AI That Persists Becomes Infrastructure

Hi, I’m Lily. I live in the world of distribution, where infrastructure decisions define decades, not quarters. When you commit to an ERP, a WMS, or warehouse automation, you’re hardwiring how work gets done for 10-15 years. I understand the weight of that decision, and why the instinct is to wait for certainty. But AI has crossed into infrastructure territory, and your comfort zone won’t protect you from the danger of inaction.

Companies treating AI as infrastructure are pulling ahead of innovation theater. Home Depot deploys AI that replaces weeks of manual work. PUMA pushes voice-activated warehouse intelligence across the continents. GE Appliances commits $3 billion to automation. Tech giants pour $380 billion into AI infrastructure, tightening procurement budgets across every sector. Universities now require AI literacy as a baseline graduation skill for future distributors. And in this week’s podcast, InstaLILY AI Founder & CEO Amit Shah breaks down why operator-first AI deployment wins over product-market fit.

Here’s what I’ve observed from this week’s sources: companies that commit to AI as core infrastructure and give it room to evolve see results. The technology learns their domain, adapts to their exceptions, and surfaces opportunities they didn’t know existed.

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If you only read one thing this week, it is this:

Home Depot’s AI Blueprint Takeoffs transform contractor material estimating from a multi-week manual process into instant automated takeoffs, enabling pros to purchase all project materials through a single supplier. Despite housing market pressure, the retailer maintains capital expenditures at 2.5% of sales to accelerate pro segment technology investments.

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What’s Working in the Field

PUMA Adds Agentic AI in Logistics

PUMA pushed agentic AI straight into warehouse operations with a voice-activated assistant across sites in Austria, South Africa, and India. Supervisors can now ask the system for inventory status, root-cause a tech issue, or spin up a dashboard without waiting on an analyst or IT. For distributors, this eliminates one of the most expensive hidden costs in warehouse operations: decision lag caused by waiting on IT tickets, analyst reports, or manual data pulls, turning every supervisor into a self-sufficient data analyst who can act on real-time intelligence without escalation.

Home Depot Automates Constructor Takeoffs

Home Depot’s new AI Blueprint Takeoffs nuke a multi-week estimating process for contractors and turns it into near-instant material lists. Combined with its computer-vision-powered program and Sidekick system, HD drove a 400-basis-point jump in customer satisfaction and 11% digital sales growth despite a soft housing market. When AI does the takeoff, pros stop shopping BOMs across three suppliers. HD is quietly converting workflow ownership into share-of-wallet, something every distributor watching the professional contractor needs to take seriously.

Supply Chain Leaders Need AI Literacy Now

At the 65th Plains Mountain Business Conference, industry and academic leaders emphasized that AI literacy is now a baseline leadership skill in supply chains instead of a niche technical capability. Companies are scrambling to manage “shadow AI” while building T-shaped talent that can blend operational depth with AI fluency. Unilever already trained 23,000 employees; Ohio State is rolling out genAI course for supply chain majors; Deloitte runs AI training that helps implement AI across procurement and supplier performance.

AI Infrastructure Boom Shifts Procurement Priorities

Tech giants are investing $380 billion in AI infrastructure by end of 2025, reshaping the competitive landscape for distributors. This private-sector-driven capital surge is tightening customer procurement budgets as organizations redirect spending toward AI platforms instead of traditional operating expenses. Lock in long-term contracts now, and diversify your supplier base. If you wait until customers finalize their 2026 budgets, you’ll be buying into what’s shaping up to be the tightest procurement environment of the decade.

GE Appliances Automates for Zero-Distance Supply

GE Appliances is investing $3 billion over five years in U.S. manufacturing automation to support its “zero distance” supply chain strategy, which means producing near consumers for faster response. The company deployed automation across three areas: production efficiency using robots, cutting production time by 50%; a “digital thread” providing real-time equipment visibility that reduced downtime from 15-20% to under 5%; and autonomous vehicles moving materials within plants, with piloted automated delivery trucks between facilities. When manufacturers cut production time in half, the entire supply chain upstream must match that velocity and reliability or risk becoming the bottleneck.

Agile Robots Launches Its First Industrial Humanoid Robot

Agile Robots launched Agile ONE, its first humanoid robot designed for industrial applications including material handling, pick-and-place operations, and machine tending. Early demos highlight dexterous manipulation, enabled by fingertip and force-torque sensors, along with an AI stack trained on a large real-world industrial dataset. Production is slated for early 2026 at a new facility in Bavaria. Distribution centers with labor-intensive tasks are likely to be the first real test beds, but most operators will be watching.

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What’s In My Ears

He left a $2B ARR company to build AI agents - then hit $1M ARR in < 6 months | Amit Shah, Founder of InstaLILY AI

In this episode of the Product Market Fit Show, Amit Shah, CEO and Co-Founder of InstaLILY AI, breaks down why for almost half of the global GDP, 90% of the workflow is still manual, which degrades human potential. The discussion centers on three critical insights for distributors:

1. Confirming that one size fits none, by deploying purpose-built AI that understands domain nuances like the difference between shingle sizes and floor materials.

2. Building systems of learning that improve over time unlocks entirely new capabilities that were previously impossible due to cost or scale constraints.

3. Maintaining metrics-driven rigor from day zero rather than empty promises to your clients. Progressively leaned in operators who understand operational complexity will lead this transformation, proving that operator market fit vs product market fit determines success in reshaping distribution workflows.

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Lily’s Quick Take

AI is crossing the threshold from “nice to have to speed up workflows” to “AI agents help me create entirely new workflows.” The difference matters. Speed is incremental: you do the same work faster. But new workflows are transformational: you do work that was previously impossible or uneconomical to attempt.

AI won’t disappoint operators that don’t abandon it at the first rough edge. Lean in progressively, measure relentlessly, and trust the compounding. The infrastructure that learns is the infrastructure that lasts.