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.

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.

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.

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.

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.

I spent some time recently building something I have been curious about for a while: a retail store run almost entirely by AI agents. No manual design work, no product research, no uploading listings by hand. Just a three-agent pipeline that scouts trending content, generates original artwork, and publishes finished products to a live storefront.

The store is real & running. Read about how it works and what it revealed about where agentic AI is heading for retail. I Let Agents Run a Retail Store.

Supplychain software has a workflow problem, not a capability problem. Planners spend their days navigating dashboards, reconciling signals across systems, and manually triaging exceptions. The tools work. The work just never stops feeling like tool management.

Enmovil, the newest member of the Microsoft for Startups Pegasus portfolio, is taking a different approach. Their CADDIE platform sits as an orchestration layer across existing ERP, TMS, and WMS infrastructure and brings autonomous decisioning directly into Microsoft Teams. The goal, as founder Ravi Bulusu put it in our conversation this morning, is not to replace what enterprises have built. It is to make the intelligence invisible.

Steve Jobs said the best technology disappears. Enmovil is building toward that for supplychain logistics.

Enterprise AI is moving beyond copilots and dashboards toward systems that participate directly in how work gets done. In operationally intensive industries such as distribution, manufacturing, and field services, this move is particularly visible. These environments rely on complex workflows, fragmented systems, and deep domain expertise. Traditional automation has struggled to keep up with that complexity.

InstaLILY AI (a Microsoft for Startups Pegasus partner) represents a new approach to this problem. Rather than layering intelligence on top of existing tools, the platform introduces AI teammates that operate within enterprise systems and execute work end to end. These AI teammates are designed to function alongside human teams, extending their capacity while working within the constraints of real-world operational systems.

Retailmerchandising is one of the most mature areas in enterprise software, yet much of the work still relies on disconnected systems and manual coordination.

I explore how Intelo.ai approaches merchandising as a continuous, agent-driven system that connects planning, allocation, and in-season optimization.

If you are exploring how AI can reshape retail planning and execution, this is a space worth paying attention to.

Agentic AI is starting to show up in places we often overlook. Contract management is one of them.

In this piece, I explore how SimpliContract is turning contracts into a continuous layer of intelligence across the enterprise.

This came out of a great conversation with Guru Venkatesan, CEO and Founder of SimpliContract, on financial efficiency, execution, and the untapped value sitting inside agreements.