AI for Business This Week: Starbucks AI Inventory Rollout, Google Cloud AI Growth, Microsoft Viva Engage Copilot, University Hospitals & Hippocratic AI, MIT Chemical Reaction Predictions
Hi there,
Quick pulse check: are you testing one new AI workflow each week? If not, this roundup makes it easy. Below are five practical wins from retail ops to internal comms and healthcare that you can borrow for your business playbook.
This week’s insights
Starbucks rolls out AI inventory counting across 11,000 stores
Why it matters: Starbucks is implementing AI-driven inventory counting to all North American company-owned locations by the end of September. Using NomadGo’s computer vision + spatial tech, stores are counting inventory up to 8x more frequently, helping keep popular items in stock and freeing staff from back-room tasks.
Business takeaway: Expect fewer stockouts, faster replenishment, and happier customers—this is the kind of behind-the-scenes AI that immediately improves service quality and labor efficiency.
My take: If you manage multi-site retail or restaurants, this is your blueprint. Pilot shelf-scanning + low-stock alerts in a few high-volume stores first, then roll out once you prove velocity and waste reduction.
Link: Reuters coverage
University Hospitals to deploy conversational AI voice agents with Hippocratic AI
Why it matters: University Hospitals (Cleveland) announced a partnership to roll out voice-based agents to expand patient engagement and close care gaps, starting with pharmacy support. It’s focused on real-world workflow wins.
Business takeaway: Voice agents that handle routine outreach (refill reminders, care navigation, FAQs) can reduce call volumes and speed up time-to-care—without adding headcount.
My take: As they are doing, you can also start narrow with low-risk use cases (refills, pre-visit checklists). Measure call deflection, response time, and patient satisfaction before expanding to clinical scheduling or triage.
Link: Becker’s Hospital Review
Google Cloud’s AI push is accelerating revenue and enterprise adoption
Why it matters: At Goldman Sachs Communacopia, Google Cloud CEO Thomas Kurian credited AI workloads (including tailored Gemini models and custom TPUs) with driving 32% YoY revenue to $13.62B and 141% jump in operating profit; backlog hit $106B. Translation: enterprises are moving serious workloads to AI-ready cloud stacks. Are you?
Business takeaway: If you’re choosing a platform, model fit + infra pricing matter as much as model quality. Evaluate total cost across compute, storage, and accelerators—not just model benchmarks.
My take: The wave is shifting from pilots to production. Make 2025 the year you standardize an “AI reference architecture” (data, security, monitoring, cost controls) so teams can ship faster with guardrails.
Link: Investor’s Business Daily
Ralph Lauren launches “Ask Ralph,” a conversational AI stylist inside its U.S. app
Summary: Ralph Lauren rolled out Ask Ralph, a Microsoft Azure OpenAI–powered shopping experience that recommends complete looks from its live catalog and brand archives—right in the RL app. It’s designed to mimic an in-store stylist and surface shoppable outfits via natural-language prompts. I love this idea and can’t wiat to be able to do try-on like with glasses.
Business takeaway: This is a clear template for conversational commerce: higher conversion/AOV, faster product discovery, and tighter brand control than marketplace chatbots.
My take: If you run ecomm, A/B test an “Ask [Your Brand]” entry point on product pages. Track assisted revenue, time-to-product, and return rates to build your ROI case.
Eli Lilly unveils “TuneLab,” sharing AI drug-discovery models with smaller biotechs
Summary: Lilly launched TuneLab, offering partners access to AI models trained on years of internal R&D data (>$1B worth), with Circle Pharma and insitro among early collaborators. The goal: speed discovery and lower costs while partners contribute back new training data.
Business takeaway: This is a platform strategy monetize proprietary know-how, expand R&D capacity via partners, and shorten cycle time without hiring an army.
My take: If you’re sitting on deep domain datasets, consider a gated partner platform. Start with 1–2 use cases, tight data-sharing terms, and shared KPIs (e.g., hit rate, cycle time). If you need data sets, just ask. This has worked for us, we’ve been able to collaborate with companies who have sets that we can use in a different way than they do. It’s a win-win.
Source
MIT CSAIL’s “SustainaPrint” makes eco-friendly 3D prints stronger with targeted reinforcement
Summary: MIT researchers introduced SustainaPrint, which uses simulation + AI to strengthen only the weakest zones of an object. With ~20% reinforced material, prints recovered up to ~70% of the strength of full high-performance plastic cutting material use and waste.
Business takeaway: For prototyping or distributed manufacturing, this points to lower material costs and greener ops without sacrificing performance.
My take: If you operate in hardware, this is a great way to test small in-house and then track material saved per part and defect rates before moving to customer-facing components.
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Tools worth a test drive (this week’s 3 picks)
- Talkadot– use to capture real-time feedback, collect testimonials, and turn engaged audience members into future clients.
- Fyxer – organizes your inbox, drafts extraordinary emails and writes better-than-human meeting notes.
- Firefly – FREE Adobe online tool creates AI-generated textured text, images, and more.
What’s the one process you’ll make 10% easier with AI this week inventory checks, patient outreach, or internal comms? Pick a single workflow, run a 14-day pilot, and measure time saved. If you want help scoping that pilot (or picking the right tool), I’ve got your back.
