AI knows the data, not the people

Behavioral Public Policy and Economics GmbH

AI knows the data, not the people

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Behavioral Insights Germany and Saudi Arabia

Artificial intelligence has become the rising promise of consumer insight. It analyses and breaks down immense amounts of behavioral data, detects patterns (that probably humans would miss), and powers a new generation of hyper-personalized experiences and visibility into customer preferences, purchase patterns, and social interactions.

In boardrooms across the region and beyond, leaders are told that algorithms can understand their customers better than traditional research ever could. The promise is simple: more data equals more insight by AI!

Yet, that equation does not hold. Data reveals what people do; understanding, on the other hand, reveals why they do it. The difference is not academic – it is strategic. It determines whether organizations make informed decisions or misinterpret the very markets they aim to serve. AI can illuminate patterns at unprecedented scale, but meaning still sits on the human side of the equation.

1. The promise – and illusion – of AI-driven insight

Across the Arab region, businesses and governments are adopting AI at remarkable speed. Predictive engines recommend products before consumers ask. Sentiment tools scan live social feeds to flag emerging trends. Real-time analytics, smart dashboards, and automated segmentation have created the sense of a market that is more knowable than ever.

These capabilities are unquestionably powerful:
AI can tailor messaging to individual behavior.
AI can forecast demand by reading subtle shifts in attention.
AI can deliver personalized support at scale.

AI can adjust pricing dynamically.

But these systems excel at correlation, not interpretation or causation. In fact, they map behavior without understanding the cultural, emotional, or social forces that shape it. For example, a surge in online engagement might signal enthusiasm. However, it could just as easily reflect sarcasm, social pressure, disappointment, or coordinated advocacy. Treating patterns as motives collapses the distinction between signal and meaning, creating the illusion of deep insight where none exists.

In fact, true understanding requires context, and context is precisely where AI struggles most…

2. When AI ‘misreads’ the human world: some documented cases

When the AI misinterprets content, the problem often isn’t a ‘technical error’ – the model did exactly what it was trained to do. The failure comes from the fact that human meaning is not reducible to statistical patterns. The machine or AI only sees statistical patterns, not human meaning or implications. These limitations are not theoretical. They have been documented across academic research, industry reports, and real-world deployments. Here are concrete instances where AI fell short:

  • Sentiment and sarcasm misclassification in Arabic social media: A recent study analyzing tens of thousands of Arabic-language tweets about generative AI found a measurable amount of sarcasm – a communication form rich in implicit context(1). Even state-of-the-art natural language processing models continue to struggle with sarcasm and irony, especially when cultural or dialectal nuance is involved(2). As a result, a post that is sarcastic criticism could be interpreted as praise; a serious risk for brands using sentiment analysis to gauge public opinion.
  • Bias and discrimination in automated classification systems: Concerns about algorithmic bias are no longer speculative. Research in AI from multiple sources has highlighted a tension: while algorithms can, in theory, be designed to outperform humans in unbiased perception, real-world systems often inherit and amplify structural biases embedded in data, objectives, and deployment contexts(3). The problem is not that machines or AI are inherently biased, but that bias becomes encoded, scaled, and difficult to contest once automated. Moreover, marketing and targeting algorithms can sometimes over-represent some consumer groups and other times render others invisible(4). These distortions directly affect segmentation, demand estimation, and strategic decision-making which could be questionable to developing ethical and equitable AI systems.​
  • Over-personalization and the erosion of trust: Industry and marketing analyses have documented multiple cases where AI-driven personalization crossed the line into intrusion. One widely cited example, discussed in marketing and tech commentary, involved retail recommendation systems inferring sensitive life events (such as pregnancy) before consumers had disclosed them in their families(5). Rather than signaling relevance, these predictions triggered discomfort and backlash. The failure was not technical accuracy, but a lack of consent and social judgment: prediction without permission.
  • Over-reliance on AI in high-stakes decision systems: domains leading to failure: Outside marketing, the promise of AI has sometimes led to disastrous overconfidence. As a high-profile example, an AI-based medical-recommendation system, once heralded as a game-changer, produced unsafe and inaccurate treatment recommendations (6). This demonstrates that AI’s “insight” may appear authoritative but can be dangerously misguided when applied without human oversight and contextual judgment.

Taken together, these cases point to a consistent conclusion: AI’s strengths – scale, speed, pattern recognition – do not compensate for its fundamental inability to interpret human meaning and nuance, context, culture, or ethics. Without interpretation, automation merely accelerates misinterpretation.

3. A balanced path: pairing machine scale with human intelligence

Success will come not from replacing humans with machines, but from combining their strengths:

  • Hybrid human-AI models: Use AI to flag emerging patterns or weak signals, but keep humans responsible for interpretation, strategy, and final decisions. AI can surface “what changed”, humans decide what it means.
  • Reskilling and repositioning human talent: As AI automates data collection and segmentation, human roles should evolve toward strategic interpretation, scenario planning, cultural reading, and ethical judgment; areas where machines remain blind.
  • Explainability, transparency, and consent: Systems should be designed so that consumers and researchers understand how data is used, what is inferred, and why. Opt-outs, clear data-use policies, or “human-in-loop” checks should limit misuse or overreach.
  • Ethical governance and data protection: Organizations should adopt frameworks to monitor bias, privacy, discrimination, and fairness – not as afterthoughts but as integral parts of insight systems. Regulation analogous to global standards (e.g. GDPR) becomes essential, especially in culturally diverse contexts.

4. In conclusion: Meaning remains human

AI will continue to transform how we gather data and detect signals, but unless we acknowledge its limitations, we risk building insights on shaky foundations. It is true that machines can cluster behavior, forecast probabilities, and highlight patterns. However, they cannot interpret intention, emotion, cultural meaning, or ethical weight. Those remain human domains.

For marketers, researchers, and policymakers, the challenge is clear: Build insight infrastructures where AI accelerates discovery and humans provide anchoring interpretation. Data may fuel the engine, but meaning must come from human thinking, beliefs, and judgement.

AI may shape the future of consumer understanding, but human reasoning remains what guides it responsibly.

References

  1. Al-Khalifa, S., Alhumaidhi, F., Alotaibi, H., & Al-Khalifa, H. S. (2023). ChatGPT across Arabic Twitter: A study of topics, sentiments, and sarcasm. Data, 8(11), 171. https://doi.org/10.3390/data8110171
  2. Bhargava, N., Radaideh, M. I., Kwon, O. H., Verma, A., & Radaideh, M. I. (2025, April 8). On the impact of language nuances on sentiment analysis with large language models: Paraphrasing, sarcasm, and emojis. arXiv. https://arxiv.org/abs/2504.05603
  3. Van Eyghen, Hans (2025). “AI Algorithms as (Un)virtuous Knowers”. Discover Artificial Intelligence. 5(2) 2. doi:1007/s44163-024-00219-z
  4. Marabelli, Marco (2024). AI, Ethics, and Discrimination in Business. Palgrave Studies in Equity, Diversity, Inclusion, and Indigenization in Business. Springer. doi:1007/978-3-031-53919-0
  5. (2025). Worst ad campaigns: AI’s marketing failures. reelmind Blog. https://reelmind.ai/blog/worst-ad-campaigns-ai-s-marketing-failures
  6. Özerdem, H. (2025, October 7). Lessons to be learned from 30 AI fiascos. Medium. https://hakanozerdem.medium.com/lessons-to-be-learned-from-30-ai-fiascos-acc2a61a03ab

Dr. Maha Baz

Dr. Maha Baz

Dr. Maha Baz is a Senior Researcher / Senior Consultant at Behavia and a Strategic Communication Advisor for Excellence. She has 20 years of experience in strategic marketing, communication, and consumer behavior. She holds a PhD in Marketing from the University of Leicester on the subject of ‘Sharing Economy in the Arab world’ and has written a book chapter about it in the “Handbook on the Sharing Economy”. She advises and consults public and private sector clients on marketing with behavioral insights.