In this issue I dig into the trend I find most significant right now, an agentic layer that overlays existing mature systems instead of replacing them, with great examples from Auger, Intelo.ai, Buynomics, and SimpliContract. I also explain MCP servers in plain terms (think USB ports for AI) and highlight Microsoft for Startups Pegasus portfolio #startups like Nimble, Omnistream, YDISTRI, Tembi - Market Intelligence, and Toolio that are shipping them today.

Plus a look at my own experiments running autonomous retail stores with #AI agents.

Merchandise planning is one of those domains where deep vertical focus wins. Toolio built their platform around the actual vocabulary of #retail planners, things like open-to-buy, size curves, and weeks of supply, with apparel brands like Bombas, Knix, and AKA Brands on the customer roster.

The results speak for themselves: AKA Brands cut SKU count by 50 to 75 percent while holding sales steady.

What I find most interesting is their MCP Server. Across the hashtag#startups I work with, I keep seeing the same pattern: products are becoming capabilities that a customer's own AI agents can call on. Toolio exposes its entire planning brain through #MCP, so a #retailer's agents in Copilot Studio, Claude, or ChatGPT can query plans, run exception searches, and act on inventory data with the same permissions and governance the planning team already has.

What happens when you let AI agents run an entire retail store?

I just published my second experiment in autonomous retail. The first was a store that scanned trending news and auto-designed viral t-shirts. This one is Mini Me: upload a photo, and a pipeline of agents turns it into a 3D-printed figurine of you, a loved one, or your pet.

Both were really experiments to teach myself how to build and coordinate agents by running something real instead of a toy demo. The biggest lesson? The breakthrough came when I stopped making my agents rebuild everything from scratch and started treating them as operators that drive existing tools, like having an agent run Meshy AI for the 3D models and Printfield for printing and shipping.

If you're building a B2B tech startup in retail or CPG, getting in front of the right enterprise teams is often harder than building the product itself. This piece looks at the corporate innovation programs and CVCs that are genuinely set up to work with external tech providers: from PepsiCo Labs and LVMH to General Mills 301 INC and Tesco Labs, and what it actually takes to convert a pilot into a lasting partnership.

Most supplychain compliance failures are not caused by careless companies. They are caused by a visibility problem that most brands do not realize they have until a shipment gets detained at the border. This article is about how that plays out in practice and what a new generation of trade intelligence tools is doing about it. Trademo is one of the startups we are working with through Microsoft for Startups Pegasus portfolio that is building genuinely interesting solutions for Retail and CPG companies in this space.

Excited to welcome Buynomics to the Microsoft for Startups Pegasus portfolio. After a fantastic conversation with Ingo Reinhardt and Tim Schneider, it's clear they've built something genuinely differentiated in the 𝑹𝒆𝒗𝒆𝒏𝒖𝒆 𝑮𝒓𝒐𝒘𝒕𝒉 𝑴𝒂𝒏𝒂𝒈𝒆𝒎𝒆𝒏𝒕 space. Their agent-based simulation approach gives commercial teams a digital twin of their customer ecosystem to stress-test pricing, promotions, and portfolio decisions before they reach the market.

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.