Afleveringen
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Retail and CPG run on a deceptively simple promise: ๐๐๐ ๐๐๐๐๐ ๐๐๐๐ ๐๐๐, ๐๐ ๐๐๐ ๐๐๐๐๐ ๐๐๐๐๐, ๐๐ ๐๐๐ ๐๐๐๐๐ ๐๐๐๐๐, at the moment a shopper is ready to buy.
Delivering on it has never been harder.
I've been spending time mapping the Microsoft for Startups Pegasus portfolio against the challenges retailers and brands actually face, from consumer insights and demand forecasting to shelf intelligence, supplychain resilience, and the agentic platforms reshaping customer experience.
The result is a market map of ๐๐ ๐๐๐๐๐๐๐๐๐๐-๐๐๐๐ ๐ ๐๐๐๐๐๐๐๐ ๐๐๐๐๐๐ ๐๐๐๐ ๐๐๐๐๐๐๐๐ ๐๐๐๐๐๐, plus a walkthrough of the companies I find most compelling for this industry.
Two things worth noting:
- Most of these startups reach well beyond Retail and CPG. A retailer's forecasting engine is a manufacturer's demand planner. A CPG brand's content automation is a bank's compliance accelerator.
- This portfolio isn't static. We're constantly listening to where retailers and brands see capability gaps, then recruiting through our tier-one VC network to fill them with proven startups.
If your team is working through a specific challenge, reach out and we can organize a private startup showcase built around your priorities.
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Satya Nadella recently argued that the real #AI advantage isn't which model you pick, but whether your organization builds a learning loop that gets better at your business every time it's used.
This piece applies that idea to #retail: how a learning loop paired with a world model can turn everyday signals (transactions, weather and local events, and in-store sensor data on traffic, dwell time, and shelves) into a compounding advantage competitors can't buy. It also looks at how Microsoft's stack and the #startups in the Microsoft for Startups ecosystem can help #retailers and #brands build it faster.
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Zijn er afleveringen die ontbreken?
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Spent this morning with the team at Brain Co., and I had to write it up.
They're tackling a problem every enterprise knows too well: AI pilots that look great in a demo and then never make it to production. What stood out to me is their forward deployed model. Instead of handing over software and wishing you luck, they embed engineers and product specialists right alongside your operators to get complex AI actually working in the real world.
Their Retail and CPG work is a great example, including a roofing distributor where they blended external signals like weather and competitor pricing into forecasting, pricing, and sales, and got real results in weeks rather than months.
Brain Co. is one of the hashtag#startups in our Microsoft for Startups Pegasus portfolio, and there are hundreds more we've curated based on what we're seeing across our customer ecosystem. Reach out if you'd like the list or want to organize a Startup Showcase with some of the founders.
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Had a great conversation with Shameel Abdulla, CEO of Clootrack, this morning, and came away energized about what his team is building.
Most brands have gotten very good at measuring customer experience but not at driving outcomes from it. Clootrack closes that gap. It's an AI Super Agent for Voice of the Customer that doesn't just tell retailers and CPG brands what happened, but why it happened and what to do next, connecting customer feedback with transactions, churn, returns, and operational data to actually move the needle.
I'm also excited about their new MCP server, which brings this customer intelligence directly into Microsoft Copilot and other AI work fronts, so teams can ask questions in plain language and get governed, evidence-backed answers.
Clootrack is a great example of the kind of Agentic AI company we love supporting through the Microsoft for Startups Pegasus Program.
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Most frontierAI research isn't built with retail in mind, but a handful of models in Microsoft Foundry Labs map onto real retail problems remarkably well. I went through the catalog and picked out the ones with a genuine use case, from product imagery and conversational commerce to search, optimization, and 3D shopping. I also dig into why in-house retail teams can treat these as building blocks rather than off-the-shelf products, and how pairing them with startups in the Microsoft for Startups portfolio can accelerate the work. If you're a retailer or brand thinking about where to build, I'd love to hear from you.
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I spent the last month building autonomous projects: a hashtag#retail operation that designs and sells t-shirts off trending phrases, a pipeline that turns photos into 3D-printed figurines, and a newsletter that translates retail news into insights and actions rather than just headlines.
I learned a lot along the way. That you no longer need coding skills to build real things, and that you can build them in a fraction of the time. That debugging can be handed to hashtag#AI, and watching it explain its reasoning is genuinely educational. That the hard part has quietly moved from writing code to saying clearly what you actually want.
But the biggest realization was this. All of this technology is democratizing the ability to create. The challenge no longer lives in the tools or the skill to code something. It lives in creativity, in coming up with ideas worth building that no one else has thought of. That is the new skill all of us need to develop, and it is what becomes our unique strength.
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What if your news feed answered "๐๐๐๐ ๐ ๐๐๐ ๐๐๐๐ ๐๐๐๐ ๐๐๐ ๐๐" instead of "what happened"? I wrote a job description for an #AI agent using #MicrosoftScout, that reads 30+ #retail 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.
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Earlier today I had a great conversation with Georges F. Mirza, creator of the ARSยฒ 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: ARSยฒ 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?
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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.
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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.
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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.
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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.
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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.
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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 ๐น๐๐๐๐๐๐ ๐ฎ๐๐๐๐๐ ๐ด๐๐๐๐๐๐๐๐๐ 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.
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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.
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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 ๐จ๐๐๐๐๐๐ ๐น๐๐๐๐๐๐๐๐๐๐, and I have written up my thinking on what it means specifically for Retail and CPG enterprise software.
๐๐ก๐ ๐ฌ๐ก๐จ๐ซ๐ญ ๐ฏ๐๐ซ๐ฌ๐ข๐จ๐ง: 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.
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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 Iโve 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.
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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.
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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 ๐ฎ๐๐๐ฏ๐๐ ๐ช๐๐๐๐๐๐ ๐ช๐ณ๐ฐ, 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.
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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.
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