‍Lumi AI Webinar: AI in Supply Chain

AI is becoming a cornerstone tool of how leading companies make smarter decisions, manage complexity, and respond faster to change. From manufacturers and logistics firms to retail giants, organizations are moving past dashboards and into true supply chain automation.

What this webinar uncovered

In this session, industry experts from Lumi AI and some of the world’s most experienced supply chain leaders break down how Generative AI is transforming supply chain operations today. The conversation moves from demand forecasting and inventory optimization to procurement and transportation management, focusing on real-world examples, measurable ROI, and the expanding need for cross-functional collaboration.

Key speakers include
• Ibrahim Ashqar (CEO, Lumi AI)
• Kunal Thakker (Walmart, Newegg, UPS, SEAIR Global)
• Colin Kessinger (End-to-End Analytics, Accenture, Stanford Lecturer)

These experts share practical insights and firsthand lessons on implementing AI in supply chain management at scale. They also explore the common traps that companies fall into—such as beautiful dashboards that lead to no action—and unpack what McKinsey calls the GenAI paradox: when adoption grows but real business value stalls.

This conversation is all about moving from ideas to implementation, from promise to performance.

1. The value isn’t more visibility — it’s better decisions

Many supply chain teams already have visibility tools. But visibility without decision-making power is just more data with no direction.

The real value of AI in supply chain planning comes when artificial intelligence helps teams decide what to do next. Whether it is reordering inventory ahead of demand, rerouting goods due to disruption, or adjusting workflows to prevent delays, AI enables action.

Generative AI and augmented analytics allow teams to shift from simply reporting what happened to understanding what will happen, and more critically, what to do about it.

Bottom line: To get value from AI in the supply chain context, the tool must help teams make decisions not just give visibility to a problem.

2. You don’t need perfect systems or data to start

A common misconception about AI in supply chain optimization is that it requires perfect data or an entirely integrated tech stack. This belief stops progress before it even begins.

The truth is, companies seeing success are starting with specific and known pain points—like shift scheduling, supplier disruptions, or demand volatility—and solving them one by one.

Instead of asking for a 12-month AI roadmap, they are asking:

• What is the hardest decision we face today
• Where do we lose the most time or accuracy
• What process breaks the most under pressure

That’s where AI in supply chain and logistics delivers value: tackling operational bottlenecks that hurt performance every day.

Bottom line: You do not need perfect data. You need a high-impact use case and the willingness to start.

3. The real barrier isn’t tech — it’s culture

Most AI in supply chain initiatives fail not because the models are wrong but because teams never adopt them.

If the tools do not fit into your team’s rhythm—if they feel too slow, too complex, or too disconnected—they will get ignored, no matter how smart the algorithm is. Success in supply chain AI depends on one thing: whether the solution fits how your team already works. It must be fast, intuitive, and useful without requiring a change in culture.

That is how companies like Walmart and UPS are seeing success, not by forcing transformation, but by enhancing workflows teams already trust.

Bottom line: If your team will not use the AI, it does not matter how good it is.

Where AI Is Delivering Real Value in Supply Chain Today

If you are wondering how AI is being used in supply chain, here are the areas where it is having the greatest impact:

• ‍Demand forecasting and inventory planning: Still the most consistent and high-impact use case for artificial intelligence in supply chain operations
• ‍Operational automation: Especially in high-volume, repetitive decisions where speed and precision are critical
• ‍Supply chain visibility with action: Dashboards are not enough, AI must lead to proactive movement and improved decision flow
• ‍Food and beverage analytics: Gaining traction in fast-moving retail environments with constantly shifting demand and inventory dynamics

Three takeaways about using AI supply chain

1. Focus on real decisions, not just better dashboards
2. Solve one workflow before trying to transform your system
3. Make the AI feel like part of the team, not a separate system

The leaders in AI are teams that adopt AI pragmatically: starting small, proving impact, and expanding based on real business value.

In Short:

• Start with decisions, not dashboards
• Fix one real workflow before redesigning your system
• Make the tool feel like part of your team

Missed the session?

You can still watch the full recording to learn how the most innovative teams are applying artificial intelligence in supply chain management to move from reactive operations to proactive advantage.

At Lumi, we’re helping supply chain teams move from siloed data to automated workflows using AI, fast, and without replacing their existing tools.

If you're exploring how predictive analytics or retail analytics could actually work inside your operations, let’s talk