10 retail AI agent challenges—And how to overcome them
As retailers brace for the usual holiday headaches—surging ticket volumes, delivery delays, the corresponding deluge of WISMO queries and returns—AI agents are becoming the go-to solution to these persistent challenges. Research shows 9 in 10 retailers already use AI in some form, while Salesforce reports that 75% of retailers say AI agents will be “essential” by 2026.
Unlike previous retail software, AI agents act with autonomy. They reason, make decisions, use tools like APIs, and execute tasks from start to finish on behalf of users. Critically, they also operate seamlessly across once-siloed platforms and channels, syncing live data into one unified, evolving customer profile.
Using this holistic view, agents can interpret real-time behavior and intent signals to deliver a new level of personalization and a more seamless, convenient customer experience (CX). And by automating routine tasks and customer inquiries, they free human teams to focus on higher-value interactions, boosting efficiency and revenue without adding headcount.
But for all the benefits AI agents offer retailers—unified data, streamlined operations, superior CX—they’re not without their challenges. From workflow design to integration complexity to data quality, retailers must overcome various roadblocks to turn AI agents into a scalable, trusted solution.
This article explores the most common failure modes of retail AI agents and offers practical strategies for designing, governing, and scaling agents to be resilient, reliable, and enterprise-ready.
1. Integration with legacy systems
Many retailers use legacy IT systems that struggle to integrate with AI agents. This can cause agents to break down mid-task, especially for cross-system workflows with inventory or order systems.
Solution: Rather than “bolting on” AI to legacy processes, re-architect workflows around agents from the start. McKinsey found that organizations reporting "significant" ROI from AI are twice as likely to have redesigned end-to-end workflows before deployment. For smaller retailers with limited resources, choosing vendors with AI agent integrations and robust APIs can ease integration and ensure performance without requiring a costly overhaul.
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2. Data quality and integration
AI agents need access to clean, unified data to make reliable decisions, but retail data is notoriously messy. Inconsistent product catalogs, mismatched inventory counts, and fragmented customer histories can derail performance and frustrate shoppers.
Solution: Retailers should prioritize data readiness and governance: consolidating data sources, standardizing data, and putting quality checks in place. For example, agent activity trails are how Sendbird helps retailers ensure their agents always act on accurate information and deliver consistent, on-brand experiences.
3. Workflow design
AI agents work best when their tasks are clearly defined, but technical limitations, infrastructure gaps, or lack of oversight can make this difficult. Without guardrails on workflows, agents might over-promise and leave customers frustrated (such as offering a refund they can’t actually process).
Solution: Retailers should map precise workflows for agents with clear inputs, outputs, and fallback paths to human support. Well-designed AI actionbooks (SOPs) from agent builders like Sendbird make it easy for non-technical users to design and optimize agent workflows.
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4. Talent and expertise shortage
Most retailers lack the in-house talent to design, scale, and manage AI agents, which is already scarce.
Solution: Instead of hiring a costly cadre of machine learning engineers and data scientists, partner with trusted AI agent platforms that offer consultative support and proven expertise in retail use cases. This allows internal teams to focus on adoption and CX rather than getting bogged down in technical complexity, helping to ensure both technical performance and change management at speed.
5. Capability-expectation gap
AI agents excel at narrow, well-defined tasks—but often fall short when built to serve as general-purpose assistants.
Solution: For best performance, retailers should focus on specific yet enduring use cases (reducing ticket volume, boosting CSAT) where ROI is clear and measurable. Be sure to validate project feasibility with technical experts, start small before expanding to complex applications, and prioritize observability features to track and fine-tune agent performance.
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6. Change management and employee uptake
Retailers that treat AI deployment as a purely technical process risk overlooking the organizational changes key to lasting success. One study shows 31% of employees resist or even sabotage AI initiatives without proper support.
Solution: Success hinges on strong change management: upskilling staff, redesigning workflows, and defining how humans and machines work together. For example, Sendbird provides role-based access controls that enable effective collaboration and governance at scale, helping employees be champions who drive success.
7. Data security and privacy
AI agents often handle sensitive customer data, raising concerns about data protection and compliance with regulations like GDPR. Their autonomy also widens the attack surface for breaches or misuse.
Solution: To mitigate these risks, retailers can prioritize enterprise-grade AI agent platforms with built-in encryption, audit trails, and compliance certifications. Leading platforms pair agentic capabilities with robust security and AI governance frameworks, ensuring innovation doesn’t come at the expense of trust.
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8. Ethical concerns and bias
AI agents can replicate biases in their training data, leading to unfair or discriminatory outcomes. For instance, skewed recommendations could exclude certain demographics from promotions.
Solution: Regularly audit agent outputs for fairness and responsibility. The best AI agent platforms include tools that monitor, detect, and correct bias, hallucinations, and inaccuracies in real time, helping teams mitigate risk, ensure transparency, and fine-tune performance.
9. Balancing automation with a human touch
Retailers that lean too heavily on automation risk alienating shoppers, as many still prefer to speak to a real human in emotional moments like delayed deliveries.
Solution: The sweet spot is balance. AI agents are best suited for routine, high-volume tasks (WISMO, FAQs, promo codes), and can make seamless, context-rich handoffs to humans for escalations and exceptions. A front line of 24/7 localized, multi-lingual AI support backed by humans still creates efficiency, but without risking trust.
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10. Maintaining customer trust
Last but not least, shoppers may have concerns about how their data is used by AI, or whether they’re talking to a bot. Customers who feel their needs and preferences aren’t being met will look elsewhere.
Solution: Reinforce customer trust by being transparent about AI use, clearly explaining data practices, and offering a human fallback. Transparency isn’t just compliance in the AI era; it’s a loyalty driver. This is why Sendbird makes it easy to evaluate agent outputs with scorecards that assess message content, solution quality, and customer satisfaction. By treating data protection as a brand differentiator, retailers can strengthen customer trust and turn responsible AI into a competitive advantage.
