Industry
Retail
Omnichannel CX and Intelligent Innovation – The Future of Retail
HIGHLIGHTS
- The retail industry faces multiple supply chain challenges, including daily disruptions, emanating from climate volatility and geopolitical shifts to supplier delays and viral demand swings.
- Retailers struggle to handle sudden disruptions, demand swings, or reputational risks with speed and confidence in an age where consumers expect real-time availability, faster fulfilment, and consistent omnichannel experiences.
- Agentic AI can become a coworker that can help transform the supply chain into an adaptive, resilient network—ready to reason, adjust, and respond confidently and swiftly to whatever the market demands.
On this page
Overview
In today’s unpredictable retail landscape, supply chains face daily disruptions ranging from climate volatility and geopolitical shifts to supplier delays and viral demand swings.
At the same time, consumers expect real-time availability, faster fulfilment, and consistent omnichannel experiences. In addition, the response time is narrowing, requiring retailers to operate with both intelligence and agility. Agentic artificial intelligence (AI) enhances these capabilities significantly.
Agentic AI brings a paradigm shift—transforming AI from a background tool into a trusted coworker. It doesn’t just automate, it collaborates; it doesn’t just respond, it reasons. By embedding goal-driven, context-aware agents into the retail supply chain, organizations can elevate responsiveness, resilience, and continuous learning.
This is where the DAIEL framework comes in, providing clear steps to make agentic AI work effectively as a coworker:
- Detect: Identify early signs of disruption in real time
- Analyze: Determine causes and evaluate impacts within the context
- Inform: Provide actionable recommendations to relevant teams
- Escalate, execute: Act autonomously when authorized or escalate for human judgment
- Learn: Continuously improve based on outcomes
Why agentic AI?
Retail supply chains have evolved significantly.
They have moved from rigid, rules-based operations to more advanced models powered by machine learning (ML), reinforcement learning (RL), and modern control towers.
However, many retailers still struggle to handle sudden disruptions, demand swings, or reputational risks with speed and confidence.
Modern approaches often fall short because supply chains constantly change in ways that break predictable patterns. For example:
- Reinforcement learning works well for known, stable scenarios but often fails in the face of new events. Small changes like a new supplier or distribution center (DC) layout can render old learning useless and force costly retraining.
- Control towers surface data in real time but still depend on people to interpret insights and coordinate actions under tight timelines.
Example scenario: A sudden quality concern over a meat product from a supplier starts to trend on social media. This leads to a sharp drop in demand overnight, customers flood stores with questions, and unsold stock risks spoilage and waste.
Challenges with current technologies: Even with modern control towers, ML and RL-based supply chain management (SCM) can’t react fast enough to viral sentiment shifts. ML struggles with sudden change, while RL fails outside trained scenarios. Control towers surface real-time data but still rely on people to interpret and coordinate urgent actions, resulting in loss of valuable time.
How coworker agents can help: Detect early signals from social chatter, analyze the impact across suppliers and stores, inform stakeholders with clear actions (stock holds, markdowns, rerouting), execute approved steps automatically, and learn to respond faster next time. Treating agentic AI as a coworker can help transform the supply chain into an adaptive, resilient network that is ready to reason, adjust, and respond confidently to whatever comes its way (see Figure 1).
Figure 1
A comparative look at different supply chain solutions
Figure 1
DIAEL architecture
Better decision-making in today’s supply chains depends on timely support.
The support requires providing early signals, clear analysis, practical recommendations, and the ability to escalate issues or act quickly when it matters most
This is exactly what a trusted coworker does: staying alert, reasoning through scenarios, informing the right people, helping execute routine tasks, and learning from every situation to be more helpful the next time.
The DAIEL framework (see Figure 2) provides a simple, structured way for agentic AI to behave like a coworker—acting with conscience, context, and clear boundaries. By detecting, analyzing, informing, escalating, executing, and learning within defined guardrails, these agents strengthen human decision-making rather than replacing it, helping supply chains stay adaptive and resilient in a fast-changing world.
Figure 2
Elements of the DAIEL framework
Figure 2
Use case: AI response to power outages in DCs
A DC experiences a sudden power outage at 4 am, caused by a local grid failure. Automation halts, picking systems stop, and morning shipments are at risk across the network.
How the DAIEL framework responds
The DC agent identifies the anomaly using system logs, infrastructure telemetry, and halted scanning activity:
- Confirms the grid issue via utility feeds and backup UPS alerts
- Detects ‘DC automation offline’ status
- Notes no-picking activity for over 20 minutes
- Confirms external power grid failure
Analyze: The agent runs a simulation to assess operational impact:
- Identifies 12 store shipments affected
- Forecasts stockout windows based on store inventory and sales velocity
- Reviews backup DC stock levels and potential route capacities
- Flags dairy and produce as high risk due to perishability
Using GenAI, the agent sends role-specific updates:
- To store managers: ‘Today’s inbound may be delayed by 3-4 hours. Categories impacted: dairy, fresh vegetables. Backup sourcing under evaluation.’
- To replenishment planners: ‘DC X outage. 12 orders delayed. Redistribution feasible from DC Y and Z. Recommend route reallocation.’
- To transport managers: ‘Truck-loading paused. Route shifts being modelled for store coverage within SLA.’
The agent looks at options and escalates, executes tasks as needed.
- The agent reroutes five low-priority store orders using spare capacity from other DCs.
- For high-priority shipments, it escalates action to human planners, seeking approval due to promotional constraints.
After resolution, the agent logs key metrics:
- Time to detect
- Stakeholder responses
- SLA misses (if any)
- Root-cause tagging
This feedback improves future disruption responses enabling pre-emptive simulations when grid instability signals are detected. The DAIEL framework acts as a cognitive coworker, seamlessly managing operational disruptions and enhancing decision-making processes through continuous learning.
Key considerations
While agentic AI enables real-time reasoning and human-like support, it introduces new challenges.
Organizations must address these to ensure agents truly function as trusted coworkers.
The challenges include:
- Data integration and readiness: Legacy systems often lack real-time application programming interfaces (APIs) and consistent data quality. Solution: Unified pipelines, event-driven architecture, and strong data governance.
- Infrastructure demands: Agents require reliable, scalable infrastructure.
Solution: Cloud-based solutions with observability and targeted rollout. - Security and oversight: Balancing agent autonomy with policy compliance is essential.
Solution: Guardrails, role-based access, and explainability. - Organizational change: Moving to agentic AI means a shift in mindset across all levels.
Solution: Executive sponsorship, user training, and phased, ROI-driven rollouts. - Real-time context: Agents must act based on live data like weather or social trends.
Solution: Access to real-time sources, contextual reasoning, and scenario-based simulations.
Co-working for the win
Modern grocery supply chains operate in an environment of constant change, driven by local behaviors, unpredictable disruptions, and evolving customer expectations.
In this context, speed and quality of decision-making become the true differentiators.
Agentic AI, through the DAIEL framework, introduces a new class of intelligent agents that embed directly within the supply chain, detecting issues, analyzing root causes, informing stakeholders, and recommending or coordinating responses. These agents don’t just automate tasks, they enrich the decision-making process with timely insight, context, and traceable learning.
Industry experts have been cautioning that over 40% of agentic AI projects are likely to be cancelled by the end of 2027, highlighting the risk of deploying AI in isolation or without alignment to real-world workflows. It has been observed that too often, these systems fail to deliver sustained value because they are positioned as replacements rather than extensions of human capability. AI, therefore, has to be viewed as a holistic approach to ensure success. This applies to the integration of agentic AI as well.
Agentic AI offers multiple advantages compared with traditional SCM models, reinforcement learning, or control towers:
- Localizes intelligence, enabling real-time decisions at the point of need
- Communicates in a human-friendly language, going beyond just providing alerts or metrics
- Builds a shared intelligence layer that improves with every action and outcome
The result is a supply chain that is not just automated but augmented. One where managers, planners, and store operators are empowered with the right information, at the right time, to take the right action.
