Unlocking value, innovation, and growth through AI-driven supply chains

Introduction 

CPG and retail supply chains continue to face increasing pressure due to rising e-commerce demand and growing customer expectations for faster delivery. At the same time, global disruptions, complex tiered sourcing models, and limited visibility make it difficult for organizations to respond with agility. Furthermore, traditional capacity planning often cannot meet volatile, nearshore-driven demand, while inflation and labor shortages further impact both cost control and service reliability. 

In addition to these external forces, the internal landscape presents its own challenges. Fragmented data systems, manual workflows, and siloed insights slow down decision-making and reduce productivity across key supply chain functions. 

Agentic AI offers a 360-degree approach to addressing these issues by enabling intelligent automation, real-time insights, and adaptive decision-making at scale. 

In a recent webinar co-hosted by Fractal and Microsoft, Sandeep Bogharaju, Client Partner – Supply Chain Analytics Practice at Fractal, and Felice Miller, Director, Global Supply Chain Strategy, WWRCG at Microsoft, and Rochelle Fleming, Director, Partner Marketing, shared how AI Agents can drive this transformation and unlock new value across the supply chain. This article offers a sneak peak of the key concepts this webinar (and the associated white paper) covers.  

Enterprise use cases for GenAI adoption 

Organizations can leverage generative AI at various levels, depending on their readiness and business priorities. The following use cases do not follow a fixed path but instead demonstrate ways to unlock value, ranging from enhanced insight generation to full-scale intelligent orchestration.

• Insight Summarization (Foundational Layer) 
  GenAI helps teams synthesize insights from ERP, WMS, TMS, and CRM systems. This reduces cognitive load and shortens decision cycles across core supply chain functions.

• Training Assistants (Enablement Layer) 
  AI supports workforce enablement by generating synthetic data, summarizing standard operating procedures, and simulating processes. These capabilities improve training quality and operational consistency. 

• Workflow Copilots (Decision Support Layer)
  Copilots integrate into daily workflows with increasing confidence within the organization. They guide procurement negotiations, support S&OP reviews, and deliver contextual coaching within existing workflows.

• Hyper-Automation (Execution Layer)
  AI agents now begin to monitor, detect, and automate actions across procurement, inventory, manufacturing, and service. This improves speed, compliance, and output quality.

• Agentic AI (Orchestration Layer)
  At the highest maturity, intelligent agents orchestrate decisions across planning and execution. They simulate trade-offs, manage end-to-end workflows, and enable adaptive, insight-led, and resilient supply chains.

Harnessing the potential of Agentic AI 

Today’s supply chains operate under constant pressure from volatility, complexity, and speed. Traditional systems provide information but lack the ability to act in real time. 

Agentic AI changes that. Agentic AI can transform supply chain decision-making through a continuous Sense–Reason–Act–Simulate loop. It senses signals across traditional processes, fragmented systems, and diverse data streams. Agentic AI reasons by contextualizing those insights and learning from past decisions. It then acts by recommending or autonomously executing next-best actions. Finally, it simulates outcomes to anticipate impact and refine future responses.   

By enabling supply chains to sense disruptions early, reason through trade-offs, act decisively, and simulate future scenarios, AI agents transform fragmented operations into an intelligent, self-improving system. This is not just automation but insight-driven orchestration at scale. This approach enables supply chains to shift from reactive and siloed operations to adaptive, intelligence-driven orchestration at scale. 

As Felice Miller of Microsoft explains, “Agentic AI is revolutionizing supply chain optimization by driving efficiency and adaptability. By leveraging these advanced systems, we can streamline operations, reduce costs, and enhance responsiveness to market changes. Embracing Agentic AI will pave the way for sustainable growth and transformative impact.” 

Transforming supply chain processes with AI 

Agentic AI is driving a fundamental shift in supply chain operations by turning fragmented, manual workflows into intelligent and adaptive systems. Organizations can unlock speed, foresight, and resilience at scale by embedding the Sense–Reason–Act–Simulate loop across critical processes. Here are a few key use cases: 

• Planning transformation (sales, inventory & operations planning) 
  AI agents detect shifts in demand and supply signals, analyze cross-functional constraints, and generate proactive risk alerts and alignment opportunities. They also simulate planning scenarios to support consensus-driven decisions, helping reduce latency and improve profitability. 

• Sourcing transformation (procurement) 
  AI continuously senses market and supplier volatility, reasons through historical data and negotiation patterns, and acts with dynamic recommendations. It also simulates alternate supplier strategies to enable smarter, faster, and more cost-effective sourcing decisions. 

• Production transformation (manufacturing) 
  AI agents sense machine-level deviations and process inefficiencies, reason to identify root causes, and act with real-time guidance. This enables manufacturers to move from reactive to predictive operations with higher uptime and reliability by simulating failure risks and throughput scenarios. 

Implementing AI-driven automation 

Successfully adopting AI-driven automation requires a clear and structured approach. Fractal recommends a four-phase framework to guide implementation: 

Phase 1: Discover automation opportunities 
Audit existing workflows to identify bottlenecks through detailed process mapping and collaborative workshops. 

Phase 2: Define value proposition and prioritize 
Create clear business cases highlighting efficiency gains and feasibility of AI implementation.  

Phase 3: Establish human intervention levels 
Determine the optimal balance of human oversight based on AI accuracy, regulatory requirements, and operational impact. In this phase, you should also assess reskilling and change management requirements. 

Phase 4: Design to-be processes and implementation roadmap 
Embed AI into workflows strategically, scale successful pilots rapidly, and prioritize quick wins through agile implementation and ongoing value tracking. 

Conclusion 

Integrating AI into supply chain operations calls for a strategic shift that drives greater efficiency, smarter decision-making, and continuous innovation. 

To explore this further and take the first step toward AI-driven automation, watch the video below. You can also click here to download the slides for a quick overview of the key points discussed. 

Explore the topic further by downloading our (ungated!) whitepaper “Unlocking value, innovation, and growth through AI-driven supply chains”.