For the past year, distribution’s AI conversation has centered on one question: how does AI make us more efficient? This keynote reframes it. Efficiency is real — but it isn’t why customers choose you. They choose the distributor who understands them: the rep who notices when an order doesn’t look right, prepares before tomorrow becomes today’s problem, and comes through when it matters most.

Those behaviors have always defined exceptional distributors. They have also always been constrained by time, capacity, and the experience locked inside a handful of veteran reps. What happens when they no longer are?

Jack Carrere, CEO and Co-founder of Prokeep, draws on over a decade spent at distributor counters across North America to argue that AI’s greatest opportunity isn’t operational — it’s relational.

This session introduces a new lens: AI not as a force multiplier measured in speed and output, but as the first service multiplier the industry has ever had. The winners of the next five years won’t be the distributors with the smartest technology. They’ll be the ones customers can’t imagine leaving and the result of this is more orders because those customers not only don’t leave, they choose to buy more from you.

In this 25-minute session, Jeff Metersky challenges two widespread assumptions in supply chain: thatagentic AI is a direct path to autonomous planning, and that AI can automate decisions without the guidance of planners.

The real problem: most planning solutions are built on 30 year-old static models that have requiredplanners to turn recommendations into decisions. For agentic AI to power autonomous supply chain, it must learn what planners know.

In this session, you will learn:

• Why spending time on HOW we make decisions matters as much as deploying new tools
• How agentic AI can learn the bounds of “autonomous” decision making and when a decisionrequires a “human-in-the-loop.”
• The difference between physical supply chain resiliency and organizational decision-makingcapability under stress
• The RAPID principle: evaluating which decisions are candidates for automation based onreversibility, financial impact, and risk tolerance

Praveen Gautam, Chief Information Officer, BlueLinx

Praveen Gautam has served as Vice President of Information Technology and Chief Information Officer since May 2021. In this role, Praveen is responsible for working closely with BlueLinx leadership to understand their business strategy while developing and executing an IT strategy that supports the Company’s digital transformation journey. Praveen has more than 25 years ofInformation Technology experience, with expertise in IT Strategy, IT Governance, Enterprise Architecture, IT Security & Risk Management, Enterprise Applications, Business Intelligence & Reporting, Data Management, Application Integration, Web Application Development, and IT Audit & General Controls. Prior to joining BlueLinx, he held IT leadership positions at Altium Packaging, Brambles, and Tellabs.

Praveen holds a Bachelor of Technology degree in Electrical Engineering from Indian Institute of Technology, Kanpur, and a Master of Business Administration (MIS & Finance) from the Illinois Institute of Technology in Chicago.

For distributors, that changes a website’s job: less a lead funnel, more a trusted resource that helps an already-serious buyer confirm they’re making the right call. This session looks at what that shift means for your content, chat, and site structure, and leaves you with a practical way to check whether your website still earns its keep.

ou already have a wealth of customer and operational data, but struggle to translate it into actions that improve service, increase share of wallet, or reduce friction.

In this session, I’ll share how AI can be applied practically in distribution environments to improve customer experience where it matters most:

• Identifying at-risk customers before revenue declines 
• Enabling sales teams with better insights, not more dashboards, simplifying interactions across inside sales, field reps, and digital channels 
• Improving fill rates, recommendations, and order accuracy 

We’ll go beyond theory and walk through real-world use cases, what worked, what didn’t, and the trade-offs involved—especially in environments with legacy systems and complex product catalogs.

You already have a wealth of customer and operational data, but struggle to translate it into Attendees will leave with a clearer understanding of how to apply AI to drive measurable outcomes—such as retention, revenue growth, and operational efficiency—not just for experimentation.