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Retail teams adopted AI fast. But now, it’s about applying strategy. To learn how the industry was moving past the hype and into execution, eTail Insights surveyed senior retail and ecommerce leaders for The 2026 retail AI revolution in commerce. The report includes a snapshot of what retail teams are using now, what results actually support those choices, and what still looks promising but hasn’t proven reliable just yet. The result is a baseline that retailers and brands can use for planning, grounded in how peers describe strategy, effectiveness, and readiness.
What might be surprising—or relatable—is that only 4% of organizations say they have a comprehensive AI strategy in place. Most teams still build the playbook as they go. 54% say they’re developing an AI strategy while 39% say they’re experimenting.
Adoption is widespread, confidence is not. Only 7% rate their AI implementations as very effective. Most respondents land in the middle: they call AI “somewhat effective.” 36% say their AI has been minimally effective, and 4% say it has not been effective.
In practice, AI projects can move faster than the plan that’s meant to guide it. What does this mean for AI effectiveness?
Where AI already runs, and why results vary
AI already runs through everyday ecommerce, especially in personalized product recommendations, inventory management and demand forecasting, and customer data analytics and insights. Most organizations rate their capabilities in these areas as at least somewhat advanced.
That’s not the surprising part. Retail doesn’t debate whether to start with AI anymore. Many teams already operate with it in the mix but want to turn widespread use into repeatable results. The report starts to answer that by looking at where AI is already making a clear difference.
Where leaders see results today
Instead of predictions, the survey asked for outcomes that retailer and brand leaders can point to right now. The biggest reported impacts are in inventory management where 60% reported improvement, with 58% noting demand forecasting. Other areas of impact include customer value, customer service costs, and conversion.
Only a small share rate implementations as very effective. Mariko Davison, a Senior Technical Account Manager at Rithum, puts a practical reason behind that gap: AI acts like a magnifier, so messy inputs create messy outcomes. One example she sees often is incorrect titles, descriptions, or attributes pushing an item into the wrong product type, which then triggers missing required fields, listing and variation errors, and sometimes even post-sale issues.
The three challenges leaders cite most
When leaders explain why AI progress slows after early wins, three issues come up most often:
- 50% data privacy and compliance
- 49% high costs
- 47% customer trust
These constraints help explain why strategy maturity remains low, with only 4% reporting a comprehensive AI strategy.
Personalization stays a top customer experience priority
Personalization remains a top customer experience priority, with 55% of the survey respondents ranking it as high-or-top tier. Personalized product recommendations also rank among the most advanced AI uses reported. Many teams already run them, but they still want stronger results.
Agentic AI exposes a readiness gap
Agentic AI refers to systems that can surface products and take shopping actions on a shopper’s behalf. The survey asked leaders how prepared they are to make products discoverable in these environments. The answer: Readiness for agentic discovery is low.
Only 2% say they are fully prepared. 56% say they are partially prepared, and 42% say they are still in early exploration.
Retail and brand leaders expect discovery and buying behavior to change, but most say they are not prepared yet.
Where budgets go next, based on planned investment
According to the survey, budget priorities are still fundamentals-first. 53% name inventory and demand management as a top investment area next year, with supply chain at 45% and customer analytics close behind. This focus matches the biggest reported gains in inventory and forecasting.
What respondents expect next
In open responses, respondents describe AI as speeding up operations, improving personalization, reshaping loyalty programs, and making the customer journey faster and more data-driven. The emphasis stays practical: efficiency and personalization first, then faster decisions.
While AI adoption is high, readiness lags. Use the benchmarks to test where you are strong and where you are exposed before you scale. Download The retail AI revolution to pressure-test what comes next.
