When it comes to agentic AI, Walmart is getting small. The company is choosing to deploy agents that are focused on performing very discrete functions rather than AI agents that attempt to be “AIs-of-all-trades.” This strategy could provide a roadmap for others, especially organizations currently struggling to get value from their AI deployments.
The retail giant’s approach comes as new research from The Futurum Group reveals that 89% of surveyed CIOs now consider agent-based AI a strategic priority, with the technology expected to drive up to $6 trillion in economic value by 2028. Unlike traditional AI systems that require extensive human oversight, agentic AI operates with greater autonomy, making decisions and executing complex workflows independently.
Targeted Implementation Over Broad Deployment
Walmart’s strategy stands in contrast to many enterprise AI initiatives that attempt to solve multiple problems simultaneously. The company says its extensive early testing has proven that agents work most effectively when deployed for specific tasks, with their outputs then orchestrated together to solve complex workflows.
“Our approach to agentic AI at Walmart is surgical,” Hari Vasudev, chief technology officer, Walmart U.S., stated in outlining the company’s strategy. This method has already yielded measurable results across several operational areas, from merchant tools that automate time-intensive data entry and analysis to the Trend-to-Product system, which has shortened traditional fashion production timelines by as much as 18 weeks.
AI agent specialization addresses a critical challenge identified in Futurum’s research, which found that 60% of do-it-yourself AI initiatives fail to scale past pilot stages due to unclear return on investment. Walmart’s focus on specific use cases appears designed to avoid this pitfall.
Multi-Agent Orchestration Drives Customer Experience
Central to Walmart’s agentic AI strategy is multi-agent orchestration, particularly evident in its GenAI-powered shopping assistant. The system combines the retailer’s proprietary, retail-specific large language model with other large language models (LLMs) to create highly contextual responses tailored to individual customer needs.
The shopping assistant represents a sophisticated implementation of what The Futurum Group identifies as a leading trend, where commercial AI agents deliver a faster return on investment (ROI) than custom-built solutions. Salesforce’s Agentforce achieved a 10/10 performance rating in Futurum’s analysis, with users reporting a ROI in as little as two weeks. Meanwhile, Microsoft Copilot Agents reduced customer service response times by 30-50%.
Walmart’s Customer Support Assistant demonstrates similar autonomous capabilities, with agents already routing, resolving, and increasingly acting independently to automate routine tasks. This aligns with Futurum’s finding that agent-based AI excels in automating enterprise workflows with minimal human intervention.
Anand Logani, EVP and chief digital officer at global data analytics and digital solutions provider EXL, agrees that multi-agent workflows can transform customer interactions. “If you were processing something and that took seven steps and four hands, that workflow can get completely disrupted and reimagined right at the stage when the customer is contacting you, regardless of the channel they choose (call center or web portal), you can use multiple agents to understand the intent of the customer and to beginning working on the right solution,” he said.
Preparing for the Personal Shopping Agent Revolution
Looking ahead, Walmart is positioning itself for what it views as the inevitable rise of personal shopping agents—AI systems that will act on behalf of individual consumers. This requires addressing two critical components that CIOs should note for their own strategic planning.
First, customers must effectively train their agents by providing specific parameters, including budgetary limits, brand preferences, and location data. Second, retailers and technology providers must establish communication bridges that enable personal shopping agents to interact with internal systems.
This evolution will fundamentally change how businesses approach customer acquisition and engagement. Traditional marketing strategies focused on visual appeal may become less effective as agents prioritize data-driven decision-making over emotional responses.
Governance and Security Challenges Demand Attention
Despite the promising opportunities, Futurum’s research highlights significant implementation challenges that should concern security-minded CIOs. A substantial 78% of surveyed CIOs cited security, compliance and data control as primary barriers to scaling agent-based AI.
Much of those security challenges indeed stem from maintaining the ability to keep data sources and AI systems secure from direct breaches, but AI agents orchestrated in this way can potentially fall victim to fraud. “Think about customer service agents interacting with customers. The “customer” calls in, and they want to change a product shipment to a new location, or they want to return something. If all of these processes are automated, how do you detect attempts of fraud or theft,” said Tim Crawford, strategic CIO advisor at the consultancy AVOA.
Walmart acknowledges these concerns, emphasizing that accuracy remains paramount in its deployment strategy. The company is exploring ways to leverage agents for critical governance functions and checks and balances, often preferring a copilot model where humans and AI work as teams.
IBM Watsonx Agents leads in governance capabilities with a 10/10 score in Futurum’s evaluation, offering enterprise-ready features including role-based controls, compliance auditing and AI explainability safeguards. This focus on governance will become increasingly critical as autonomous systems take on more decision-making responsibilities.
Strategic Agentic AI Implementations Implications
Walmart’s measured approach offers several lessons for evaluating agentic AI investments. The emphasis on specific use cases, rather than broad automation, provides a more straightforward path to demonstrable ROI while reducing implementation complexity.
As the agentic AI market prepares for significant expansion—with projections reaching $41.32 billion by 2030, according to market research—CIOs must strike a balance between innovation and practical governance considerations. The technology’s potential to revolutionize enterprise operations is substantial, but success requires careful attention to security, compliance and measurable business outcomes.
Walmart’s experience suggests that enterprises prioritizing strategic implementation, precise challenge identification and collaborative ecosystems will be best positioned to capitalize on the transformative potential of agentic AI. For CIOs, the question is no longer whether to adopt agent-based AI but how to implement it strategically and securely.
