Walmart is accelerating a bold transformation that ties scale, speed, immediacy, and data-driven insight directly to every in-person and virtual customer interaction. The company’s Emerging Tech group has long partnered with conversational AI while leaning on augmented reality, spatial computing, and spatial awareness technologies to deliver increasingly seamless customer experiences. At the heart of Walmart’s strategy is a suite of conversational AI use cases—from shopping assistance and customer care automation to boosting associate productivity—that are redefining what it means to shop and interact with a retailer in a digital-first world.
This evolution is underpinned by a clear belief: conversational AI should enable every Walmart department and facility worldwide to speak retail, creating a consistent and enriched experience for customers and a more efficient, informed workflow for associates. The progress to date has yielded tangible results in customer care automation, where AI-powered capabilities have shortened the time agents spend with customers while handling a large volume of interactions. In practical terms, Walmart reports that tens of millions of customer interactions are now supported or enhanced by conversational AI, and a significant portion of its front-line workforce engages with these tools to perform their daily duties more effectively.
As Walmart continues to expand the reach and sophistication of its conversational AI platform, leadership emphasizes the importance of a scalable, adaptable architecture that can accommodate diverse markets, languages, and operating environments. The company’s approach combines standardized platforms with country-specific customization, enabling rapid deployments without sacrificing language accuracy or cultural relevance. The aim is to remove friction at every customer touchpoint, from the moment a shopper begins researching products to the moment they complete a purchase, and to equip store associates with instant, context-rich guidance that helps them serve customers more efficiently.
Below is a detailed exploration of Walmart’s global conversational AI program, its organizational approach, the concrete use cases it has deployed, the lessons learned from international expansion, and the ongoing considerations around trust, cost, and risk management as retailers increasingly rely on large language models and related AI technologies to deliver real-time, personalized experiences.
Walmart’s global conversational AI ambition and operational model
Walmart’s overarching ambition with conversational AI is to propel its vast multinational operation toward a model where every department, every process, and every customer interaction can be enhanced through intelligent dialogue and contextual understanding. The practical objective is twofold: to improve the customer experience by delivering faster, more accurate assistance and to raise associate productivity by providing ready access to information, workflows, and scheduling. Achieving this requires a deliberate blend of technology, process, and data culture that scales across thousands of stores, distribution centers, and digital touchpoints worldwide.
A key component of the operational model is a standardized, scalable platform for conversational experiences. This platform is designed to support a broad range of use cases while enabling rapid iteration, localization, and adaptation to local needs. The Emerging Tech team leverages a combination of natural language understanding and processing capabilities, including large language models, to interpret customer intents, manage complex dialogues, and generate helpful responses in real time. This foundational capability supports both customer-facing interfaces—such as chat experiences and voice-enabled assistants—and internal tools used by associates for daily tasks.
The enterprise-wide effort hinges on a modular architecture that allows different parts of Walmart to “speak retail” in a consistent fashion. That means a common language for intents, entities, and workflows, plus standardized data schemas and telemetry that make it possible to measure performance, understand customer sentiment, and continuously optimize the experience. The result is not a single monolithic application but a family of closely related services that can be composed and customized to fit distinct departments, product categories, and regional requirements without losing the coherence of the overall strategy.
From a customer perspective, Walmart’s goal is to deliver faster, more precise assistance at each stage of the shopping journey. For example, customer care automation has demonstrated measurable improvements in efficiency by handling a large volume of inquiries without adding headcount. In practical terms, this means faster response times, more consistent information, and the ability to shift human resources toward higher-value activities that require empathy, complex problem solving, or specialized domain knowledge. The end result should be a smoother, more intuitive shopping experience that reduces the friction customers often encounter when seeking help or trying to locate products.
Internally, the same platform underpins the productivity gains Walmart seeks for its associates. By providing tools that can interpret questions, fetch relevant information, and guide workflows, the company has been able to streamline routine tasks and free up time for associates to focus on higher-impact activities. In aggregate, these improvements contribute to faster service, fewer miscommunications, and a more responsive retail environment. The enterprise-wide adoption of conversational AI also creates opportunities to capture and leverage insights from customer interactions, feeding back into product discovery, merchandising, and service design to close the loop between customer needs and Walmart’s offerings.
The architecture that enables this transformation emphasizes security, governance, and ethical use of AI. Walmart’s approach includes safeguarding sensitive customer data, ensuring consistent policy enforcement across channels, and maintaining transparent explanations about AI-driven interactions. This focus on trust is essential in a retail context where customers expect reliable, respectful, and privacy-conscious experiences. The company continuously evaluates risk and implements safeguards to mitigate issues such as overly confident responses or content that could confuse or mislead users. By combining robust technical controls with clear user guidance and human oversight, Walmart aims to deliver AI-driven experiences that customers can rely on with confidence.
In terms of scale, Walmart’s global footprint—serving hundreds of millions of customers across diverse markets—poses unique challenges and opportunities. The platform must handle variations in languages, dialects, regional shopping patterns, and regulatory landscapes while maintaining the speed and reliability customers expect. The Emerging Tech team addresses this by building a flexible, modular foundation that supports rapid localization and continuous improvement. The approach enables the company to roll out new capabilities across countries or regions with minimal friction, while preserving a consistent user experience that reflects Walmart’s brand and service standards.
Key performance indicators associated with this strategic direction include measures of customer satisfaction, response times, resolution rates, and the volume of inquiries handled by AI-enabled channels. In addition, Walmart tracks associate productivity gains, the adoption rate of AI-assisted workflows, and qualitative indicators such as the perceived ease of use and perceived usefulness of conversational tools. The balance of these metrics informs ongoing investment decisions and helps determine where to extend capabilities next—whether into new channels, domains, or geographic markets. Ultimately, the objective is to harmonize speed, accuracy, and empathy in every conversational interaction, so customers feel understood and guided at each step of their journey.
Beyond execution, Walmart emphasizes a culture that values experimentation, learning, and speed. The Emerging Tech team’s mandate includes rapid prototyping, cross-functional collaboration, and the ability to translate retail expertise into AI-enabled experiences quickly. This cultural emphasis is crucial for sustaining momentum as technology evolves and as the company expands into additional use cases, languages, and regions. The company’s leadership underlines that successful scale is not only about technology—it requires disciplined governance, robust data practices, and a commitment to customer-centric design that prioritizes usefulness and trust.
In practice, this translates into a continuous loop of ideation, experimentation, evaluation, and deployment. Teams are encouraged to test new ideas in controlled environments, measure outcomes in real-world settings, and iterate based on feedback from customers and frontline staff. The result is a dynamic capability that can adapt to changing market conditions, new products, and evolving customer expectations. This disciplined, iterative approach is a core driver of Walmart’s ability to maintain momentum while expanding the breadth and depth of its conversational AI capabilities across a global retail enterprise.
The Emerging Tech group: strategy, structure, and culture of rapid experimentation
The heart of Walmart’s AI-driven transformation lies in the Emerging Tech group, a cross-functional entity that collaborates across the company to design, deploy, and refine conversational experiences at scale. This group operates at the intersection of technology, operations, and customer insight, combining advanced AI techniques with deep retail knowledge to deliver practical, high-value outcomes. The group’s strategy centers on building a standardized yet flexible platform, enabling teams from different regions and business units to create their own conversational experiences without duplicating effort or breaking the consistency of the core system.
One of the distinctive characteristics of this team is its emphasis on rapid experimentation and learning. Rather than pursuing long, isolated development cycles, the team embraces a culture of quick prototyping and fast feedback loops. This means releasing early drafts of conversational flows, evaluating performance on real users, and iterating based on observed outcomes. The approach is designed to shorten the time from idea to impact, allowing Walmart to test a wide array of use cases, languages, and interaction modalities. It also means the team can glean actionable insights about how customers interact with conversational interfaces, what kinds of questions they ask, and where friction tends to arise.
The Emerging Tech group anchors its work in a robust foundation of natural language understanding and processing, with an emphasis on scalability and reliability. The organization relies on large language models as a core capability, but it also acknowledges the importance of domain-specific training and knowledge. For Walmart, that means tailoring AI behavior to reflect retail-specific workflows, product catalogs, pricing structures, and customer service standards. The approach is not about generic AI performance alone; it’s about delivering high-fidelity, contextually aware experiences that align with Walmart’s retail expertise and operational realities.
A practical aspect of this strategic framework is the deliberate integration of data assets and business domain knowledge to empower AI to deliver meaningful outcomes. The Emerging Tech team leverages Walmart’s extensive store, product, and customer data to ground language models in the realities of the business. This enables more accurate interpretations of user intents, more precise recommendations, and more effective routing of inquiries to the appropriate conversational flows. At the same time, the team is mindful of data governance, privacy, and security, ensuring that data usage complies with internal policies and external regulations.
The organizational design supports a culture of cross-pollination, where insights from customer service, merchandising, operations, and technology feed back into the AI platform. By coordinating efforts across departments, the group can standardize best practices, share reusable components, and reduce duplication of work. This collaboration extends to the localization work required to support global rollouts. The team develops reusable templates and localization kits to accelerate deployment in new regions, enabling teams to adapt the platform to local languages, dialects, and cultural nuances with minimal effort while maintaining consistency with Walmart’s global standards.
From a governance perspective, the Emerging Tech group implements clear policies around AI use, model updates, and monitoring. They establish guardrails to prevent inappropriate or unsafe responses, enforce transparency about AI involvement in conversations, and maintain logs that support auditing and accountability. The governance framework also defines escalation paths for edge cases, ensuring that human agents can step in when complex or high-risk interactions arise. This combination of technical discipline, cross-functional collaboration, and robust governance forms the backbone of Walmart’s scalable, responsible AI program.
The team’s emphasis on speed and flexibility is matched by a focus on customer-centric design. When a new AI capability is conceived, the team seeks to understand the customer’s context and expected outcomes. They map the customer journey to identify points where AI intervention can reduce friction, accelerate decision-making, or improve satisfaction. This user-centered lens helps prioritize work streams that deliver the most significant impact and ensures that each deployment meaningfully enhances the shopping experience for customers and the efficiency of store associates.
Finally, the Emerging Tech group treats experimentation as an ongoing discipline rather than a one-off effort. They maintain a careful balance between exploring novel approaches and consolidating proven capabilities. This means maintaining a portfolio of experiments—some aimed at incremental improvements, others at bold, transformative shifts in how Walmart engages with customers. The objective is to build durable capabilities that endure beyond individual experiments, enabling Walmart to continuously upgrade and expand its conversational AI across products, services, and markets.
Subsection: Learning from retail-specific experiences to improve AI
A distinguishing factor in Walmart’s approach is the deliberate use of retail-specific experiences to inform and improve AI systems. The team draws on deep knowledge about shopper behavior, product discovery patterns, store layouts, and the day-to-day realities of frontline operations. By embedding this expertise into AI pipelines, Walmart can deliver more intuitive, friction-free interactions that align with how people shop and how associates work. This reflective practice helps ensure that AI outputs are relevant, actionable, and trusted by users, whether they are customers seeking guidance on where to find an item or associates looking for the quickest way to fulfill a task.
In practical terms, this means designing conversational experiences that understand common shopping scenarios, can handle a wide range of product questions, and can adapt to seasonal promotions, inventory changes, and new product introductions. It also means creating flows that guide customers through complex tasks such as returns, exchanges, or multi-item purchases in a manner that feels natural and efficient. For associates, the AI tools provide direct access to schedules, tasks, and product information in a way that integrates seamlessly with existing workflows, minimizing disruption and enabling smoother service delivery.
As Walmart continues to expand the capabilities of its conversational AI platform, the group remains committed to measuring impact across multiple dimensions. They track not only quantitative metrics such as response speed, resolution rates, and assist volume but also qualitative indicators like user satisfaction, perceived trust in the AI, and the degree to which the experience feels personalized and helpful. This comprehensive approach to evaluation supports continuous improvement and ensures that AI investments translate into tangible, customer-centric benefits.
From contact centers to customer experience: use cases driving results
Walmart’s use of conversational AI spans several high-value scenarios designed to transform both customer and associate experiences. At the center of these efforts is customer care automation, a domain where AI systems handle routine inquiries, guide customers through common tasks, and escalate more complex issues to human agents as needed. The result is a more efficient support model that reduces the time required to resolve issues, improves consistency in responses, and frees human agents to handle cases that require empathy, problem-solving, or specialized knowledge. The impact on throughput and service levels is measurable, with AI-enabled workflows handling a significant share of routine interactions and enabling faster overall service.
Beyond customer care, conversational AI supports shopping assistance across digital and physical channels. This includes helping customers locate items, compare products, understand features, and navigate the store or online catalog. AI-powered assistants can answer questions about stock availability, pricing, promotions, and delivery options, helping shoppers make informed decisions and complete purchases more quickly. The platform’s ability to understand buyer intent and provide timely, relevant information enhances the overall shopping experience and can contribute to higher conversion rates and greater basket value.
Another core use case is enabling associates to perform their tasks more efficiently. Through AI-assisted workflows and internal tools, employees can access scheduling information, locate merchandise, and obtain product knowledge in real time. This not only improves personal productivity but also reduces the time customers spend waiting for assistance, leading to higher satisfaction and a more streamlined store environment. The combination of customer-facing and internal use cases demonstrates how conversational AI can impact multiple facets of the retail operation, from front-end interactions to back-end processes.
The Emerging Tech team also explores the potential of predictive and proactive AI capabilities, aiming to anticipate customer needs before they arise. By analyzing patterns in past interactions, purchase histories, and local inventory, Walmart’s AI systems can surface helpful suggestions, reminders, or prompts that align with what customers are likely to want next. This proactive dimension adds a layer of anticipatory service that can differentiate Walmart’s shopping experience and reinforce customer loyalty.
To support these use cases, Walmart emphasizes a user-centric design process that prioritizes clarity, usefulness, and trust. Clear explanations of when customers are interacting with AI versus a human agent help set appropriate expectations, while transparent responses and robust error handling contribute to a sense of reliability. The company also invests in continuous improvement programs that gather feedback from customers and associates, enabling ongoing refinement of conversational flows, language models, and integration with other systems. The end result is a more capable, context-aware AI platform that can support a broad spectrum of customer and employee interactions with greater accuracy and efficiency.
Subsection: The role of language understanding and domain knowledge
A central element of Walmart’s conversational AI strategy is the combination of natural language understanding and domain specialization. The platform is trained to interpret and respond to user inquiries with high fidelity, using both general language processing capabilities and Walmart-specific retail knowledge. This dual approach reduces misinterpretations and ensures that AI outputs are aligned with the company’s product catalog, merchandising strategies, and service standards.
The emphasis on domain knowledge extends to the ability to handle specialized requests, such as finding a particular product with specific attributes, understanding regional promotions, or guiding a customer through a return or exchange. By grounding language models in retail context, the AI system can offer more precise guidance, suggest relevant alternatives, and minimize the need for escalations to human agents. This leads to more seamless interactions, faster resolutions, and an enhanced sense of reliability for customers.
In practice, this means building a robust knowledge base that’s tightly integrated with the conversational platform, enabling real-time access to product data, stock levels, pricing, and policy information. It also means developing localized content and flows for different markets, accounting for language nuances, regional product assortments, and local consumer expectations. The convergence of strong language understanding and rich domain knowledge is a critical factor in delivering scalable, high-quality conversational experiences across Walmart’s global footprint.
The customer care automation journey: metrics, impact, and learnings
Walmart’s customer care automation efforts have produced meaningful improvements in service efficiency and customer satisfaction. The deployment of AI-driven support has helped reduce the average time agents spend with customers by automating routine tasks, answering common questions, and guiding customers through standard procedures. By handling a substantial portion of inquiries autonomously, the platform enables human agents to focus on more complex or high-value interactions that benefit from human judgment and empathy.
The measurable impact of these efforts includes a large volume of assisted contacts being supported by conversational AI. This scale demonstrates the platform’s reliability and potential to augment customer service operations without proportional increases in staff. The efficiency gains translate into faster resolution times, higher first-contact resolution rates, and a more consistent customer experience across channels. In addition, AI-assisted interactions provide valuable data on common customer concerns, enabling teams to identify patterns, inform product and service improvements, and tailor communications to address recurring needs more effectively.
From an economic perspective, Walmart’s approach to AI-driven customer care includes careful cost management and optimization. As with any large-scale AI deployment, practitioners must consider the total cost of ownership, including model training and inference costs, platform maintenance, data processing, and ongoing governance. Walmart’s strategy involves balancing the benefits of automation with prudent cost controls, adopting efficient inference practices, and prioritizing use cases with the highest potential ROI. This prudent approach helps ensure that AI investments deliver sustainable value over time rather than ephemeral gains.
Beyond pure cost considerations, Walmart’s leadership highlights the importance of building trust and reducing the risk of AI failures in customer care scenarios. Trust is cultivated through transparent interactions, consistent information, and safeguards against incorrect or misleading responses. The company also acknowledges the fragility of AI systems in complex, multi-turn conversations and emphasizes human oversight when necessary. By integrating human-in-the-loop processes, monitoring, and escalation, Walmart aims to preserve a high standard of service quality even as automation handles the bulk of routine inquiries.
Subsection: Measuring success and identifying opportunities
Success in customer care automation is not measured solely by efficiency gains. Walmart also looks to customer satisfaction, sentiment, and perceived quality of AI interactions as critical indicators. Feedback from customers and associates informs ongoing refinements to conversational flows, response accuracy, and the overall user experience. By combining objective metrics with subjective assessments, Walmart can gauge how well its AI systems meet customer expectations and where improvements are most needed.
In addition to immediate performance metrics, Walmart uses insights from AI-driven customer care to influence broader product and service strategies. For example, trends in questions about certain product categories can reveal gaps in product information or gaps in merchandising content. These insights can drive updates to knowledge bases, help pages, or in-store guidance, creating a loop that extends beyond support channels to enhance the entire shopping journey. The ability to translate AI analytics into actionable business decisions is a key advantage of scalable conversational AI in a retail environment.
Learnings from the journey to date inform future investments and deployment plans. Walmart emphasizes that AI should complement and augment human capabilities rather than replace them wholesale. As the platform becomes more capable, it’s essential to preserve the value of human judgment and empathy in areas where customers benefit most from nuanced support. The company’s approach is to scale capabilities where they deliver the most meaningful improvements while maintaining a careful, ongoing evaluation of risk, cost, and performance. This balanced perspective supports a durable, customer-centric AI program that can evolve with changing customer expectations and competitive dynamics.
International rollout: Walmart Chile as a case study in speed and adaptation
Walmart’s ambition to deploy conversational AI beyond the United States hinges on speed, adaptability, and the ability to tailor the platform to local needs without compromising global standards. A standout example of this approach is the rapid onboarding of Walmart Chile, where teams leveraged the standardized, scalable platform to implement localized capabilities in a matter of weeks. This accelerated timeline underscores the platform’s design for speed and its capacity to accommodate country-specific requirements without introducing untenable complexity or delay.
Localization at scale is not about translating content alone; it involves adapting conversations to language nuances, cultural expectations, and local service norms. Walmart Chile’s rollout demonstrated that a standardized platform can be customized efficiently to address unique regional needs while maintaining a consistent user experience aligned with Walmart’s global approach. The Chilean operation faced distinct customer care scenarios and language considerations that required adjustments to handling flows, dialogue design, and the overall user interface, all of which the Emerging Tech team managed within a compressed timeline.
A key outcome of the Chile deployment was a substantial uplift in customer satisfaction, driven by the ability to onboard quickly and tailor the platform to meet local expectations. Specifically, the team was able to establish more than 60 distinct flows to handle a variety of customer interactions, delivering improved service quality and faster resolution times. The result was a measurable increase in customer satisfaction, reported at approximately a 20 percent improvement, as a direct consequence of expeditious deployment and thoughtful localization. This example illustrates how Walmart’s scalable platform, when combined with region-specific adaptation, can deliver rapid, meaningful improvements in customer experience across international markets.
The Chile experience also highlights an important operational advantage: the platform’s ability to scale speed without requiring specialized language experts on local teams. The Emerging Tech leaders explained that Chile’s onboarding benefited from pre-built, reusable components and localization workflows that could be applied with limited linguistic resources on site. This capability is crucial for accelerating expansions into new markets where language-specific expertise may be scarce, allowing teams to focus on configuring country-specific flows and validating outcomes rather than building everything from scratch.
In discussing challenges, Walmart’s leaders acknowledged that operationalizing large language models at scale across multiple geographies presents common industry obstacles. These include questions of reliability, performance in low-resource languages, and the need to manage costs as model usage grows. They emphasized the importance of establishing trust and safeguarding against issues such as hallucinations or inconsistent responses. To address these concerns, Walmart invests in governance, monitoring, and robust risk mitigation strategies, ensuring that AI readiness meet both customer expectations and regulatory requirements. The Chile example demonstrates that with a standardized platform and a thoughtful localization strategy, global expansion can be accelerated while maintaining high quality and customer trust.
The Chile rollout also emphasizes the broader strategic point that Walmart’s AI platform is designed to support rapid experimentation at scale. The ability to onboard new markets quickly, test localized flows, and measure outcomes in a controlled manner enables the company to learn and iterate, applying learnings from one market to others. The ultimate objective is not simply to replicate success in a new country, but to continuously refine the platform so it becomes easier to replicate, customize, and scale across additional markets, products, and languages. The Chile case study thus serves as both proof of concept and blueprint for future international deployments, demonstrating how Walmart’s approach to conversational AI can be generalized to drive growth and improve customer experiences worldwide.
Subsection: Country-specific adaptations and the language challenge
Adapting AI systems to different languages and cultural contexts is a central challenge in global retail. Walmart’s Chile success illustrates that language adaptation goes beyond translation; it requires adjusting dialogue structures, tone, and the flow of conversations to align with local consumer expectations. Local teams can leverage the standardized platform to implement language-specific configurations, regional vocabulary, and appropriate conversational styles while preserving the core logic and governance of the global system.
In practice, this means designing flows that accommodate regional search patterns, preferences in product discovery, and variations in store layouts or pickup options. The platform supports these adaptations through modular components that can be swapped or tuned for different markets, enabling teams to respond quickly to local preferences without compromising the integrity of the platform’s global architecture. The result is a scalable approach to localization that enables Walmart to bring sophisticated, AI-driven experiences to new markets with fewer barriers and faster timelines.
Operational challenges persist, of course. Language coverage, nuance in customer expectations, and regulatory considerations all require careful management. Walmart’s leadership stresses the importance of ongoing evaluation, cross-market learning, and shared best practices to ensure that expansions strengthen the overall AI program rather than fragment it. The Chile case demonstrates how disciplined governance, coupled with a modular platform and a clear localization strategy, can deliver rapid value and set a precedent for subsequent international deployments.
Managing risks: trust, hallucinations, and cost in large language models
As Walmart expands its use of large language models (LLMs) and related AI technologies, it faces common industry concerns about trust, reliability, and cost. The company’s approach to risk management emphasizes creating a trustworthy experience by prioritizing transparent AI interactions, safe content generation, and robust safeguards to minimize the likelihood of hallucinations or incorrect responses. This requires a combination of technical controls, human oversight where necessary, and clear communication with users about when they are interacting with AI versus a human agent.
A critical component of risk management is cost control. While AI can deliver substantial efficiency gains, the associated computational costs, data processing, model maintenance, and governance overhead can be significant. Walmart’s strategy includes optimizing the efficiency of inference, exploring more cost-effective model architectures, and employing prudent selection of use cases with the highest potential ROI. By balancing performance with cost considerations, Walmart aims to sustain AI-driven improvements in a financially responsible manner.
Trust-building also involves system transparency and user education. The platform should clearly indicate when AI is providing assistance, outline its capabilities and limitations, and offer a straightforward path to human assistance when needed. This transparency helps manage expectations and fosters a positive user experience, even when AI handles routine tasks. The emphasis on trust is complemented by continuous monitoring, testing, and validation across real-world usage to detect and correct deviations promptly.
In addition to user-facing safeguards, Walmart invests in governance and ethical considerations related to AI deployment. This includes establishing policies for data usage, model updates, and the responsible handling of user data. The company’s objective is to maintain compliance with privacy and regulatory requirements while delivering value through AI-powered capabilities. The governance framework supports consistent practices across markets and channels, ensuring that AI interventions align with Walmart’s service standards and customer expectations.
Another layer of risk management involves addressing operational challenges that arise when scaling AI across a global retail network. These include maintaining performance in varying network conditions, ensuring compatibility with diverse hardware and software ecosystems in stores, and managing the lifecycle of AI workflows as products and services evolve. Walmart’s approach to risk management remains proactive, with ongoing assessment, remediation, and iteration to preserve the quality and reliability of AI-driven interactions across all touchpoints.
Ask Sam: empowering store associates and transforming productivity
A notable demonstration of Walmart’s AI-driven transformation is the introduction of Ask Sam, a conversational AI tool designed to empower store associates and enhance their productivity. Ask Sam supports associates by answering customer questions and helping them check schedules, thereby enabling smoother operations and more responsive service at the point of sale. With more than two million associates using the application today, the platform has delivered measurable productivity gains and improved the efficiency of daily tasks.
The practical impact of Ask Sam is visible in both customer-facing and internal tasks. When a shopper has a question about product location or store layout, associates can leverage Ask Sam to quickly retrieve relevant information, reducing the time needed to locate items and assist customers. Beyond customer interactions, the tool helps employees manage their schedules and identify tasks linked to productivity improvements, enabling more effective time management and workflow planning. This combination of real-time knowledge access and task management demonstrates how AI can be a force multiplier for frontline teams.
Ask Sam’s deployment is a testament to Walmart’s broader strategy of extending AI-enabled capabilities across the enterprise to support frontline staff. The system is designed to be intuitive, with conversational interfaces that fit naturally into the daily routines of associates. The goal is to make AI a seamless part of work processes, helping employees deliver faster, more accurate information to customers while also managing their responsibilities more efficiently. By integrating AI-powered assistance into everyday tasks, Walmart aims to create a more productive, confident workforce that can respond to customer needs with speed and accuracy.
In addition to productivity benefits, Ask Sam contributes to a broader cultural shift within Walmart—one in which associates increasingly rely on AI-enabled tools to augment their knowledge, improve decision-making, and deliver superior customer experiences. The technology acts as a co-pilot, supporting human agents rather than replacing them, and reinforcing the company’s emphasis on human-centered service, empathy, and reliability. As adoption continues, Walmart closely monitors usage patterns and outcomes to identify additional opportunities to expand AI-enabled capabilities that align with frontline workflows and customer expectations.
Subsection: Real-world usage and value for employees
The practical, day-to-day value of Ask Sam is reflected in the way associates interact with the tool during routine tasks. For example, an employee on the shop floor may use Ask Sam to quickly confirm the location of a product, check stock levels, or verify pricing and promotions. The tool’s ability to retrieve precise information in real time reduces the time spent on searching for data and increases the speed at which a customer can be assisted. This efficiency translates into shorter customer encounters and more opportunities for associates to engage with shoppers, address queries, and guide them toward relevant products.
Ask Sam’s usefulness also extends to internal scheduling and workload management. Associates can consult their schedules, review upcoming tasks, and understand what actions they need to take to maintain smooth operations. By centralizing these tasks within a conversational interface, Walmart creates a streamlined workflow that minimizes the cognitive load on employees and supports better time management. The aggregate effect is a more productive workforce capable of handling a higher volume of customer inquiries without compromising service quality.
The broader implication of Ask Sam within Walmart’s AI ecosystem is that it exemplifies how conversational AI can be embedded across a retailer’s value chain to support front-line staff. It demonstrates that AI can be used to improve personal productivity, enhance service delivery, and empower employees with timely information and guidance. As the company continues to expand its usage, the experience gained from Ask Sam will inform the development of additional tools and capabilities designed to support associates in tasks ranging from customer assistance to merchandising and operations planning.
Delivering real-time experiences at scale
Walmart’s systematic approach to delivering real-time experiences across multiple channels is transforming how retail is experienced by customers and how operations are conducted internally. The core idea is to orchestrate cutting-edge technologies to anticipate customer needs and provide contextual intelligence just before customers even ask for it. This approach reflects decades of accumulated knowledge about retail dynamics and a relentless focus on friction reduction throughout the shopping journey.
The platform emphasizes speed and reliability, enabling rapid deployments, particularly in international markets, without requiring extensive language programming or specialized expertise from local teams. This capability is a cornerstone of Walmart’s global expansion strategy. It allows the company to roll out new conversational experiences in foreign markets quickly, testing and refining flows, and expanding coverage in a controlled manner. The emphasis on speed does not come at the expense of quality; rather, it is achieved through standardized, scalable architectures and reusable components that can be adapted to local contexts efficiently.
A defining feature of Walmart’s AI organization is its emphasis on context-aware intelligence. The Emerging Tech team designs systems that understand the customer’s situation, prior interactions, and store-specific constraints to deliver highly relevant responses. This contextual awareness helps anticipate needs and provide proactive assistance, reducing the time customers spend seeking information and making the shopping experience feel seamless and intuitive. By delivering timely, relevant insights, Walmart aims to improve conversion rates, increase satisfaction, and strengthen customer loyalty.
From a technical perspective, real-time, cross-channel orchestration requires sophisticated integration across digital channels, in-store devices, and back-end systems. Walmart’s approach involves building a cohesive ecosystem where conversational AI flows can traverse channels, such as chat, voice, and mobile interactions, while maintaining continuity of context and history. The platform’s capability to coordinate across channels and devices ensures a unified customer experience, regardless of where the interaction begins or ends. This cross-channel coherence is essential for delivering a smooth, consistent, and personalized customer journey.
In practice, Walmart’s deployments bring together a mix of chat interfaces, voice-enabled assistants, and text-to-shop capabilities, all connected to a central knowledge base and product catalog. This integration enables rapid, real-time responses to customer inquiries, while also allowing associates to assist customers with confidence and accuracy. The ultimate objective is a moving, adaptive experience in which AI-driven intelligence meets shopper expectations with speed and precision, across both online and physical retail environments.
The organization also emphasizes ongoing learning and optimization across channels. Insights gathered from conversations, customer feedback, and usage patterns feed back into product development, merchandising, and service design. This closed-loop approach ensures that AI initiatives remain aligned with evolving customer needs and business priorities, reinforcing Walmart’s ability to adapt quickly and stay ahead in a competitive retail landscape.
Subsection: The role of proactive, context-aware AI at scale
A distinctive advantage of Walmart’s approach is the ability to anticipate customer needs with context-aware AI. By analyzing historical interactions, current store conditions, and real-time inventory, the platform can propose relevant actions or recommendations before customers explicitly request them. This proactive dimension of AI contributes to a more efficient and satisfying shopping experience, as customers receive timely guidance, suggestions, and options that align with their intent and preferences.
Context-aware AI also improves the efficiency of associates by preemptively surfacing information that is likely to be needed for a given interaction. For example, if a customer is seeking a specific product, the AI can pull related product recommendations, availability, and delivery options, enabling the associate to respond quickly and accurately. This reduces the cognitive load on staff and accelerates the pace of service.
The cross-channel orchestration of real-time intelligence supports a broad range of use cases, from simple product lookups to more complex interactions involving returns, exchanges, and multi-item orders. By ensuring continuity of context across channels, Walmart creates a cohesive experience in which customers can transition smoothly from, say, an online inquiry to an in-store interaction without losing the thread of their conversation. This level of coherence is critical for maintaining trust and satisfaction as customers navigate multiple channels and touchpoints.
In sum, Walmart’s delivery of real-time experiences at scale is built on a foundation of modular, scalable architecture, robust governance, and a culture of experimentation. The company combines advanced AI capabilities with deep retail know-how to create contextual, proactive, and cross-channel experiences that meet customers where they are and anticipate what they need next. The result is a more responsive, personalized, and efficient retail experience that differentiates Walmart in a crowded marketplace.
The continuous learning and future pathways
Walmart’s conversational AI program is a living system that evolves through continuous learning and iteration. Each deployment provides data and feedback that inform refinements to models, flows, and integrations. The company’s strategy emphasizes not only what works today but also what could be done better tomorrow, guided by customer preferences, operational realities, and emerging AI technologies.
Continuous improvement entails updating the knowledge base with new product information, promotions, and policies, as well as refining language models to better handle evolving customer expressions and intents. Walmart’s approach to ongoing improvement includes monitoring model performance, auditing outputs for quality and safety, and adjusting flows to minimize friction and maximize usefulness. The process is iterative by design, with each cycle contributing to stronger capabilities and more reliable experiences.
Looking ahead, Walmart is likely to expand the scope of its conversational AI program by incorporating more advanced capabilities, such as deeper integration with merchandising and inventory management systems, enhanced predictive capabilities for customer needs, and more sophisticated orchestration across additional channels. The potential for more proactive, contextually aware assistance across a broader array of use cases is substantial, and Walmart’s foundation is designed to accommodate such growth without sacrificing governance, trust, or user experience.
The company’s focus on cross-market scalability means that learnings from one region can inform efforts in others, enabling faster, more efficient international expansion. Standardized components, tested patterns, and local customization balance the need for global consistency with the imperative to honor local language and cultural differences. The result is a scalable, resilient AI program that can adapt to changing market conditions while maintaining a high standard of service for customers and a productive, empowered workforce for associates.
As Walmart continues to invest in conversational AI, the emphasis remains on delivering tangible business value—improved customer experiences, enhanced associate productivity, and operational efficiencies that compound over time. The company’s approach to scale, speed, and insight demonstrates how a global retailer can transform its interactions with customers and employees through thoughtful, responsible, and innovative AI-driven capabilities.
Conclusion
Walmart’s journey into scalable conversational AI reflects a holistic vision that blends technology, process, and human-centric design to reshape retail interactions. By empowering a global network of associates, standardizing a flexible yet robust AI platform, and extending capabilities across multiple markets and channels, Walmart is delivering faster, more accurate, and more personalized experiences for customers around the world. The Emerging Tech group’s culture of rapid experimentation and continuous learning fuels ongoing improvements, with measurable gains in customer care efficiency, expanded international capabilities, and enhanced workforce productivity through tools like Ask Sam. As the company navigates the challenges of trust, cost, and risk associated with large language models, its approach remains grounded in governance, transparency, and a steadfast focus on customer value. Looking forward, Walmart’s emphasis on context-aware, proactive AI across channels positions the company to meet evolving shopper expectations and to sustain competitive advantage in a rapidly changing retail landscape.
