AI agents are beginning to change something fundamental in marketing: how consumers make decisions.
Traditional consumer behaviour research largely assumes that the consumer is the cognitive actor. They search for information, evaluate alternatives, form preferences and ultimately make the decision.
AI agents complicate that model.
An AI agent can increasingly do more than provide information. It can interpret a consumer’s request, learn from previous interactions, connect to external tools and, in some cases, execute decisions on the consumer’s behalf.
From AI recommendations to AI decision-making
A useful distinction is between two types of AI agents.
Advisory agents recommend options to consumers, while transactional agents can take action on the consumer’s behalf.
That difference is significant.
With an advisory system, the consumer remains responsible for evaluating the recommendation. With a transactional agent, some of the cognitive and operational work of the decision is delegated to the machine.
This creates a fundamentally different consumer journey.
Instead of:
Consumer → Search → Evaluate → Decide
we increasingly have:
Consumer → Prompt → AI agent processes information → Recommendation/action → Consumer response
And the process does not necessarily end there.
The decision journey becomes a feedback loop
Repeated interactions between consumers and AI agents can create two important feedback loops.
The first is a relational capital loop.
As consumers interact with an agent, their experience can influence trust, confidence and their willingness to delegate future decisions. An agent that consistently understands a consumer’s preferences may receive greater autonomy over time.
The second is an AI learning loop.
The agent can use information from previous interactions to update its understanding of the consumer, potentially changing how it responds to future prompts.
This means the relationship between consumer and agent can become increasingly dynamic.
The consumer’s preferences may also change
Perhaps the most interesting implication for marketers is that AI agents may not simply respond to existing preferences.
They may help shape them.
If a consumer repeatedly asks an AI agent to compare products, narrow down options or make recommendations, the conversation itself can influence how the consumer understands their available choices.
Preferences could therefore become increasingly co-constructed through human–AI interaction, rather than being entirely formed before the search begins.
For marketers, this raises a new set of questions.
- What does a consumer actually prompt an AI agent to find?
- Which information does the agent retrieve and prioritise?
- Which brands become part of the agent’s consideration set?
- What determines whether an agent recommends one option over another?
- How does trust develop between consumers and agents?
- How does an agent’s memory affect future recommendations?
- What happens when an agent can actually complete the transaction?
These questions extend beyond traditional SEO and consumer research.
A new layer of consumer behaviour
The emergence of AI agents means that understanding search behaviour may no longer be enough.
We increasingly need to understand agent-mediated decision journeys: the interaction between the consumer, the prompt, the agent’s processing, the information it retrieves, the actions it takes and the consumer’s subsequent response.
For marketers, this represents a major shift.
The question is no longer simply:
“How do we get the consumer to choose us?”
It is increasingly:
“How do we become part of the decision process when an AI agent is helping – or making – the choice?”
That is likely to become one of the most important questions in consumer behaviour and digital marketing as AI agents move from answering questions to taking action.
