{"id":51362,"date":"2026-09-22T12:41:37","date_gmt":"2026-09-22T09:41:37","guid":{"rendered":"https:\/\/sbs.strathmore.edu\/?p=51362"},"modified":"2026-09-22T12:41:37","modified_gmt":"2026-09-22T09:41:37","slug":"can-ai-personas-replace-real-consumers-why-the-future-of-market-research-may-be-hybrid","status":"publish","type":"post","link":"https:\/\/sbs.strathmore.edu\/fr\/can-ai-personas-replace-real-consumers-why-the-future-of-market-research-may-be-hybrid\/","title":{"rendered":"Can AI Personas Replace Real Consumers? Why the Future of Market Research May Be Hybrid"},"content":{"rendered":"
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    For years, market research has asked a deceptively simple question: what do consumers want? And today, artificial intelligence is offering a new way to ask it. Instead of waiting for a focus group to assemble or a survey to reach thousands of respondents, organisations can now generate synthetic consumers AI-generated personas designed to simulate how different audiences might respond to a product, service, campaign, or idea. The appeal is obvious: speed, scale, and cost. But underneath that promise lies a harder question.<\/p>\n

    \n

    Can a simulated consumer really tell us what a real consumer thinks, feels, and does? That was the foundation of the conversations at the Marketing Roundtable, hosted on Friday, 18th September 2026, by the Strathmore Marketing and Communication Excellence Hub at Strathmore University Business School. Held under the theme “Synthetic Consumers: Is AI Persona the Substitute for Market Research?”, industry practitioners and experts examined what synthetic consumers can do, where they fall short, and what responsible AI-driven research should look like.<\/p>\n<\/blockquote>\n

    By the end of the discussion, one thing was clear: the answer wasn’t about choosing sides. It was about figuring out how to combine the best of both worlds.<\/p>\n

    Joel Rao, CEO of Dentsu Kenya, opened with the drivers behind the shift: speed, scale, and cost. Predictive models trained on historical data can approximate how people are likely to react and, as he noted, the gap between real and synthetic audiences is closing fast.<\/p>\n

    But Rao drew a sharper distinction that framed the rest of the discussion: evidence is not the same as empathy. AI can approximate patterns, but it misses subgroup realities, emotional nuance, and the fast-moving nature of culture. That distinction matters more than it might first appear, and it’s most visible when you move from what consumers say<\/em> to what they experience.<\/p>\n

    One conversation on sensory science illustrated the gap well. A synthetic consumer might predict that someone would describe a conditioner as making their hair feel “soft.” But actually, using the product reveals things a text-based answer can’t capture: texture, scent, a sense of cleanliness, or any number of sensory details.<\/p>\n

    Annejoy Muhoro from East Africa Breweries Limited, grounded this in practice: the company still relies on physical liquid testing and tasting to account for what she called “Kenyan bias” the local, contextual detail a model trained elsewhere simply won’t have. Angela made a related point from the consumer side: African consumers still prefer human interaction. Her suggested dividing line was useful, complex, rational tasks can reasonably be offloaded to AI, but anything requiring context, intuition, or emotional nuance should stay with people.<\/p>\n

    It’s worth noting the reverse is also true. Ndirangu reminded the room that human respondents don’t always tell the truth either \u2014 a useful check against treating “real” research as automatically more honest, even where it remains more complete.<\/p>\n

    For companies operating in Africa, a further question emerges: whose reality does the underlying data capture? Moses Kemibaro laid out six criteria he uses to evaluate whether AI is fit for a given research task, and several cut to the heart of this problem. Data quality comes first \u2014 if it isn’t structured and validated, he argued, it shouldn’t be used at all. Just as important is grounding: avoiding the default assumption that “the internet” means the Western internet, when models trained that way can be blind to everyday realities like a Mama Mboga<\/em> running her business from a smartphone, or customers carrying two phones out of necessity rather than preference. He also pointed to geographic and cultural nuance \u2014 the differences between Ethiopia and Kenya, or between Kiswahili and Sheng \u2014 as exactly the kind of detail a generic model will flatten.<\/p>\n

    Kemibaro’s remaining criteria; benchmarking, reproducibility, and liability and ownership, \u00a0point to a question every organisation eventually has to answer: when a decision goes wrong, who is accountable for explaining it to leadership?<\/p>\n

    If the underlying data is poorly prepared, outdated, or unrepresentative, AI won’t solve that problem. It will simply reproduce it faster.<\/p>\n

    Caroline, representing the Office of the Data Protection Commissioner, was direct on this point: even though synthetic personas are artificial outputs, real personal data is used to build them, which brings the entire exercise under Kenya’s Data Protection Act. Consumers need to know when their data is being collected and shared with third parties \u2014 and that consent has to be explicit, not buried in the fine print of lengthy terms and conditions.<\/p>\n

    The risks of getting this wrong aren’t hypothetical. Joel Rao cited Spotify’s deletion of 75 million bot-streamed tracks as a cautionary example: bad actors had used cheap generative AI tools to flood the platform with near-identical or ultra-short tracks \u2014 ambient noise loops, in many cases \u2014 specifically to game the streaming algorithm and siphon royalties away from real musicians. It’s a reminder that synthetic content, left unchecked, doesn’t just produce bad research; it can be actively exploited.<\/p>\n

    Responsibility doesn’t disappear just because a decision was AI-assisted. If an AI system produces a result that looks credible but is actually wrong, the responsibility for acting on it sits with the people who use it \u2014 which is exactly why the panel’s closing discussion flagged a formal Cost-Benefit Analysis as a prerequisite before adopting synthetic consumers, not an afterthought.<\/p>\n

    If synthetic consumers are neither a replacement for research nor a gimmick, what are they for?<\/p>\n

    Moses Mutua offered Safaricom’s approach as a working example: the company uses digital personas and digital twins for specific, well-defined use cases \u2014 mapping customer journeys, measuring task completion rates \u2014 while disclosing upfront whenever a project relies on a digital persona. Critically, Safaricom keeps a “human-in-the-loop” to continuously validate the outputs, which he argued keeps the associated risk low.<\/p>\n

    That human role is becoming more demanding, not less. Daniel Oseman raised a concern about “cognitive surrender\u201d the risk of over-relying on automated outputs but argued that AI raises the bar for critical thinking rather than removing the need for it. Annejoy Muhoro pushed this further, in blunter terms: marketers who conduct research merely to confirm existing assumptions, she suggested, should reconsider their careers. Her broader point was that EABL’s core assets are its brands, not its factories and decision-making responsibility for those assets cannot be handed off to a machine.<\/p>\n

    The panel also touched on a broader version of this concern, beyond marketing itself: both Annejoy and Joel Rao noted worries about screen time and its effects on cognitive development and critical thinking, particularly in education \u2014 a reminder that questions about AI’s cognitive trade-offs extend well beyond the research industry.<\/p>\n

    Perhaps the clearest takeaway from the discussion is also the simplest: the future of market research won’t be AI-only nor will it look like the past.<\/p>\n

    The panel’s closing framing captured this precisely: the core challenge isn’t choosing between human research and AI, but knowing when observation is the right approach and when prediction will do. Confidence in these models, the panel agreed, comes from trust and rigour rather than sheer data volume \u2014 and that trust has to be maintained continuously. Without ongoing real-world input, models risk drifting into generating data based on other models’ outputs rather than real human behaviour.<\/p>\n

    That, ultimately, is the shared responsibility the panel kept returning to: AI adoption in market research isn’t a one-off technology decision but an ongoing partnership, one where the efficiency gains are worth reinvesting into rigorous data validation rather than treated purely as savings.<\/p>\n

    The question is no longer whether AI can replace market research. It’s about using AI in a way that makes research faster, more efficient, and leaner without losing the humanity that makes it useful in the first place.<\/p>\n<\/div><\/div><\/div><\/div><\/div>","protected":false},"excerpt":{"rendered":"","protected":false},"author":47,"featured_media":51371,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[39],"tags":[624],"class_list":["post-51362","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news","tag-smace"],"acf":[],"yoast_head":"\nCan AI Personas Replace Real Consumers? 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