The retail sector is in a pivotal transformation. Customer expectations keep rising, supply chains are still unpredictable, and most retailers now realize that incremental improvements will not be enough. You can see this in the decisions large retailers are making. Some of these choices make headlines. Others sit quietly within operational updates and are easy to miss unless you follow the sector closely. In today’s blog, we analyze 12 recent moves from major retail & eCommerce players and draw key advice for CTOs and engineering leaders.
What happened: Walmart launched a suite of “super agents” such as customer (Sparky), employee (Associate), seller/supplier (Marty), and internal development (Developer) to consolidate dozens of tools into agentic workflows.
Why it matters: It demonstrates that AI is no longer just an experiment and retailers are embedding agentic systems across front-line, operations, and partner workflows.
Advice for CTOs: Don’t build isolated bots. Design an agent ecosystem: shared data, inter-agent orchestration, unified identity/trust, and cross-domain workflows. Think of your tech stack as hosting many agents that talk to each other.
What happened: Amazon rolled out a new AI shopping assistant called “Help Me Decide,” which uses interactive prompts, product recommendations, and comparisons to speed decision-making.
Why it matters: This shows generative/interactive AI moving into discovery and decision support processes, not just marketing or recommendation engines.
Advice for CTOs: Personalization and discovery are still fertile ground. But ensure your data schema supports intent-based interactions, multi-modal inputs (text/voice/image), and decision-support UX.
What happened: Global retailer Mango initiated streamlining and digitizing all its lab testing protocols across materials and product categories. This centralizes various lab tests (often performed by different labs) into a single system, providing Mango with end-to-end visibility from the initial test request through execution, report generation, and supplier corrective action plans.
Why it matters: Previously, retailers had limited visibility, only receiving a final report. This move demonstrates that CTOs are prioritizing AI-powered data centralization not just for customer-facing experiences, but for governance and quality control in the highly complex global supply chain.
Advice for CTOs: Move critical compliance and quality data out of manual reports and into a consolidated, digitized system; use AI-powered performance analytics to reduce supplier quality defects and integrate this compliance data into your vendor management processes.
What happened: Kroger simultaneously accelerated its profitable hybrid model, expanding partnerships and integrating advanced AI (like Instacart’s Cart Assistant) for faster, cheaper delivery.
Why it matters: The new architecture leverages store proximity and partner APIs, proving that a nuanced, hybrid approach is the sustainable winner over a rigid, single-system bet.
Advice for CTOs: Prioritize building a flexible, API-driven hybrid network that scales via partnerships rather than fixed capital.
What happened: Ingka Group (the largest IKEA retailer) launched IKEA Kreativ, a tool that uses spatial computing and bespoke visual AI models optimized for their unique catalog to allow customers to design and visualize rooms in 3D, moving beyond off-the-shelf recommendation engines.
Why it matters: It highlights that while hype is high, true differentiation comes from data foundation combined with bespoke models with the ability to integrate at scale.
Advice for CTOs: Rather than chasing every trend, pick 1 or 2 high-value use cases (e.g., visual discovery or demand forecasting) and build a minimum viable AI stack that you can scale.
What happened: Leading retailers like Target are utilizing generative AI to run thousands of simulations (e.g., weather events, port delays) and feed the resulting synthetic data back into their core demand forecasting and replenishment models to continuously improve accuracy and response.
Why it matters: This meta-feedback loop makes your AI system self-reinforcing but also increases safety, data drift, and governance issues.
Advice for CTOs: Plan for model lifecycle management: monitoring, retraining, drift detection, and data lineage. Don’t assume “model in production = done”. Our data engineering teams can build pipelines with these features baked in.
What happened: Lululemon has aggressively restructured its data infrastructure to support real-time data streaming and vector databases to power instant, context-aware personalized recommendations on their app, requiring high throughput and a departure from legacy systems.
Why it matters: The sheer growth signals major infrastructure investment ahead: faster inference, vector databases, real-time streaming, and agent orchestration.
Advice for CTOs: Evaluate your tech stack readiness: can your systems support real-time personalization, vector search, and multi-modal indexing? Build a roadmap from legacy to AI-native architecture.
What happened: Home Depot’s strategy to serve both the “Pro” contractor and “Consumer” shopper was slowed by siloed data platforms and inconsistent schemas, requiring a multi-year effort to build a unified data fabric that can serve personalized services across both segments.
Why it matters: Regardless of how advanced your AI models are, if your data foundation is weak, you’ll struggle to scale and extract value.
Advice for CTOs: Prioritize your data platform by unifying data sources (online, in-store, supply chain), ensuring schema consistency, and investing in data governance and integration.
What happened: Following the implementation of complex AI-driven tools, Nordstrom, like many major retailers, announced internal restructuring, creating new, high-value roles such as “AI Product Owner” to bridge business strategy, data science, and technical execution.
Why it matters: As CTOs build AI systems, they must also manage the people-process-technology change: different skills, roles, and governance will apply.
Advice for CTOs: Treat your AI rollout as an organizational transformation, not just a tech project. Build a talent roadmap, retrain staff, and define new roles (agent manager, data steward, AI product owner). Partner with DPP Tech for agile delivery and managed talent services to staff your transformation.
What happened: Walmart announced a partnership with OpenAI to enable customers to make purchases directly within ChatGPT, facilitating “agentic commerce” where shopping becomes conversational and contextual.
Why it matters: This signals a shift from websites + search bars to conversation-first interfaces powered by generative AI and the retailer’s data.
Advice for CTOs: Treat your digital commerce experience as an agentic interface rather than just “web + app”. Ensure your architecture supports conversational inputs, catalog access, real-time inventory, and frictionless checkout.
What happened: Best Buy’s strategy, which contributed to its return to sales growth in Q4 2025, centers on an integrated omnichannel experience with two distinct prongs:
Why it matters: The move proves that the leading retailers are not choosing between digital and physical; they are investing in highly specialized experiences for each channel, unified by a single customer journey and data foundation. High-speed services like curbside pickup are validated by this integrated ecosystem.
Advice for CTOs: Don’t let your “omnichannel” strategy treat stores as mere fulfillment centers. Segment your tech focus: use AI for digital friction points (search, discovery, service routing) and empower in-store employees with specialized tools and physical setups to deliver human expertise. Ensure the resulting data flows back to the core data platform.
What happened: Macy’s is moving dynamic pricing beyond the website to their physical stores, using Electronic Shelf Labels (ESLs) to adjust prices in real time based on local demand, inventory levels, and competitor pricing—a strategic shift from the traditional, slow markdown cycle.
Why it matters: These use cases have matured and are transitioning from pilot to production – CTOs should choose their “first movers”.
Advice for CTOs: Select 1–2 high-ROI micro-use cases for 2026, deliver them quickly, measure the results, and then scale.
If your organization is a large retail or eCommerce business looking to build or scale AI/data/technology capabilities, from foundation to agents to omnichannel transformation, DPP Tech is ready to partner with you.
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