From early GenAI tools like sentence-completion algorithms to the integration of retrieval-augmented generation (RAG) technology to fill context, advancements in AI have greatly impacted the supply chain industry’s ability to work efficiently. But are you ready? The next frontier for advancement is agentic AI.

Gartner research predicts that by 2028, 33% of enterprise software applications will include agentic AI, up from less than 1% in 2024. This will enable 15% of day-to-day work decisions to be made autonomously. For supply chains, which thrive on speed, accuracy, and adaptability, the integration of agentic AI could be the key to staying ahead.

But what exactly is agentic AI, and how can it reshape the retail landscape?

Understanding Agentic AI and its Potential in Modern Enterprises

The concept of AI agents first appeared with AutoGPT in 2022, offering a glimpse into how agentic AI could solve complex problems beyond the capabilities of earlier patterns like RAG or simple LangChains. Though initial versions were slow and unreliable, they sparked significant interest, leading companies like Meta and OpenAI to invest heavily in refining agentic AI. By 2024, these efforts resulted in AI agents with extraordinary capabilities, including Meta's Llama 4, which is fully fine-tuned for agency, and OpenAI's five-level roadmap for agentic AI.

At its core, agentic AI is GenAI with the power to make independent decisions, or “agency.” This means that AI agents can choose whether to act, gather more data, or verify information before proceeding — key for supply chain management where accuracy and responsiveness are critical. Chain of Thought (CoT) prompting further enhances this by requiring agents to create step-by-step plans, offering transparency and justification for decisions made.

Agents also conduct their own research, pulling data from multiple sources to build context before acting. They aren’t limited to responding to simple queries; they can trigger workflows, adjust inventory, or reroute shipments, all via approved tools for autonomous action. Additionally, agents can course-correct in real time, adapting to changing conditions such as unexpected results or disruptions, while external verifiers ensure compliance with business goals.

Agentic AI in Action: Dynamic Pricing Strategies

Retail value chain management is still in its early stage of deploying or implementing GenAI. In an August 2024 McKinsey survey of more than 50 retail executives, while most said they were piloting and scaling large language models and GenAI broadly, only two executives had successfully implemented GenAI across their organization.

However, as retailers continue making this move, one area where agentic AI could shine is pricing strategy optimization. Its impact would go beyond just setting prices. Accurate pricing often depends on understanding supply chain costs, availability of materials, and logistical considerations. Agentic AI can integrate these factors, ensuring that pricing strategies align with real-time supply chain conditions.

For instance, when determining if a price adjustment is feasible, agentic AI could analyze not only historical sales data but also current supply chain data, such as inventory levels and transportation costs. It could calculate price elasticity based on this comprehensive view, visualize the potential market response, and provide a narrative recommendation that considers both pricing and supply chain efficiency. This holistic approach would allow retailers to optimize pricing and operations simultaneously, improving profitability and reducing costs.

A New Level of Precision for Risk Management

The complexity of global supply chains often exposes retailers to various risks like weather disruptions, geopolitical shifts and supplier reliability issues. Traditional risk management tools may provide generic alerts, but they often lack the specificity needed for effective action. Agentic AI can address this by offering tailored, proactive solutions that integrate multiple data sources.

For example, when a retailer queries the impact of a severe weather event, agentic AI would be able to do more than just issue an alert; it could identify the specific parts of the supply chain that may be affected, such as shipping routes, distribution centers, or critical seasonal products. The AI could then pull long-term weather forecasts and integrate this data with supplier information to assess the potential impact. Finally, it could suggest mitigation strategies, such as rerouting shipments or adjusting stock levels in certain regions, ensuring that the retailer’s supply chain remains resilient and efficient.

This agentic pattern’s ability to autonomously gather, analyze, and adjust data based on new information promises to provide retailers with a precise and dynamic approach to managing supply chain risks beyond what GenAI can provide. To ensure that these autonomous adjustments align with business goals, verifiers like independent AI scripts or models monitor the agent’s actions. Verifiers check whether the AI adheres to the intended tasks, policies, and safeguards, providing an extra layer of oversight and compliance. This ensures that agentic AI maintains compliance with the retailer’s operational standards, even when making adjustments on the fly.

Building Reusable Supply Chain Assets with Agentic AI

The most transformative aspect of agentic AI may be use cases that include the ability to build and reuse enterprise assets for stronger supply chain management. Once an agentic AI develops a solution, complete with a verified step-by-step sequence, this output can be saved and reused for similar scenarios in the future, significantly enhancing efficiency. This capability means that agentic AI doesn’t just solve problems once; it creates a repository of transparent, replicable tactics that become integral to the retailer’s supply chain management toolkit.

And businesses are starting to take notice of these benefits. A recent Capgemini survey of over 1,000 executives found that 10% of organizations already use AI agents, more than half plan to use them in the next year, and 82% plan to integrate them within the next three years.

As agentic AI continues to evolve, retailers have the opportunity to harness its capabilities to build smarter, more resilient supply chains. IDC reports that spending on AI for the retail industry is reaching around $25 billion this year. As executives decide where to put their money, understanding what makes an AI agent truly agentic, and integrating these models into their operations will be essential for retailers aiming to stay competitive and agile in an increasingly complex market.

With agentic AI, the choice lies between choosing to automate the hustle, or keep grinding away manually, one shipment at a time.

Mike Finley is co-founder and CTO of AnswerRocket.