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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteAvnet says it uses AI to improve quoting, customer decisions, engineering, inventory forecasting, customer service and supply-chain coordination. CIO Max Chan’s approach is to judge these efforts by whether they support a business outcome—not by AI spending alone. The company’s interview describes the intended value mechanisms, but reports no quantified Avnet-specific results.
How does Avnet use AI?
In a February 11, 2026 interview with CIO, Avnet CIO Max Chan described AI as part of the company’s effort to make useful information available to customers and employees. The examples span several business functions; the interview does not identify particular models or systems for most of them.
| Business function | How Avnet says AI is used | Intended value mechanism |
|---|---|---|
| Sales and quoting | Bring pricing, product end-of-life information and country-of-origin information into more complete quotes, delivered more quickly. | Help customers make decisions and support Avnet’s goal of winning business and improving conversion. |
| Engineering | Support engineering design. | Chan identifies design as an area where AI may provide leverage; the interview gives no specific workflow or measured result. |
| Inventory | Support inventory management and forecasting. | Assist planning; the interview does not report forecast-accuracy or inventory changes. |
| Customer service | Make information more readily available to customer-service agents. | Give agents information for customer interactions; no service-quality or handling-time effect is reported. |
| Supply chain | Combine customer, partner and Avnet data to support orchestration and provide customer information. | Use connected information to support supply-chain resilience and coordination. |
For quoting, Chan’s emphasis is on assembling the information a customer needs, rather than on AI as a standalone feature. He says the goal is to make a quote more complete and timely. The interview does not quantify quote speed, conversion, or margin.
How does AI help Avnet’s supply chain?
Chan describes supply-chain orchestration as depending on relationships with customers and partners, data availability and connectivity. Avnet’s Partner Digital Exchange is described as bringing downstream data together with Avnet’s own data so the company can coordinate information and serve customers.
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The technical foundation Chan identifies is a shift away from a monolithic, ERP-centered environment toward a digital- and AI-first architecture. He points to microservices and strong API connections as relevant to connecting with partners. In this account, AI’s usefulness depends not only on models, but also on having relevant data that can move between systems and organizations.
How does Avnet measure AI ROI?
Chan says teams should start with the business outcome they want to produce and then ask whether an AI-supported change contributes to business strategy. More accurate pricing and a more complete customer quote are examples of the outcome-oriented work he values. He cautions against treating AI return as a calculation of technology spend in isolation.
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That is a decision principle, not a disclosed measurement framework. The interview does not provide a formula, target, baseline, or comparison of returns across Avnet’s AI use cases. It also does not show that AI alone caused a business change. Chan’s stated approach is to connect a proposed use to a business objective, then assess its contribution in that context.
What business results has Avnet reported from AI?
The CIO interview reports no quantified Avnet-specific AI results. It provides no conversion-rate increase, profit change, forecast-accuracy measure, inventory reduction, service metric, productivity figure, or financial return. Chan describes use cases and intended mechanisms, not independently measured causal impact.
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For broader context only, McKinsey’s survey conducted March 26–April 5, 2019, included 2,360 participants; 1,872 said they worked at companies that had piloted or embedded AI, or embedded it across multiple areas. In that survey, 63% of respondents reported AI-related revenue increases in business units where AI was used, and 44% reported cost savings in units where it was deployed. These are respondent reports from 2019, not current market measurements and not evidence of Avnet’s outcomes. McKinsey’s survey report also said 58% of respondents reported embedding at least one AI capability in at least one function or business unit, compared with 47% in 2018.
McKinsey defined AI high performers partly by adoption in at least five business activities and average revenue increases and cost decreases of at least 5% in units using AI. The high-performer group was 54 respondents, or 3% of respondents reporting company AI use. Among high performers, 72% said their company aligned AI strategy with corporate strategy and 65% reported a supporting data strategy; the corresponding figures among other AI-using companies were 29% and 20%. These survey comparisons offer context for the importance of strategy and data, but do not establish results at Avnet.
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What data, governance and people practices support the work?
Chan connects AI with data quality: “Leveraging AI means cleansing data so customers can make the right decisions.” That concern runs through Avnet’s examples, from product details in quotes to customer and partner information used for orchestration.
He describes three categories of AI tools at Avnet:
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- Productivity tools, such as ChatGPT.
- AI features built into software Avnet already uses.
- Capabilities developed by Avnet’s own team.
Chan says the company has a governance process for employees requesting tools, including the possibility of receiving an approved alternative. The interview does not specify a universal approval checklist or name all approved products.
Implementation also involves organizational change. Chan identifies change management, workforce upskilling and learning agility as requirements. He says Avnet considers whether an end-to-end process could be made more autonomous, then brings people back into the loop to augment it. That approach frames automation as a way to support work while retaining human involvement, rather than as a blanket removal of people from processes.
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