Avnet uses AI as an operating capability across its supply chain and customer-facing work—not as a standalone technology experiment. Under CIO Max Chan, the company applies it to component data, sales and engineering support, demand forecasting and supply-chain decisions, while investing in modern architecture, data quality, governance and workforce skills.
Who is Max Chan, and what is his AI strategy?
Max Chan, also known as Leng Jin Chan, has been Avnet’s CIO since 2019. Avnet says he leads IT, cybersecurity, digital strategy and transformation, and oversees its global IT team. He became a senior vice president in 2021; earlier in his career at Avnet, he led global-supply-chain IT and Avnet Technology Solutions in Asia-Pacific.
Chan’s central test for technology investment is whether it helps the business accelerate, redesign or reimagine a capability. He describes the strategy as “how we are enabling the business,” with technology, digital enablement and AI considered in relation to business priorities—not as separate initiatives looking for a use case.
That approach makes data quality foundational. Avnet uses AI to cleanse data so customers and employees can make better-informed decisions. Chan’s stated aim is practical: “Everything that we’re doing, from an AI standpoint, helps to fulfill that philosophy.”
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Where does Avnet apply AI across its supply chain?
Avnet describes AI as supporting people’s decisions across several connected workflows. Its published examples range from helping a customer identify a suitable component to using customer, supplier and market information to inform demand projections.
| Operating area | How AI and data are used | Intended benefit |
|---|---|---|
| Sales enablement | Assemble information such as pricing, end-of-life status and country of origin for quotes; help customer-service agents find answers; suggest pin-to-pin component alternatives that retain required capabilities and power specifications. | Faster quoting and service, and help finding viable substitutes. |
| Engineering design | Use data and AI insight to support component selection and help customers identify technically suitable parts. | More informed design choices and component discovery. |
| Inventory forecasting | Combine historical customer and supplier data with market trends to project demand. Avnet Silica says its historical data base spans more than 30 years. | Better-informed demand and inventory planning. |
| Supply-chain orchestration | Use ecosystem data to inform decisions about moving products from point A to point B, including which considerations matter for a given movement. | Support decisions aimed at profitability and customer outcomes. |
The forecasting figure is specific to Avnet Silica’s 2024 description of the data used with market trends; it should not be read as a claim that every Avnet forecast uses the same dataset. More broadly, Avnet’s descriptions present AI as decision support and enablement across capabilities, not as proof that every supply-chain decision is automated.
What does Avnet’s AI-enabled product catalog do?
The product catalog is Avnet’s clearest published implementation example. The company says its AI-Enabled Product Catalog applies machine learning and generative AI to more than 16 million components. The work improves component classification and product descriptions, which in turn supports more advanced search and product recommendations.
Avnet reports that the catalog has increased sales, strengthened supplier partnerships, reduced manual intervention and lowered operating costs. The company also reported a consistent monthly revenue increase when announcing the project’s 2025 CIO 100 Award. It did not publish a percentage for that increase in the cited announcement, so it cannot be used to estimate the size of the revenue change.
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Why is Avnet moving beyond a monolithic ERP?
Chan says a monolithic ERP environment was not sufficient for the transformation Avnet wanted. Rather than make every new capability depend on the constraints of a decades-old system, the company supported greenfield builds where needed. The intended direction is a digital-first, AI-first architecture that can evolve continuously.
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“Greenfield” here means building a capability on a fresh foundation when the existing environment is not fit for the purpose; it does not establish that Avnet is replacing every legacy system. The architectural choice is tied to the business case: modernize where a new foundation enables the capability, while retaining systems and infrastructure where a wholesale move is not justified.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How does Avnet manage AI costs, governance and skills?
Chan has highlighted cloud spending as a reason to pair modernization with FinOps and governance. Costs can rise quickly without controls, so Avnet retains a hybrid posture rather than moving everything to the cloud. That makes financial oversight part of the operating model, alongside governance of AI use and the continuing need to upskill employees.
For Chan, investment discipline comes back to outcomes: an AI initiative should accelerate, redesign or reimagine a business capability. This keeps ROI in view and helps distinguish an operational improvement from a technology experiment that has no clear connection to business priorities.
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