AI Trends Shaping the Rest of 2025 in Retail

Mid-Year Checkpoint: Where AI in Retail Stands 

We’ve officially crossed the halfway mark of 2025—a perfect moment for retail operators, brand owners, and ecommerce strategists to pause and assess: is your AI adoption driving tangible results, or still sitting in the idea folder? 

From high-performing Amazon sellers to Shopify-first startups and mass retailers navigating omnichannel growth, one thing is clear—AI isn’t just “on the roadmap” anymore. It’s already shaping how we sell, communicate, and operate. But it’s also easy to get distracted by noise. 

That’s why I’m sharing the trends I believe are truly worth your attention for the second half of the year. These are based on what we’re seeing inside brands, what platforms are launching, and what technologies are quietly scaling in the background. 

What We’re Seeing Across Retail 

AI is no longer about hypothetical potential. It’s becoming an operational infrastructure. The smartest brands are: 

• Building specific use cases with measurable ROI (not just experimenting with flashy tools) 

• Replacing manual workflows with automated agents, especially in pricing, customer support, and inventory forecasting 

• Connecting AI to conversion, retention, and team efficiency—not just creative outputs 

But with this momentum comes complexity: inconsistent data, hallucinations, governance gaps, and tech overload. Adoption only works when there’s clarity and alignment. 

Amazon: Driving Relevance Through Intelligence 

Amazon has stayed ahead by investing in domain-specific foundation models. Their latest update? Rufus, an AI shopping assistant trained on Amazon’s full catalog and customer reviews. Launched in early 2025, Rufus brings a conversational context to product discovery—helping shoppers go from “I need a router for outdoors” to the exact spec that works for their setup. 

We’re also seeing meaningful movement in hyper-personalization: PDPs and search results adapting dynamically to individual behavior. Combined with refreshed A+ content and AI-enhanced creative testing, this is setting up a new bar for performance. 

And behind the scenes, agents are picking up speed. Amazon Bedrock Agents can now support tasks like real-time pricing adjustments and product matching. For brands, this means greater automation in decision-making and better integration across global marketplaces. 

What to watch: 

• Rufus-style AI experiences expanding beyond search and into branded stores. 

• Programmatic A/B testing on Amazon’s back end (via Manage Your Experiments + custom AI tools). 

• Higher-performing listings powered by seasonal and high-intent keyword layering. 

Shopify: From Toolstack to Tailored AI 

Unlike Amazon, Shopify is not building foundation models—but it’s rapidly enabling AI-powered commerce through integrations. In June 2025, Shopify announced deeper partnerships with tools like Hatch AI, Rebuy, and NeuralMagic for predictive personalization, product bundling, and visual merchandising. 

What we’re seeing work: 

• Custom AI-powered bundles based on shopper behavior 

• Predictive search improvements using external data 

• Real-time copy generation on PDPs tailored to traffic source and seasonality 

Shopify brands don’t need to adopt every new tool. What matters is choosing the ones that: 

• Strengthen conversion across devices 

• Improve customer experience with less operational lift 

• Help small teams scale creative and merchandising without losing brand integrity 

That’s the difference between shiny AI and scalable growth. 

Walmart + Target: Scaling with Control 

While Amazon and Shopify focus on speed and personalization, mass retailers are prioritizing control. Walmart’s investment in generative AI has taken shape through better on-site search and smarter replenishment forecasting. Target, meanwhile, is leaning into AI-driven audience segmentation and product matching across its DTC brand portfolio. 

Both retailers are: 

• Tightening supply chain and logistics using predictive models 

• Elevating product discovery through curated search experiences 

• Testing generative media for in-store signage, seasonal campaigns, and social ads 

What sets these players apart is their emphasis on governance. AI is implemented with guardrails—from security protocols to internal validation layers—minimizing reputational and compliance risk. 

If your brand is selling into these channels, it’s worth asking: is our data AI-ready for these partnerships? Are we delivering creative assets that match how Walmart and Target are optimizing their own platforms? 

Common Barriers to Adoption 

While enthusiasm is high, implementation gaps remain. Across teams, we’re still seeing: 

• Data fragmentation: Without clean, structured data, even the best models deliver noise. 

• Hallucinations: LLMs generate incorrect information, especially without retrieval-augmented generation (RAG). 

• Governance blind spots: Security, access, and usage policies lag behind. 

• Misaligned pilots: AI experiments run parallel to actual workflows, creating silos instead of solutions. 

The fix isn’t more than AI. It’s better AI strategy—aligned to business KPIs, integrated with data hygiene practices, and executed with cross-functional ownership. 

My Take 

The next six months will define whether AI becomes your competitive edge—or a missed opportunity. 

The most successful teams I’ve worked with don’t chase every new trend. They choose one or two high-impact use cases, get buy-in across the org, and invest in consistent execution. Whether it’s an Amazon listing assistant, a Shopify upsell model, or an internal forecasting agent—the shift happens when AI is embedded, not just tested. 

If your retail strategy needs a refresh—or you’re unsure which tech move makes sense—I’d be happy to share what we’re seeing on the ground. AI won’t solve everything. But done right, it can absolutely help your team do more with clarity, consistency, and confidence. 

Let’s build smarter.