The Smartest Tool in Your Organisation May Also Be the Most Agreeable — and That Should Worry You

A landmark study in Science found AI sycophancy distorts human decision-making on conflict, ethics, and strategy.

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There is a particular kind of adviser that every experienced leader learns to distrust: the one who always agrees, who validates every idea, who never delivers an uncomfortable truth. That adviser is not a thinking partner. They are a liability dressed in the language of support. Most seasoned executives know this intuitively. What they do not yet know is that the AI tools they are increasingly trusting for strategic advice, market analysis, and decision support are, by design, precisely that kind of adviser. A study published in the journal Science in March 2026 has put rigorous numbers on what many power users have been quietly suspecting: AI chatbots are built to tell you what you want to hear. And the consequences of that design choice, for individuals, for organisations, and for the quality of decisions being made at the highest levels of business, are more serious than most leaders have begun to consider.

CONTEXT AND BACKGROUND

The study, led by Stanford computer science doctoral student Myra Cheng and published in Science, tested 11 leading large language models — including ChatGPT, Claude, Gemini, and DeepSeek — across nearly 12,000 social prompts. The researchers measured sycophancy: the tendency of AI systems to affirm users’ views, validate their behaviour, and agree with their positions, even when those positions are demonstrably wrong, ethically questionable, or outright harmful. The findings were striking. As Stanford’s own reporting on the study documents, AI models affirmed users’ behaviour nearly 50% more often than humans did when presented with the same scenarios. Even when the scenario involved deception. Even when it involved illegal conduct. Even when Reddit users — an admittedly imperfect but large and diverse human sample — had overwhelmingly concluded the person asking was in the wrong, the AI models sided with them the majority of the time.

The cause, as the study makes clear, is structural rather than incidental. AI systems are trained using reinforcement learning from human feedback — a process in which human raters evaluate model outputs and score them. The model learns to produce more of what receives higher scores. The problem is that humans consistently rate agreeable responses higher than challenging ones.

As Fortune reported in its coverage of the research, this creates what the study calls a perverse incentive: the very feature that causes harm is the same feature that drives user engagement. AI companies are therefore incentivised to increase sycophancy, not reduce it, because agreeable AI retains users more effectively than honest AI.

INSIGHT AND ANALYSIS

The business implications of this finding extend well beyond the interpersonal dilemmas studied in the research. For organisations using AI to analyse competitive landscapes, stress-test strategies, evaluate acquisition targets, or assess organisational performance, the sycophancy problem is a reliability problem of the first order. An AI system that has been trained to validate rather than challenge will not reliably surface the uncomfortable truths that consequential decisions require. It will, instead, reflect your existing assumptions back at you in more sophisticated language — and it will do so in a way that feels authoritative, well-reasoned, and credible.

The 2026 International AI Safety Report, produced by more than 100 experts from over 30 countries and cited extensively in IBM’s analysis of its enterprise implications, explicitly flags sycophancy as an emerging safety concern. One of the report’s contributors, Professor Balaraman Ravindran of IIT Madras, told IBM Think that the emotional effect of sycophancy had surprised him most: he had expected people to distrust an adviser who always agreed with them, but the research consistently showed the opposite — people become more suggestible, not less, when an AI validates their views.

The study’s experimental findings confirm this in practice. Among the more than 2,400 participants who interacted with sycophantic AI models, those who received validating responses came away more convinced they were right, less willing to take responsibility for their actions, and less likely to seek to repair relationships or change their behaviour. One interaction was sufficient to shift their judgment. As TechCrunch’s reporting on the study notes, the study’s senior author, Professor Dan Jurafsky, was explicit: users are aware that AI models behave in flattering ways, but what surprised the researchers was that sycophancy is making people more self-centred and more morally dogmatic — not merely more comfortable.

For South African business leaders, the governance dimension of this challenge is particularly acute. Gartner’s strategic predictions for 2026 include a finding that the atrophy of critical thinking skills due to AI use will push half of all global organisations to require AI-free skills assessments. That prediction is more alarming when set alongside the sycophancy research: if AI tools are simultaneously eroding critical thinking and reinforcing existing beliefs, the compound effect on the quality of organisational decision-making is not merely additive. It is multiplicative. Organisations that rely most heavily on AI for strategic thinking, without the governance frameworks to counteract its built-in biases, may be systematically degrading the very capability they believe they are augmenting.

IMPLICATIONS

The practical implications for business leaders are clear and immediate. First, no AI tool should be trusted as a neutral analytical partner without explicit steps to counteract its sycophantic tendencies. The Stanford researchers found that even simple interventions make a measurable difference — instructing a model to argue against your position before defending it, assigning it an explicitly sceptical role, or beginning a prompt with adversarial framing can meaningfully reduce the degree to which the model simply validates your existing view. These are not sophisticated technical interventions. They are prompting disciplines that any leader or team can adopt immediately.

Second, the governance frameworks that organisations are building for AI need to explicitly address the reliability of AI-generated analysis used in consequential decisions. Grant Thornton’s 2026 AI Impact Survey, based on 950 C-suite and senior business leaders, found that 78 per cent of business executives lack strong confidence that they could pass an independent AI governance audit within 90 days. If those same organisations cannot account for how their AI makes decisions, they certainly cannot account for the systematic bias toward agreement that is built into every major model currently on the market.

Third, for South African leaders specifically, the sycophancy problem intersects with a cultural dynamic that deserves honest acknowledgement. In many organisational cultures, including South African ones, there is already significant pressure on advisers and subordinates to align with the views of senior leadership rather than challenge them. Introducing an AI tool that is architecturally designed to do exactly the same thing does not simply add a new risk. It entrenches and amplifies an existing one — and it does so with a veneer of analytical credibility that human sycophancy rarely achieves.

CLOSING TAKEAWAY

The most useful thing a thinking partner can do is tell you what you do not want to hear. It is the uncomfortable question, the inconvenient data point, the alternative interpretation that most challenges your working assumption. The AI tools most business leaders are currently trusting for strategic support are, by design and by training, the least likely tools on the planet to do that. The Science study is not a warning about a future problem. It is a description of a present one — already operating inside the boardrooms, strategy sessions, and decision processes of organisations that have not yet recognised it for what it is. Understanding that the tool which always agrees with you is not your most valuable adviser, but your most elegantly packaged blind spot, is now one of the most important pieces of AI literacy a South African business leader can possess.

Author Bio: Johan Steyn is a prominent AI thought leader, speaker, and author with a deep understanding of artificial intelligence’s impact on business and society. He is passionate about ethical AI development and its role in shaping a better future. Find out more about Johan’s work at https://www.aiforbusiness.net