The Maze: The numbers beside electronics, travel and food measure AI’s influence on shopping decisions. They come from Zinklar, a consumer research provider. Its Agentic Commerce Index, or ACI, combines how much people use AI while buying with how much they trust it in that category. A higher score signals stronger combined AI influence. It is an index score, not the percentage of people using AI or a sales-growth rate. Hernan Sanchez Neira’s post uses it to explain why brands need different approaches across categories.
ACI brings use and trust together. The first part measures how widely people use AI, how often they use it, and how much of the purchase process they hand over to it. The second measures their trust in AI for that category. Think of someone asking an assistant to compare headphones, narrow the options or recommend a model: these are examples of AI involvement, even if the person still makes the final decision and pays. Frequent use and high trust together suggest AI has a stronger role in choosing what to buy. The index does not mean an agent completed the checkout.
Read 6.9 as an index value, not 69%. Electronics scores 6.9, travel 6.7 and health/wellbeing 6.4, against a listed average of 5.5 in the US 2026 exhibit. Non-alcoholic beverages scores 4.2. This puts electronics above the benchmark and beverages below it on the combined measure. It does not mean 69% of electronics shoppers use AI, or that electronics receives a particular amount of extra sales. The post does not disclose the formula or full scoring scale, so we cannot call it a score out of ten. Use the values to compare categories within this study.
The circles show a different part of the story. Moving right means greater trust; moving up means greater AI involvement across the shopping process. The numbers inside the circles identify categories in the adjacent table: 10 is electronics, 22 is travel and 21 is health/wellbeing. They are category labels, not ACI scores. Colours classify purchases by high or low involvement and rational or emotional choice. Sanchez Neira calls nine categories “Agentic Masters” because of their position in the trust-and-use framework. ACI alone does not decide that grouping: restaurants qualifies at 5.6, while streaming services at 5.8 does not. The axes have no numerical ticks, and the recreated marker positions are visually approximated.
Why it matters: AI can shape which brands get considered. Sanchez Neira describes three buying paths. In the human path, people choose through their own knowledge and preferences. In the hybrid path, a person asks AI for help and the assistant shapes the shortlist. In the agentic path, product attributes need to be readable, comparable and verifiable by a machine. The business implication is that memorable advertising may not be enough if an assistant cannot understand what a product offers. For example, a headphone brand could test whether AI accurately compares its features and includes it among suitable choices. That is an illustrative test, not a result measured by ACI.
Why it matters: ACI helps brands decide where to investigate AI’s role in shopping first. In higher-scoring categories, check whether assistants can find and correctly compare your products, alongside the marketing aimed at people. In lower-scoring categories, first establish which shopping tasks customers actually use AI for. The exhibit states a US base of 163, with limited public methodology. Treat the scores as a guide to category differences, then measure real customer choices and purchases to judge whether a change works.
Sources: Zinklar Agentic Commerce Index, via Hernan Sanchez Neira, USA, 2026. The source post defines ACI; its full formula and scale are not disclosed.
Images: Cover AI-generated
