The rise of the algorithmic consumer
Earlier this year, I witnessed a transformation in consumer behaviour – one that began at home. My husband, a self-confessed tech enthusiast, embarked on a quest to buy a new laptop. Like many, he dove into YouTube reviews, Reddit threads, and endless browser tabs, only to find himself paralysed by too many choices. The breakthrough came when he turned to an AI assistant, Perplexity, and simply described his needs. Within minutes, the AI synthesised data from forums and reviews, presenting a clear recommendation. The decision was made – swiftly and confidently.
This moment was more than a personal anecdote; it was a preview of a seismic shift already underway. Consumers are increasingly partnering with AI to make decisions, evolving into what I call "algorithmic consumers." The question for market researchers is urgent: Are we ready for a world where our customers might not be entirely human anymore?
AI adoption in shopping: the data
AI-powered shopping is on the rise, with more than one-third of global consumers now using artificial intelligence to assist their purchases. According to the Adyen 2025 Annual Retail Report, usage has surged by 47% in just the past year, signalling a rapid shift in how people discover and decide on products. More consumers are replacing traditional search with AI-driven recommendations, and this trend is not limited to tech-savvy early adopters. Gen Z, in particular, is leading the charge, with over 50% already using AI for product discovery.
The spectrum of AI-assisted decision-making
The evolution of the non-human consumer unfolds along a spectrum of independence:
- The Assistant: AI summarises reviews; the human makes the final choice.
- The Collaborator: AI suggests options; the human approves.
- The Delegate: Routine tasks are delegated to AI (think Amazon’s Subscribe & Save).
- The Autonomous Consumer: AI agents make decisions independently, with minimal human intervention.
Most consumers today operate at the Collaborator level, but the future points toward increasing autonomy for AI agents.
Outsourcing decisions and the trust paradox
As consumers outsource more of their decision-making to AI, the psychology of choice is fundamentally altered. While AI can optimise for value and efficiency, core marketing concepts like trust, agency, and satisfaction are being redefined. Notably, there is a "trust paradox": while many embrace AI for product recommendations, a significant portion still prefers human advice for experiential purchases – things they can feel.
This creates a clear distinction between "search products" and "experience products." For search products defined by objective, comparable attributes (electronics, insurance policies, basic groceries), 39% of consumers and over 50% of Gen Z already use AI for product discovery, according to Salesforce's Connected Shoppers Report 2025.
For experience products that must be personally lived to evaluate (holidays, restaurants), consumers prefer human advice from those with lived experiences. This split has major implications: search products benefit substantially from AI agents, while experience products may not.
The satisfaction curve: finding the sweet spot
Contrary to popular belief, more automation does not always lead to greater satisfaction. At low levels of autonomy, AI is perceived as rigid and frustrating. At very high levels, it can threaten consumer agency, triggering resistance. The optimal balance – where AI acts as a powerful collaborator – enhances capabilities without eroding control.
Barriers to adoption: trust, hurdles, and hype
Despite the clear trajectory, widespread adoption of algorithmic consumers is not guaranteed. Poor data quality can lead to AI "hallucinations" – confidently wrong recommendations that erode trust. Data privacy concerns and scepticism about AI’s motives persist, with 41% of consumers expressing no trust in AI shopping assistants. Technical barriers are also significant; 44% of organisations lack robust data management systems. Gartner’s 2025 Hype Cycle places AI agents at the "Peak of Inflated Expectations," highlighting the gap between hype and practical deployment.
The market research challenge
Market research faces a fascinating challenge: how do we understand the non-human consumer? Traditional methods are built on human behaviour, but what happens when the decision-maker is an algorithm? The answer lies in adopting new principles:
1. Transparency: Researchers must access and understand the AI’s "decision log" to measure the transparency gap.
2. Multilayer analysis: Study the human’s goals, the AI’s decision process, and the interaction between them.
3. Dynamic adaptation: Research must be continuous and adaptive, keeping pace with rapidly evolving AI systems.
Ethical considerations
The shift to non-human consumers raises complex ethical questions. Principles of fairness, accountability, and transparency must be rigorously applied, extending beyond data privacy to the governance of AI-driven decision-making.
The road ahead: strategic questions
As retail undergoes agentic transformation – with 4 in 10 retailers piloting Agentic AI – the market research community must grapple with critical strategic questions:
- Who is our real research subject when AI influences purchase?
- Can traditional methods like surveys and qualitative techniques be used to understand AI decision-making?
- How do we prepare for multiple possible futures?
- What are our ethical responsibilities when researching non-human agents?
Evolving with the consumer
The evolution of the non-human consumer is underway, and market research must evolve in tandem. By embracing transparency, multilayer analysis, and dynamic adaptation, researchers can unlock insights into the new landscape of AI-assisted decision-making. The challenge is not just technical, but philosophical and ethical – demanding new frameworks and a readiness to explore multiple futures.
Contact our Kantar experts to learn how your brand can talk to the non-human consumer (LLM GEO), email moc.ratnak@ASofni.
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