INFO-TECH RESEARCH GROUP

Build a Retail Supply Chain Resilience Strategy

Smart AI adoption: Reduce complexity, boost resilience.

Analyst perspective

Align AI and turn it into a competitive advantage.

Retail is undergoing rapid transformation as expectations for convenience, speed, and personalization rise, while operational pressures intensify. To stay competitive, retailers must modernize legacy processes, strengthen supply chain resilience, and leverage data and automation.

AI has become a top strategic priority, yet many retailers lack clarity on where to begin and how to scale. AI use cases offer a practical starting point by showing where value can be created across supply chain actions. When applied effectively, AI reduces inefficiencies, improves product availability, and enables more adaptive, resilient supply chains.

This research provides actionable AI use cases for retail supply chains and a clear framework for identifying priorities, guiding investment, and building a scalable, future-ready AI-driven supply chain strategy.

Donnafay MacDonald
Research Director, Industry Research
Info-Tech Research Group

Executive summary

Your Challenge

No clear path forward amid AI hype. Hard to cut through the noise and identify the right high-value supply chain use cases.

Limited internal AI skills. Teams lack practical AI experience, slowing adoption and creating uncertainty.

Pressure to show real ROI. AI requires investment, and CIOs must prove value while managing cost, compliance, and risk.

Common Obstacles

Fragmented and low-quality data. Critical supply chain data lives in disconnected systems limiting AI accuracy.

Legacy systems and integration constraints. Outdated, highly customized platforms make it difficult to embed AI into forecasting, replenishment, sourcing, and logistics workflows.

Lack of clear ownership. No single team “owns” AI in the supply chain, causing misaligned priorities, slow decisions, and stalled scaling efforts.

Info-Tech's Approach

Introduce an approach to build your AI roadmap rapidly and responsibly for your supply chain via a seven-step practical framework to accelerate adoption.

Help your organization to discover and understand a variety of AI use cases that can address your business challenges as well as support organizational strategic goals.

Guide your organization on its AI journey in the supply chain by identifying and prioritizing AI use cases for business capabilities through a benefits realization model.

Info-Tech Insight

Resilience isn’t created by AI; it emerges from better decisions made because of AI. AI improves resilience in retail supply chains by making core decisions far more accurate, timely, and adaptive.

Your challenge

This research is designed to help organizations who are facing these challenges to:

• Gain clarity on where AI can create real supply chain value.

  By mapping AI use cases across the supply chain, organizations can clearly see where AI can deliver the most value, cutting through the hype by prioritizing high-impact opportunities.

• Build the internal readiness.

  Use cases make AI tangible and they show which teams need new skills, where data gaps exist, and how processes can evolve.

• Demonstrate measurable ROI through targeted, high-value applications.

  Well-defined use cases tie AI efforts directly to business outcomes, and make it possible to justify investment, track value creation, and prioritize initiatives with the biggest return.

Common obstacles

These barriers make this challenge difficult to address for many organizations:

• Critical data lives across disconnected systems, making it hard to evaluate AI opportunities. Poor data quality reduces trust and slows progress.
• Outdated customized platforms make it difficult to embed AI into planning and execution workflows. Even when high-value use cases are identified, integrating them into daily operations can be complex.
• Teams often don’t fully understand AI’s requirements or implications. Without a shared understanding, clear ownership, or aligned processes, organization often struggle to evaluate use cases, commit resources, and scale successful initiatives.

AI adoption accelerates retail transformation

AI as a core engine for retail modernization

As retail executives expand AI capabilities, the technology has moved beyond experimentation and is now a core driver of modernization, spanning merchandising, operations, supply chain, and customer experience.

Retailers that are leveraging AI can achieve these business outcomes:

• AI enables faster, more accurate decisions in forecasting, pricing, replenishment, and customer analytics, and can reduce the latency across the retail value chain.
• AI supercharges omnichannel execution by powering real-time inventory visibility, dynamic fulfillment, and personalized experiences that unify store and digital and supply-chain operations.
• AI investment continues to rise, and the deepening adoption is accelerating how retailers can scale use cases from pilots to enterprise-wide capabilities.

Artificial intelligence is becoming foundational.

85% of retail executives worldwide report having already developed AI capabilities. (Source: Honeywell, 2025.)

AI adoption is not just widespread, it’s deepening.

53% of retailers plan to invest even more in AI in the coming years. (Source: Honeywell, 2025.)

Anchor your AI roadmap in strategic retail priorities

Shift AI from experiments to measurable impact

AI is no longer a side project and is shifting to being the foundation of retail strategy. In the face of volatile demand, rising service expectations, and margin pressures, retailers are turning to AI to make smarter, faster decisions across the entire value chain.

Retailers are prioritizing AI in the following areas:

• Demand forecasting and inventory management
• Supply chain and logistics optimization
• Dynamic pricing and promotions
• Customer experience automation (e.g. chatbots, personalized recommendations)
• Loss prevention using computer vision

AI adoption drives revenue gains.

69% Among retailers who use AI, roughly 69% report revenue gains. (Source: Nvidia, 2024.)

Lead with value in supply chain and align AI to business priorities

From value-first thinking to proven ROI

Leading with business value focuses AI supply chain initiatives. It helps IT teams to start with concrete retail challenges, such as shrink, margin pressure, or inventory turns, rather than chasing hype or the latest tools. When retailers adopt a value-first approach, it aligns planners, buyers, logistics teams, and IT around a shared goal, making it easier to define success and demonstrate ROI by linking AI projects to specific business outcomes and metrics.

What retailers need to do to align the business with AI in the supply chain:

• Start with critical supply chain pain points.
• Map data to end-to-end flow.
• Embed AI into existing planning and logistics decisions.
• Create cross-functional ownership.
• Pilot, measure, then scale.

Value locked up in manual, rules-driven work.

Up to 3.3% of retailers’ total wage bill is spent on regulatory compliance tasks. (Source: Emerj, 2025.)

AI for loss prevention and inventory accuracy matters.

Up to $100 billion is estimated to be lost to inventory shrink each year in US retail. (Source: Emerj, 2025.)

Select and Prioritize AI Supply Chain Use Cases

Info-Tech Insight

AI-enabled retailers should optimize supply chains across three dimensions simultaneously – cost, service, and risk resilience – unlocking value competitors find difficult to match.

AI adoption in supply chains is slowed by siloed data, rigid legacy systems, and unclear ownership.

Challenge

Organizations struggle to identify high‑value AI opportunities, lack the internal skills to implement them effectively, and face growing pressure to deliver measurable ROI while managing cost and risk.

Outcome

Provides a clear view of what drives AI value in the supply chain, an assessment of maturity across tech, people, data, and governance, and a prioritized set of AI use cases aligned to strategic impact and organizational readiness.

Risk-adjusted, prioritized AI use cases, aligned to organization’s value drivers.

AI is an innovation in machine learning

Artificial Intelligence (AI)

A system that can make predictions, recommendations, or decisions influencing real or virtual environments.

Machine Learning (ML)

ML is a subset of AI algorithms that parse data, learn from data, and then decide or make predictions.

Generative AI (Gen AI)

A subset of artificial intelligence systems that generate new outputs based on the data the system has been trained on using modalities such as text, audio, visual, and code.

What makes AI different

Accelerate supply chain performance with AI

Measure the value of this research

Leverage this research’s approach to ensure your AI use cases align with and support your key business drivers and speed time to value.

AI use cases in retail supply chain should produce measurable results

Info-Tech offers various levels of support to best suit your needs

Guided Implementation

A Guided Implementation (GI) is a series of calls with an Info-Tech analyst to help implement our best practices in your organization.

A typical GI is 10 to 12 calls over the course of 2 to 3 months.

What does a typical GI on this topic look like?

AI Strategy Roadmap – Workshop Overview

Contact your account representative for more information.
workshops@infotech.com 1-888-670-8889

Research deliverable

Each step of this research is accompanied by supporting deliverables to help you accomplish your goals.

Retail Supply Chain Use Case Prioritization Tool

Provides a structured environment to prioritize AI‑driven supply chain use cases, capture key initiative details, and collaboratively shape the initial roadmap, ensuring alignment, transparency, and sustained momentum from strategy through execution.

Insight summary

Anchor AI in real supply chain problems, not abstract potential.

When retailers map then prioritize their supply chain AI use cases, AI stops being a “future opportunity” and becomes a focused portfolio of initiatives that can have a real, positive impact in achieving organizational goals.

Expose friction and clarify needs.

Walking through the end-to-end process (e.g. planning, sourcing, distribution, and last-mile delivery) forces teams to name where decisions are slow, manual, and error-prone. This makes it clear which data, integrations, and governance are required for AI to change outcomes.

Prioritization turns ideas into a roadmap.

Scoring use cases on impact, feasibility, and risk brings supply chain, merchandising, stores, and IT to the same table. Aligning on which problems to tackle first and define what success look like for each initiative ensures a shared goal.

Pilot on a slice, then scale what works.

Test priority use cases on a specific region, DC-store lane, or product category to prove value fast and refine the operating model. Only scale the AI interventions that improve accuracy and reliability.

Build a Retail Supply Chain Resilience Strategy

Phase 1

Align

This phase will walk you through the following activities:

You’ll review your organization’s business goals, key initiatives, and capability maps to anchor AI planning in real supply chain priorities. You’ll explore the purpose and structure of AI use cases to build a common understanding of how they support core processes and systems. Finally, you’ll assess your supply chain value chain and previously identified value drivers to confirm which business needs should guide AI use‑case development.

This phase involves the following participants:

• AI initiative lead
• CIO
• Other IT leadership
• Senior business executives and managers accountable for AI initiatives