Church.

Papal Ass

The creature never existed, yet the story spread rapidly, as woodcut illustrations reproduced in cheap pamphlets were carried through Europe’s expanding communication networks. This happened because the printing press had transformed the geography and pace of information: rumours that might once have remained local could now be replicated at scale, allowing sensational claims to travel faster than verification.

Five centuries later, generative AI and algorithmic distribution platforms are producing a comparable disruption. Persuasive synthetic text, images, and audio can be created cheaply and at scale, tailored for virality, and distributed globally within minutes. The technologies differ, but the institutional challenge remains strikingly familiar.

The World Economic Forum’s recent Global Risks Report 2026 identified misinformation (false information shared without the intent to deceive) and disinformation (information deliberately fabricated and spread to deceive) as the most severe global risk linked with technology. While this challenge is generally framed in democratic or geopolitical terms, this article explores a less examined implication: how misinformation and disinformation are increasingly becoming an operational risk for employers. As the boundaries between social discourse and workplace perception blur, information integrity becomes a matter of governance rather than public relations.

AI as amplifier and feedback mechanism

Artificial intelligence has accelerated and automated existing forms of misinformation, as generative systems lower the barrier to producing plausible narratives, making synthetic images and text appear authoritative and persuasive, while weakening authenticity cues. As Nina Schick showed in her book Deep Fakes and the Infocalypse, these tools are beginning to enable the industrial-scale production of synthetic media capable of reshaping information environments. The WEF Global Risks report similarly warned against the information risks linked with the rise of GenAI: lowering “the barriers for content production and distribution” can potentially enable “threat actors, state agencies, activist groups, and individuals who may or may not have criminal intentions” to “automate and expand disinformation campaigns, greatly increasing their reach and impact”. As a result, the report concludes, “within a decade, deepfakes and AI-generated misinformation could become ubiquitous, making it impossible for citizens to distinguish truth from deception”.

Large language models are trained on vast volumes of online material. Where that material is saturated with distortion, bias or fabrication, models may reproduce or amplify the same patterns. Over time, a circular dynamic can emerge: misleading AI-generated content enters the online ecosystem, that ecosystem supplies data for later models, and the feedback loop strengthens. The risk is less like a single error and more like a map repeatedly redrawn from other maps, rather than checked against the terrain: each version may look authoritative, but small distortions can become embedded until they are treated as features of reality. As synthetic content becomes cheaper to produce and harder to verify, the informational foundations on which organisations rely become less stable.

This is precisely what the American philosopher Lee McIntyre referred to as the ‘post-truth’ condition,  whereby misleading narratives circulating more easily than verified knowledge can erode the shared factual ground on which institutions depend. The 2025 Edelman Trust Barometer rang the same alarm bells, noting that, according to 63% of respondents globally (and up to 75% in some markets), “it is becoming harder to tell if news is from respected media or an individual trying to deceive people”.

From societal threat to organisational variable

This logic poses a significant problem for the future of work. Even though misinformation is generally framed as a political or cultural problem, it now brings about an increasingly serious, if less often explored, set of three challenges for employers.

Reputational risk at algorithmic speed

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Internal cohesion and AI literacy

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Data-dependent decision-making

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The investment imbalance

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Employment law and regulatory exposure

Information disorder rarely presents a self-contained legal problem. More often, it complicates familiar areas of exposure, including discrimination, data protection, fairness, employee speech and AI governance.

AI-assisted decisions and discrimination risk

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Automated decisions and data protection

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Grievances, evidence and employee voice

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The EU AI Act

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Data infrastructure as defensive architecture

Employers’ response to information disorder should rest on two mutually reinforcing pillars: creating resilient data infrastructure and strengthening the human capabilities needed to exercise meaningful judgment and oversight.

Workforce data as infrastructure

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Provenance and supplier oversight

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Integrated governance

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Human judgment as an organisational safeguard

Meaningful human oversight requires more than placing an individual at the end of an automated process, as reviewers need sufficient knowledge, authority and time to interrogate the evidence, identify gaps and depart from a system’s recommendation where appropriate.

This reinforces another recommendation of our Future@Work 2026 report: technological deployment should be matched by workforce development, thoughtful job design and human-centred capability building. The current investment imbalance suggests that many organisations remain some distance from that goal.

Resilience begins with practical AI literacy. Employees should understand how generative systems produce answers, the limitations of their training data and the possibility of hallucination, bias or manipulation. Training should help them evaluate sources, verify consequential claims and recognise when apparently credible material requires further scrutiny. Clear escalation routes should enable concerns about dubious data or AI-generated outputs to reach the right decision-makers quickly.

The report also recommends moving towards a skills-based workforce strategy that develops capabilities across roles and functions rather than confining AI expertise to technical specialists. This is especially relevant because 64% of employers expect demand for soft skills to increase as AI reshapes work. Critical thinking, judgment, communication and contextual awareness help employees interpret information within its wider legal, ethical and organisational setting. In an environment characterised by abundant content and weakened authenticity cues, these skills become part of an organisation’s risk infrastructure.

Leadership matters too. Managers should communicate clearly during uncertainty, respond quickly to inaccurate internal narratives and create a culture in which challenging an automated output is treated as responsible practice. They should also guard against cognitive offloading by clarifying which decisions AI may support, which require independent verification and where human accountability remains decisive.

Cross-functional exercises can embed these capabilities collectively. Simulations involving fabricated allegations, manipulated evidence or contaminated datasets can test whether HR, legal, data, communications and operational teams know how to verify information, escalate concerns and coordinate a response.

The goal is a workforce that can use AI confidently while retaining the capacity to question it. Technology may determine how quickly organisations generate and process information. Investment in people will determine whether they can distinguish insight from distortion and act accordingly.

Building resilience in an age of information disorder

The story of the ‘papal ass’ offers a final lesson. The printing press may have created an information environment that institutions were initially ill-equipped to govern, but societies gradually developed stronger norms of verification, accountability and editorial scrutiny. 

Today’s challenge is greater in speed, scale and technical complexity, yet the underlying principle remains the same: technological change must be accompanied by institutional adaptation. Employers cannot prevent every fabricated claim, distorted dataset or unreliable AI output from entering their organisations. They can, however, determine how far such information travels, how heavily it influences decisions and how quickly it is challenged.

By combining robust data foundations with meaningful human oversight, cross-functional governance and sustained investment in judgment and critical thinking, organisations can build resilience into the way they create, interpret and act on information. The task is substantial and urgent, but employers still have a choice: to allow AI to amplify disorder, or to build the governance, infrastructure and judgment needed to support more informed and responsible decisions.


Image details: Workshop of Lucas Cranach the Elder, Monk Calf, 1523. Woodcut on paper, image: 18.4 × 11.3 cm, sheet: 25.6 × 14 cm. Staatliche Kunstsammlungen Dresden, Kupferstich-Kabinett, A 1900–671. Details can be found here: https://artsandculture.google.com/asset/papal-ass-workshop-of-lucas-cranach-the-elder/fAE79ZFRTcIoSQ