AI, Data And Real-Time Insights

Akash Khurana is the Executive Vice President and Chief Information and Digital Officer at Wesco International.

Artificial intelligence (AI) is transforming supply chain management, allowing companies to turn data into meaningful action across their operations. At Wesco, we’re utilizing data-driven AI across our entire enterprise to improve processes and empower our sales team to be more customer-focused.

It’s a profound statement given the history of the company. Wesco is a leading provider of business-to-business distribution, logistics services and supply chain solutions. Our three strategic business units serve the electrical and electronics industries; utilities and broadband industries; and communication, security and data center businesses. We are transforming into a technology-driven service organization operating at the intersection of global supply chains.

Growth through acquisition has long been a business strategy for Wesco, leaving us with a large mix of various technologies. The infrastructure is massive, and it presents quite a few integration challenges. We consider AI automation a critical component in our digital transformation initiative, which helps things run more smoothly while driving stronger results across sales, inventory and customer service.

Here’s a little bit about how we’ve approached AI and what companies considering similar investments can take away from everything we’ve learned thus far.

Real-Time Data For Adaptive Inventory Management

Inventory management is central to any supply chain organization’s ability to operate efficiently. As disruptions become more frequent, real-time data becomes essential for balancing stock levels with demand fluctuations.

Keeping this in mind, we built our “Inventory Command Center” (ICC), implementing a centralized platform that integrates data from across our supply chain and provides visibility into inventory levels, stock movement and demand forecasting.

Using AI within the ICC, we can quickly adjust our stock levels and purchasing decisions based on real-time conditions—meaning we don’t have to rely on historical data that may not have been accurate in the first place and is irrelevant to today’s market conditions.

Enhancing Customer Interaction Through AI In Sales

In addition to inventory management, we’ve also been extending data-driven AI to our sales teams through a new CRM system called our “Unified Sales Desk.” A proprietary platform, the desk gives inside and outside sales representatives a single view of the customer. More importantly, its AI capabilities make it possible to analyze important data points—like inventory, pricing and customer history—and offer predictive insights to better inform customer interactions.

The goal is to ensure that every one of our more than 8,000 sales representatives will have the most timely and relevant information at their fingertips, allowing them to respond to customer inquiries and anticipate their needs more effectively. Our sales teams can also quickly identify cross-sell and up-sell opportunities based on data-driven insights, enhancing customer experience and fostering long-term relationships.

Practical Advice For Adopting AI At Scale

Focusing on foundational practices is key for organizations looking to scale AI effectively. Here are some principles we learned from our journey:

1. Secure core processes. Before implementing AI applications, establish strong data governance and make sure core processes are solid. Know where your digital assets are. Trustworthy, high-quality data will support your AI initiatives over the long term.

2. Prioritize process alignment over full integration. Instead of focusing on systems transformation or complete systems integration, start smaller with process integration and process alignment. This will provide a straightforward roadmap for what’s possible, avoid wasting potentially millions of dollars in the wrong areas and help you implement AI without too many disruptions.

3. Focus on real-time data for agility. With supply chains in constant flux, real-time data can be the difference between sinking or swimming. Investing in systems that provide live updates has been invaluable in managing shifts in demand and ensuring timely responses to supply chain changes.

4. Use customer-centric AI solutions. To get the most from AI, consider how it can enhance customer experiences. Tools like the Unified Sales Desk, for example, help our team to serve customers more effectively by providing quick, data-backed insights.

5. Develop cross-functional AI teams. AI success requires collaboration across functions. Having a team that combines data science with operational expertise can help ensure that AI solutions are practical and aligned with business goals.

Charting A Practical Path Forward

For supply chain companies, adopting AI is not just about upgrading technology; it’s about building a system that can evolve with market conditions. By staying focused on how AI can make core processes more efficient, we’re gradually shifting toward a model where data truly drives decision making. For other organizations considering AI, starting with targeted, practical applications and building from there can help ensure that AI becomes an integral, reliable part of business strategy.

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