When the Jeans You Sell Become a Liability: How Modern Retailers Are Getting Ahead of Supply Chain Risk

I Stopped Asking AI for the News. I Asked It What the News Means.

What if your news feed answered "𝒘𝒉𝒂𝒕 𝒅𝒐𝒆𝒔 𝒕𝒉𝒊𝒔 𝒎𝒆𝒂𝒏 𝒇𝒐𝒓 𝒎𝒆" instead of "what happened"? I wrote a job description for an #AI [https://www.linkedin.com/search/results/all/?keywords=%23ai&origin=HASH_TAG_FROM_FEED] agent using #MicrosoftScout [https://www.linkedin.com/search/results/all/?keywords=%23microsoftscout&origin=HASH_TAG_FROM_FEED], that reads 30+ #retail [https://www.linkedin.com/search/results/all/?keywords=%23retail&origin=HASH_TAG_FROM_FEED] news sources every week and delivers trends and takeaways rather than headlines. New post on how I built it, the exact instructions that power it, and a link to the first live issue.

Two Reliable Paths to Startup Innovation: Curated Portfolios and Independent Frameworks

Earlier today I had a great conversation with Georges F. Mirza, creator of the ARS² Framework and founder of ComTask, and it prompted me to write down something I keep seeing with the #retailers and #brands I work with. Around 70,000 B2B Tech #startups launch every year and most fail within two years. Yet the capabilities retailers need most right now, especially in agentic AI, live inside those young companies. The result is a familiar cycle: a promising demo, a quick assessment, a pilot that stalls, and the search starts over. In this article I lay out two parallel paths out of that cycle. The first is the curated route through Microsoft for Startups and the Pegasus program, where solution categories are defined through direct customer engagement, sourced from tier 1 #VC portfolios, and vetted for enterprise readiness. The second is for the startups you find yourself, at trade shows or through your own scouting, where two frameworks let you do the vetting independently: ARS² to confirm the solution delivers accurately, repeatedly, at scale, and with speed, and the Azure Well-Architected Framework to confirm the engineering underneath can carry it. Thanks to Georges for the conversation that sparked this. The full article is linked below, and I'd welcome perspectives from others who evaluate startups for enterprise deployment. What does your process look like?

The Agentic Layer and the New Plumbing of Retail

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.

Startup Spotlight: Toolio and the Reinvention of Retail Merchandise Planning

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.

From Photo to Figurine: An Autonomous Retailer Run Entirely by Agents

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.