Case Study: Inside Avnet’s AI Playbook — Governance-First Innovation in a Global Supply Chain

Global supply chains are increasingly complex, data-heavy, and vulnerable to disruption. For B2B companies, delayed production, inaccurate forecasts, and inefficient sourcing can mean lost revenue and customer dissatisfaction. AI is no longer optional; a well-governed, strategic framework can drive inventory management, cost savings, faster decision-making and operational resilience. 

Avnet, a global distributor and provider of electronic components and IT solutions, implemented an AI governance framework into its inventory management, logistics, engineering and customer engagement workflows. The initiative strengthens the company’s broader business by accelerating design, simplifying sourcing, and enabling a more resilient supply chain.

The Challenge: Tracking Inventory, Suppliers and Data 

Avnet CIO Max Chan says sourcing and inventory management had long been major pain points for Avnet’s large, multi-faceted organization. The company investigated ways to make its supply chain smarter and more resilient, while freeing employees to focus on higher-value work. 

“Operating in a global market with multiple suppliers is data-heavy, and knowing where and when inventory is consumed is critical,” says Chan. 

“AI gives us deeper insights and connects processes so production lines never stall. Take planning, for example. We follow the SCOR [Supply Chain Operations Reference] model, which means assessing demand and supply on a per-customer basis in a constantly changing, complex environment. AI helps us make those assessments quickly and accurately.”

AI Tools in Action: Inspections, Tech Support, and Design 

Avnet’s “electronic component distribution” covers a lot, including sourcing, transportation, parts verification, technical support, supply chain management and delivery. Chan says AI can make a big difference across all of these areas.

One example is inspections. AI-driven visual systems flag component-level anomalies, reducing manual checks while increasing coverage. It’s also used in tech support. Chan says tech support is often the first touchpoint for customers, whether they’re looking for a data sheet or design assistance. And AI as a design assistant is gaining traction in electronics, where tools like Retrieval Augmented Generation (RAG) can scan hundreds of data sheets in seconds, helping engineers deliver results.

“RAG is one of the AI tools we use to support field application engineers working with OEM design teams,” says Chan. “It’s all about speed, accuracy and making every interaction more valuable.”

Many B2B companies struggle with dense technical documentation and slow response times. Avnet’s RAG-enabled AI accelerates knowledge access while protecting proprietary data and improving accuracy. 

“Our business relies on the expertise of our people, and the goal was to give them a productivity boost without forcing them to change the way they work,” says Chan.

He says embedding AI into the specialized systems and familiar office apps the company’s teams already know eases the transition.

“This isn’t just about efficiency,” Chan says. “It’s about creating solutions that benefit customers for years to come. Across our supply chain programs, AI has improved forecast accuracy, lifted inventory turnover, and reduced risk exposure. 

Improved forecast accuracy lowers working capital waste, while shorter design cycles accelerate time-to-market.

“In automotive, for example, our global hub solution cut lead times and improved end-to-end visibility so customers could make confident, data-driven decisions in hours instead of days.”

A Win for Preventive Inventory Management

“One of the biggest wins has been using AI for preventative inventory management, helping us stay ahead of issues like aged inventory and forecast future consumption needs more accurately,” says Chan.

Before AI, inventory visibility was limited.

The company now tracks key benchmarks to ensure new AI tools are driving results. These include forecast accuracy, inventory cost and turnover, time to design and deploy custom solutions, and the ratio of standardized versus bespoke solutions.

Chan adds, “We also measure risk mitigation and an efficiency metric we love — line items to headcount—because it shows how AI helps us scale throughput without adding resources. All of this is visible in dashboards for transparency and continuous improvement.”

While Avnet does not disclose exact figures, Chan says the initiatives have driven measurable gross margin uplift in inventory visibility and turnover in select programs. The applications are used to enhance decision-making rather than replace it. AI tools also help the company quantify and reduce risks, maintaining supply chain resilience. 

Cultural Transformation and Employee Empowerment

While the AI rollout directly affects ROI, it impacts culture, too. Thoughtful AI implementation with direct employee input can spark innovation while building business agility and resilience.

“What excites me most is that AI isn’t just a tech upgrade — it’s a cultural shift,” says Chan. “Across Avnet, there’s this sense of momentum because AI is helping us accelerate, reimagine and transform how we work. It directly impacts employees’ ability to respond to customer requests and improve agility.”

Employees build AI agents, and tailored learning paths make AI literacy accessible to everyone.

“We’ve challenged teams to learn, experiment and share experiences — successes and failures — because innovation is an all-play,” Chan says. 

“For me, the excitement comes from seeing collaboration and creativity thrive. It’s shaping a future where AI doesn’t just support the business, it drives it forward.”

Recommendations for Implementing AI Frameworks 

Chan says Avnet will continue scaling its AI program responsibly. The first step is done: They’ve built out a governance framework focused on strong management, ethical standards and continuous improvement. Next steps include implementing adoption across business units and embedding AI into more processes and tools, while strengthening governance and compliance as they grow. 

“We’re investing heavily in training and change management because our people remain at the heart of this transformation,” says Chan. “The goal is to make sure technology serves our broader business strategies and customer needs, not the other way around. AI is a powerful enabler, but success depends on pairing it with trust, transparency and a culture that’s ready to embrace what’s next.”

Chan has these tips for companies implementing or expanding AI frameworks:

• Start with clear objectives and data you trust. “AI is only as good as the data it uses,” Chan says. Without trusted data, organizations end up reintroducing manual checks that negate AI’s speed advantage.
• Integrate AI into existing workflows: Employees won’t feel like they’re learning a whole new system.
• Build frameworks for transparency and accountability: “Governance matters too,” says Chan.
• Start small: “Start small, experiment, learn fast, and scale what works,” says Chan.
• Don’t forget collaboration: Suppliers, customers and tech partners should all be part of the AI conversation. 

Bottom line, says Chan, “Spend more time on the front end — clean data, clear goals, and strong governance — and you’ll reap enormous benefits when it’s time to execute.”