Show Notes
What if your business doesn't actually own its website, customer data, or digital marketing infrastructure? It's an uncomfortable question, but one that many founders never ask until they try to switch providers and discover just how difficult it is to leave. In this episode of AI at Work, I welcome David V. Kimball, Co-Founder and CEO of lilAgents, for a conversation that challenges many of the assumptions businesses have made over the last decade about websites, software subscriptions, AI, and digital ownership. David argues that convenience often comes at a hidden cost, with businesses gradually handing control of their most valuable digital assets to platforms that make it increasingly difficult to move elsewhere. We begin by exploring how so many organizations found themselves locked into ecosystems that seemed like the simplest option at the time. Website builders, ecommerce platforms, marketing suites, hosting providers, and CRM systems all promise convenience, yet many businesses only discover the downside when prices increase, features disappear, or they attempt to migrate to something better. The conversation then turns to artificial intelligence and where it is genuinely making a difference today. Rather than focusing on AI chatbots that have been added to almost every product, David explains why AI agents are becoming far more interesting. These systems can perform real work, connect different applications, automate repetitive processes, and solve practical business problems while people focus on higher-value work. One example that stood out involved a Shopify store with thousands of products that had accumulated years of inconsistent metadata. Using AI agents connected directly to Shopify's APIs, David was able to automate work that would have taken weeks by hand, helping improve search visibility and delivering measurable growth in organic revenue. It serves as a practical reminder that AI delivers the greatest value when solving real operational challenges rather than simply generating content. We also spend time discussing the hidden costs many businesses overlook. From paying for CRM contacts that no longer engage to running websites on platforms with far more functionality than they actually need, David explains why simplifying technology stacks can often reduce costs while improving flexibility at the same time. The objective isn't simply spending less. It's building systems that businesses genuinely own and can adapt as their needs change. Another theme running throughout our discussion is portability. Whether we're talking about websites, marketing platforms, AI models, or business data, David believes organizations should avoid becoming dependent on any single vendor. As AI continues to develop, he argues that businesses should think carefully about building modular systems that make it easy to change providers instead of finding themselves trapped by the next generation of platform lock-in. This episode offers a refreshing perspective on AI by moving beyond the hype and focusing on practical outcomes. It also raises an important question about the future of digital business. Are companies investing in technology they truly control, or are they simply renting increasingly expensive pieces of someone else's platform? How much of your digital business do you genuinely own today? And if one of your technology providers disappeared tomorrow, how easily could you move somewhere else? I'd love to hear your thoughts after listening.
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Are businesses investing in tomorrow's artificial intelligence while continuing to operate with yesterday's organizational structures? And could that explain why so many enterprise AI initiatives struggle to deliver the results leaders expect? In this episode of AI at Work, Neil C. Hughes is joined by Jesper Schleimann, Head of Business AI for EMEA at SAP, to discuss why adopting AI technology and achieving meaningful business transformation are two very different challenges. Jesper shares what he's seeing across organizations in Europe, the Middle East, and Africa, where AI adoption is accelerating but progress remains uneven. While some businesses are already reporting improvements in productivity and customer service, others are discovering that isolated projects and impressive demonstrations do not automatically translate into business value. The conversation examines why companies need to begin with a business problem rather than a technology solution, and why adding AI to inefficient processes can limit its potential. They also discuss the growing interest in autonomous AI agents and the practical realities of allowing intelligent systems to take action inside an enterprise. Jesper explains why business context, defined authority, reliable data, and human accountability all play an important role in building trust. As businesses progress toward increasingly autonomous operations, what happens to traditional management structures? How should leaders decide when humans need to intervene? And could the greatest opportunity come from redesigning how work happens rather than simply making existing tasks faster? Jesper also introduces ideas from his book, The Intent-Driven Leader, which examines the changes in leadership and organizational thinking required as AI becomes more capable. With companies investing heavily in enterprise AI, this conversation offers a timely reminder that technology alone cannot deliver organizational transformation. Are businesses ready to rethink the way they operate, or will familiar structures and habits continue to limit what AI can achieve?
How can customs agencies inspect vast volumes of cargo without slowing legitimate trade or handing consequential decisions entirely to an algorithm? In this episode of AI at Work, I speak with David Smason, founder of CargoSeer and Head of Customs AI at BigBear.ai, about a form of workplace AI that operates far from the usual office productivity conversation. CargoSeer was built to help customs teams combine cargo documents, x-ray images, routes, commodity codes, tariffs, and historical patterns before an experienced officer makes the final decision. David explains why traditional customs work can overwhelm even highly trained people. A single shipment may involve dozens of documents, different languages, handwritten information, revenue questions, and national security risks. His vision is one operator overseeing hundreds or eventually thousands of analyses while concentrating attention where human experience matters. He compares the result to a James Bond command center, although the real work involves careful data processing, explainable recommendations, and a clear role for the officer. We discuss CargoSeer’s internal findings that an operator using its supporting technology can assess a shipment in under five seconds, compared with several minutes in some existing processes. That speed comes with an important qualification. False positives may delay legitimate cargo, while false negatives may allow serious risks through. David says each customs agency must define its own risk appetite, operating procedures, success measures, and thresholds rather than accept a universal model. Human feedback remains central to the system. David compares an AI model to a baby that must be taught continuously. When an officer corrects a false alert, the correction can improve later recommendations. He argues that operators value this influence because their knowledge becomes part of the technology instead of being discarded. We also discuss localization and proof. David estimates that CargoSeer’s models are currently about 85 percent standardized, with the remaining work shaped by local data, language, culture, tariffs, agreements, and operating practices. His advice to public agencies is direct: demand evidence that the system works on your data, in your environment, and within your workflow. Test it, try to break it, and define performance before live deployment. Could this model of AI-supported judgment work in other high-consequence roles where speed, evidence, and accountability must coexist? Listen to the conversation and share your thoughts with me.
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