Why You Can’t Ignore AI Moving to the Edge | Joel Polanco, Senior Hardware Product Manager (Intel)
How Intel’s Joel Polanco sees edge AI transforming retail, cutting cloud costs, and why product leaders can’t ignore the shift from cloud-only to hybrid architectures.
TL;DR
In this episode of LaunchPod, we sit down with Joel Polanco, Senior Hardware Product Manager at Intel’s Edge Computing Group, to explore why product leaders need to rethink where their AI models run. From cloud economics to retail automation and hybrid architectures, Joel explains how moving AI to the edge reshapes cost structures, customer experience, and product strategy.
In this episode, we discuss:
Why companies start in the cloud but shift to the edge once usage and costs scale (03:00)
How retailers are using conversational agents and inventory automation to optimize workflows (04:00)
Why latency, privacy, and security make edge deployments essential for customer trust (09:00)
What agentic AI means for hybrid architectures and the future of compute (29:00)
1. AI Economics & Architecture: From Cloud to Edge
Companies deploy AI in the cloud first because it’s fast and flexible—but costs balloon with scale. As Joel explains:
“First you deploy to the cloud and then you validate the use case… Once that usage starts to grow, then you know, this cost is not going to be sustainable for me if I scale to thousands of locations.”
At that point, product leaders face a tradeoff: stick with OpEx-heavy cloud bills or shift to CapEx investments in edge infrastructure that pay off over the long term. The right decision often hinges on product-market fit and cost sustainability at scale.
2. Retail Use Cases: Conversational AI & Inventory Automation
Retailers are leading adopters of edge AI, applying it to both IT support and inventory management:
“One use case would be as an IT agent helping their employees resolve printing issues or point of sale issues. Another use case is like, hey, this agent is here to help you with inventory so you can take a picture of a shelf, send it to the agent, and then it'll come back and tell you with, hey, your order is confirmed. Go and restock this area of the shelf with so much product.”
Joel describes how these interfaces collapse multiple workflows into natural, conversational exchanges:
“Imagine that interface changing where you take a picture [of a shelf] and it knows this shampoo is out. It fills out all those fields and text boxes and hit send for you. All you really did was just send a picture.”
By bringing these workloads on-prem, retailers can reduce API costs, streamline operations, and deliver faster employee and customer experiences.
3. Security, Privacy, and Reliability as Product Drivers
Edge AI isn’t only about economics, it’s also about resilience and trust:
“If the internet's going down, can your application still function? That's the problem we're typically dealing with is, network connectivity problems and then still needing to function and operate like a retailer. If their point of sale goes down, it's fully dependent on the cloud. They're in a world of hurt during that, even if it's an hour or two hours.”
For product leaders, AI deployment strategy directly impacts customer trust, security posture, and business continuity.
4. The Future of Edge AI: Agentic AI and Hybrid Architectures
Joel foresees agentic AI starting in the cloud and then proliferating to the edge as computing improves. He also predicts significant infrastructure change:
“I believe this is gonna result in a huge compute refresh. There's gonna be a big network, memory and compute refresh both in the cloud and the edge as a result.”
The future is hybrid: batch analysis in the cloud, real-time inference at the edge. For product leaders, this means planning architectures that balance latency, security, and cost while preparing for agentic AI applications.
Links
Chapters
00:00 Introduction
03:32 Retail Applications of Edge AI
04:57 Cost and Implementation Considerations
06:18 Real-World Use Cases and Challenges
12:00 Future of AI and Edge Computing
30:42 Practical Advice for Product Executives
33:46 Conclusion
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