Sparks & Signals: An AI-Powered Audio TLDR

Why the Future of Work Will Be Built on Internal Reinvention

A recent conversation with my old friend Audrey Piquemal got me thinking about how enterprises are approaching workforce transformation in the #AI era. Merci, Audrey! Too often, companies respond to change by replacing experienced employees with newer and cheaper talent. It may improve short-term financials, but it also strips away institutional knowledge, customer context, and operational judgment that take years to build. As AI takes over more routine execution, those human capabilities may become even more valuable. I wrote about why the future may belong to organizations that focus less on replacement and more on reinventing the talent they already have, along with the emerging platforms trying to make that possible.

From Systems of Record to Systems of Action: The Agentic Re-Invention of Retail & CPG Enterprise Software

Every July, Microsoft's fiscal year turns over and I sit down to think seriously about where to focus my energy for the year ahead. For FY27, one of my five priority areas is 𝑨𝒈𝒆𝒏𝒕𝒊𝒄 𝑹𝒆𝒊𝒏𝒗𝒆𝒏𝒕𝒊𝒐𝒏, and I have written up my thinking on what it means specifically for Retail and CPG enterprise software. 𝐓𝐡𝐞 𝐬𝐡𝐨𝐫𝐭 𝐯𝐞𝐫𝐬𝐢𝐨𝐧: the first wave of enterprise software remembered. The second wave explained. The third wave acts. Agentic AI is not arriving as a set of incremental improvements to the platforms that have run retail and CPG operations for the past two decades. It is arriving as an autonomous orchestration layer that runs the workflows those platforms currently support, while leaving the systems themselves intact. That last point is the one I spend the most time on with CIOs. This is not about ripping out your ERP or your WMS. It is about giving them an autonomous brain. The years of custom configuration, the edge-case workflow rules, the compliance logic built up through live operational experience, all of that becomes the skill library that agents invoke. The institutional knowledge stays. The human bottleneck in the middle of the workflow goes.

From IoT to AI Agents: Why Sensors, Processors, and Actuators Still Matter

For years, my passion has been in the world of IoT and building connected systems that can sense, interpret, and respond to the world around them. As I’ve spent more time building AI agents, I kept noticing something familiar: The architectural DNA behind agentic AI looks remarkably similar to the systems many of us built in IoT.

When the Numbers Look Fine and the Business Isn't

Most retail analytics platforms are built around queries. You form a hypothesis, build a report, get an answer. The limitation is that the most valuable insights are often hiding in questions nobody thought to ask. This is a spotlight on Microsoft partner DataGenie, and how their approach to proactive, continuous analytics changes that equation for retail and CPG teams, and why the difference between finding an insight on Monday versus three weeks later matters more than most organizations realize.

Building an Autonomous Retail Store with AI Agents: A Technical Walkthrough

After publishing my piece on letting AI agents run a retail store, the most common question I received: how does it actually work? This post answers that in full technical detail. Three agents, three different foundation models selected for task fit rather than convenience, one sequential pipeline orchestrated through 𝑮𝒊𝒕𝑯𝒖𝒃 𝑪𝒐𝒑𝒊𝒍𝒐𝒕 𝑪𝑳𝑰, and a live storefront on RedBubble that sources, designs, and publishes its own products without manual intervention. I cover the agent configurations, the prompt engineering, the Playwright automation, the pipeline wiring, and the honest account of what needed iteration before the output was any good. I also get into two agents I am planning to add next: one for intellectual property and cultural responsibility screening, and one for automated multi-channel marketing across Instagram, Facebook, and beyond.