Agentic AI in retail: prepare for a different customer journey.

What is agentic AI in retail?

Agentic AI describes software that can use tools to carry out shopping tasks on someone’s behalf, rather than only provide an answer. Its level of autonomy varies: an assistant might compare options, while a more capable system could take an authorised action. Research and completing a purchase are different levels of responsibility.

What Bain’s research tells us

In its brief published on 14 May 2026, Bain & Company reports the following findings:

• Around 30% of US consumers say they use generative AI for product comparisons and recommendations, according to Bain’s Consumer Lab survey.
• Around half say they are uncomfortable with AI completing an entire transaction without their involvement.
• Three times more trust: Bain reports greater consumer trust in retailer-owned agents than third-party agents for completing transactions.
• Up to 25% of referral traffic, but under 1% of total traffic: Bain describes this pattern for some retailers. Those denominators must not be confused.

These are findings reported by Bain, not Genesis Algorithms research or forecasts for your store. The US adoption figure should not be presented as a UK or global estimate. Source: Bain & Company, “Agentic AI in Retail: How Autonomous Shopping Is Redefining the Customer Journey”, 14 May 2026.

Our practical response: design for an informed arrival

At Genesis Algorithms, we would use this research as a reason to inspect the buying journey, rather than rush to add a chatbot. Consider a hypothetical customer looking for a dining table that seats six, fits a narrow room and can be delivered upstairs. A beautiful image helps, but it cannot answer every constraint.

Write down the questions that customer needs resolved: exact dimensions, extension options, material, weight, assembly and delivery restrictions. Make the answers consistent across product pages and connected systems. This work supports a human visitor even when no AI channel is involved.

A readiness checklist for your store

1. Choose one buying journey. Start with a product category where customers regularly need guidance. Use real support questions to define the problem.
2. Check the facts. Compare product descriptions, variant data, stock information and policy pages. Assign an owner to resolve conflicting answers.
3. Make comparisons useful. Explain meaningful differences between products, including limitations. Avoid filling a comparison table with vague claims such as “premium quality”.
4. Keep the handover clear. When a visitor reaches your store, preserve the selected product and make the next step obvious. Test the journey on mobile.
5. Set boundaries for automation. If you introduce an assistant, decide what it may explain, what needs confirmation and when it should hand over to a person.
6. Run a small pilot. Record a baseline, test a defined change and review customer outcomes before expanding.

How SEO and GEO fit into the work

For this project, we would define generative engine optimisation (GEO) as improving how clearly the store communicates its products and expertise to AI-driven discovery systems. It is a useful working objective, not a promise of inclusion in an answer.

Begin with accessible pages, descriptive headings and factual answers to purchase questions. Where structured data is used, keep it consistent with the visible page. Link supporting guides to the relevant products and services so readers can move from research to action. Do not create dozens of near-identical pages for variations of the same question.

Our SEO and organic growth work and AI search visibility services can sit alongside customer experience improvements. Each addresses a different part of the same buying journey.

Measure decisions, not just visits

Keep referral traffic separate from total traffic in reporting. Track identifiable AI referrals where available, but also review conversion, support requests and the quality of enquiries. A customer may use an assistant during research and return directly later, so attribution will not describe every step.

For an on-site assistant pilot, useful questions include: did it answer accurately, did customers still need support, and did it make the task easier? A high number of conversations is not automatically a successful outcome.

Questions retailers should ask

Should we build a shopping assistant now?

Only after defining a customer problem, the information needed to solve it and a way to evaluate the result. A clearer product page may be a better first investment.

Does AI visibility guarantee sales?

No. Being mentioned, receiving a visit and earning an order are separate outcomes. Set a measure for each rather than treating them as interchangeable.

Where should a Shopify merchant start?

Review a small set of important products and their buying journeys. For platform-specific preparation, read our Shopify AI commerce readiness guide.

Plan the next improvement

Genesis Algorithms can help connect product content, integrations and storefront development around a concrete customer need. Bring us the journey you want to improve, and we can identify the next practical step.

Research credit: Bain & Company. Recommendations and illustrative examples: Genesis Algorithms. This article is independent commentary and does not imply a partnership with or endorsement by Bain.