Agentic Commerce: Retail’s Answer to Data Deluge

Retailers are overwhelmed by a deluge of data from disparate sources. The rise of omnichannel customer touchpoints — from website traffic to chat interactions to point-of-sale transactions and more — means retailers are dealing with unprecedented volumes of data in disconnected silos.

Because the siloed information creates blind spots and delays, retailers are trapped in a reactive cycle because they’re always behind the curve. This lag leads to delayed responses, increased costs, decreased margins, reduced productivity, and lower customer satisfaction.

Agentic commerce addresses the struggles of data overload and reactive decision-making by deploying autonomous artificial intelligence agents that connect, interpret and act on vast quantities of retail data to guide intelligent and timely actions.

AI agents unify data to provide a single source of truth and an actionable view of the business. Because systems continuously scan data streams for changes and patterns, they anticipate and streamline manual processes and address any issues — e.g., updating pricing and promotions, engaging with shoppers, demand spikes, or impending stockouts — before they become problems.

Instead of sifting through dashboards and reports, you can rely on the technology to act when an opportunity or risk is detected (e.g., pricing optimization or stock replenishment). Moreover, the AI agents will continuously learn from outcomes to refine their actions.

Leverage Use Cases

AI agents can optimize outcomes by automating key aspects and friction points for digital retail activities. The technology is designed to act independently, executing complex tasks and real-time decisions without human intervention.

For example, AI agents can re-route orders on the fly based on inventory availability, customer location, and logistics to ensure the fastest delivery at the lowest cost. You could monitor sales trends and competitive pricing to adjust promotions in real time to optimize conversion and profit. For customer service, AI agents can handle returns and refunds, provide onboarding for complex products, and proactively resolve support issues with minimal manual oversight to save on resources. The use cases are endless.

Take Actionable Steps

Before deployment of AI agents for retail operations, you need to build in the foundational capabilities and governance structure. This readiness checklist emphasizes integration, phased automation, and governance:

1. Integrate data across systems.

Start with unifying data across systems such as your order management system (OMS), commerce platform, ERP, warehouse management system, CRM, etc. This could provide AI agents with data such as real-time visibility into order status, stock levels, shipping details, and customer information. Make sure your data is standardized and clean because AI algorithms rely on data consistency to accurately interpret and act on information.

2. Execute rules-based automation.

Initiate AI agent deployment with defined rules. Using conditions and triggers (e.g., reorder inventory at a certain threshold), you can reduce and/or automate repetitive tasks. Before you scale, validate and optimize the efficacy of your rules and monitor outcomes to identify problems. Introduce autonomy incrementally. When you’re confident the rules you’ve established for key workflows are reliable, you can add new elements in phases. Oversight is critical, as is escalating exceptions and ambiguity to human decision-makers.

3. Build in governance for AI decision-making.

Specify who is responsible for AI agent decisions, including approval to move forward with autonomous actions and responses to failures. Make decisions of what should have manual oversight vs. not. Setting ethical and operational boundaries ensures compliance with brand values, customer privacy, and regulatory mandates. Implementing monitoring and audit protocols ensures you’re continuously tracking agent decisions, outcomes, and system health to detect errors, bias or performance degradation. Escalation protocols and documented AI usage policies keep you on track.

Overall, the combination of data integration, simple automation, and robust governance builds a solid foundation to extract maximum value from your investment in agentic commerce.

Meagan White is the chief marketing officer at KIBO, a composable commerce platform designed to simplify the complexities of delivering modern customer experiences that span order management, e-commerce, and subscription services.