The 10-Minute Retail Future: Amazon, AI Agents, and 2026 Predictions
Every week, we distill industry podcasts and conferences into what you need to know
In today's issue:
The micro-warehouse and partnership strategy powering Amazon’s 10-minute deliveries
The “accordion principle” to move from AI pilot to enterprise-wide transformation
Retail predictions for 2026: physical AI, autonomous commerce, and the death of channels
1. The micro-warehouse and partnership strategy powering Amazon’s 10-minute deliveries
Situation Today with Gulf Business: interview with Amazon’s Ronaldo Mouchawar on innovation, regional strategies and leadership (Dec 31, 2025)
Background: Amazon's fastest delivery happens in the Middle East: six minutes for an Apple Watch. That's not a stunt, it's the new standard built by Ronaldo Mouchawar, who co-founded Souq.com (acquired by Amazon for $580 million in 2017), powering Amazon's push to deliver "most products to most customers within 10 minutes."
TLDR:
Amazon's 10-minute delivery requires AI-powered computations down to the second, micro-warehouses positioned based on local demand topology, and assortment planning by region, city, and seasonality.
Instead of building from scratch, Amazon partners with established local players (Lulu hypermarkets, postal services, petrol stations) and repurposes their assets with digital transformation.
Proximity is everything, and partnerships get you there faster than building. When Mouchawar needed quick commerce infrastructure across the GCC (Gulf Cooperation Council) countries, he didn't build from scratch.
He partnered with Lulu (the established hypermarket chain) for fresh and grocery in UAE, petrol stations with community presence, and critically, postal services.
The UAE Post's network had physical locations throughout communities – assets that were underutilized as digital transformation reduced traditional mail. Amazon repurposed these postal facilities as micro-fulfillment centers.
💡Established local players have proximity you'd spend years building. Add digital transformation to their existing assets.
10-minute delivery requires AI precision at the seconds level. Delivering in minutes means computing the full-cycle promise down to the second: from payment to picking to assigning the delivery associate to doorstep arrival.
This requires AI models that understand local topology, traffic patterns, and delivery associate availability in real-time.
It also requires micro-warehouses stocked with the right assortment for specific neighborhoods, not just cities. What Dubai Marina customers order differs from what Al Barsha customers want, and seasonality affects everything. The AI systems must learn these patterns continuously.
Localization extends far beyond translation. Payments vary dramatically: credit cards in the Gulf, Sharia-compliant debit in some markets, cash-on-delivery dominating Egypt.
Addresses barely exist in some countries, where customers describe locations by landmarks, not street numbers.
Amazon built its own last-mile capabilities where postal systems were weak and adapted to landmark-based addressing using maps technology.
The global principles (selection, price, convenience, service) are universal, but execution requires deep local customization.
What to do about this:
→ Identify underutilized physical assets in your target communities: postal facilities, convenience stores, petrol stations, grocery chains. Evaluate partnership models that would provide proximity without capital expenditure on new real estate.
→ Calculate your delivery “promise.” If you're offering same-day or faster delivery, calculate how precisely your systems estimate fulfillment time. Can you promise within hours? Within minutes? Identify the data gaps preventing more precise promises.
→ Localize beyond language. Audit your customer experience for payment method preferences, address format assumptions, search dialect variations, and product assortment relevance by micro-geography. Build feedback loops that continuously surface localization gaps.
2. The “accordion principle” to move from AI pilot to enterprise-wide transformation
Salesforce Executive Conversations: Inside the Agentic Enterprise - Data, AI and the Future of Customer Experience (Dec 7, 2025)
Background: Kaylin Voss, EVP of Agentforce and Data 360 at Salesforce (and former CRO of Slack), has been inside the world's largest agentic transformations.
Finnair, which effectively operates Finland's largest e-commerce site, now resolves 70% of customer inquiries through AI agents.
A major Latin American retailer serves 36 million consumers across nine countries entirely through WhatsApp agents.
A home security company cut appointment calls from 16 minutes to two.
Here's the framework she's using to help companies move from AI pilots to enterprise-wide transformation.
TLDR:
Start with the "accordion principle": begin with a single small use case, prove value, then expand to interconnected enterprise agents. Finnair started with simple travel updates and scaled to 70% automated resolution.
Gift card agents aren't just notifications; they're revenue engines. A Canadian retailer turned simple balance reminders into personalized two-way conversations that generate 3-4x more revenue per interaction.
The Accordion Principle: Big vision, small start, measured expansion. Most retailers either go too big (massive AI transformation that stalls in pilot purgatory) or too small (disconnected chatbots that never scale).
💡Voss recommends the "accordion principle": start with a big agentic transformation vision, then compress it to a single use case. Learn. Measure value. Then expand back out.
Finnair demonstrates this perfectly. They didn't try to automate everything at once. They started with basic travel updates: gate changes, baggage tracking, seat upgrades. Each interaction maintained the brand's empathy and human touch while scaling personalization.
Now they're at 70% resolution rates through agents, and their internal contact center agents are happier because they handle the complex cases that actually require human judgment.
Turn notifications into two-way revenue conversations. Here's where it gets interesting for retailers: A large Canadian retailer wanted to launch a gift card agent. Simple concept: "Hey, you have a $5 balance with us."
Traditional marketing sends that as a one-way email blast. With agentic AI, it becomes a personalized dialogue: "You have $5. Based on your purchase history, here's what we'd recommend. Here are images. Here are shipping dates."
The agent takes you through the entire process end-to-end. The result? Those gift card agents return 3-4x more revenue than traditional notification campaigns.
Adopt an "Agent Boss" mentality and celebrate failures. The companies succeeding aren't just deploying technology; they're building new cultural muscles.
Voss calls it the "agent boss" mindset: treat your AI agents like new hires who need constant coaching, refinement, and guidance. They won't work perfectly out of the gate. Neither would a human you just hired.
The second piece: experimentation velocity. Track your experiments. Encourage failure. When a use case doesn't work, thank the team publicly on calls. Remove the fear of trying new things.
The companies that win are those that run experiments constantly and celebrate the losses as much as the wins. Because that's how you learn what actually works in your specific context.
What to do about this:
→ Apply the accordion principle. Map your big agentic vision, then identify the single smallest use case that can prove value in 30-60 days. Gift card reminders, appointment scheduling, order status… Pick one domain, measure before/after, then expand.
→ Schedule "agent coaching" sessions like you would for new employees. Block weekly time to review agent performance, refine responses, and test edge cases. The companies hitting 70% resolution rates don’t “deploy and forget”... they're actively training their digital workforce.
→ Create a formal experimentation log with failure celebrations. Track every AI experiment you run. When something fails, document why and share it in team meetings as a learning moment. Experimentation velocity is the real competitive advantage.
3. Retail predictions for 2026: physical AI, autonomous commerce, and the death of channels
Everest Group Webinar: From Fatigue to Focus: Retail and CPG Investment Priorities for 2026 (Dec 5, 2025)
Background: Everest Group makes three predictions for 2026: physical AI will redefine supply chain operations, autonomous commerce will take off as AI agents execute purchases, and the retail app as we know it is "almost dead", getting replaced by ecosystem orchestration. Here's what that means for your technology investments and sourcing strategy.
TLDR:
Store closures are projected to hit 12,000-15,000 in 2025 (nearly 3x the 2022-2024 average), but physical retail isn't dying. It's getting rebalanced towards experience density and digital conversion.
For every $1 spent on generative AI, enterprises need to spend $3-5 on data infrastructure. AI cannot work in silos, and data / analytics is the number one budget priority.
Generative AI is becoming table stakes. The differentiated story for 2026 is physical AI (robotics, sensors, drones) and autonomous commerce, where AI agents execute actual purchases.
The winners in 2025 won on operating model maturity, not market momentum. Everest Group's analysis shows that companies in the top 50 percentile for revenue growth invested in fundamentals:
accelerating D2C portfolios,
localizing supply chains, and
building recurring growth engines like loyalty.
Home Depot outperformed Lowe's despite being in the same category because it "built a structurally superior system investing in the right stakeholders and modernizing supply chain early on."
The bottom 50 percentile? Over-reliance on price increases while volume growth failed, squeezed by inflation, labor costs, and constrained consumer appetite.
The market won't reward size. It will reward companies that are "digitally built, lean, faster, and agile."
Physical AI is the next operating system. This means the fusion of AI, robotics, sensors, computer vision, drones, and edge automation transforming supply chains into "self-learning, self-correcting systems."
The pressure comes from consumer intolerance: 74% of shoppers will abandon a brand after three or even fewer bad experiences (stockouts, late deliveries, unapproved substitutions).
Industry forecasts suggest companies applying physical AI can cut inventory costs by 20% and reduce stockouts by 50%, which is a gamechanger in thin-margin categories.
Walmart doubled down on drone delivery while selling its robotics unit to Symbiotic (but securing a long-term commercial partnership), effectively outsourcing innovation while securing capability.
Traditional automation providers will be outshined by those with integrated offerings, and outcomes like stockout reduction will become key ROI indicators in sourcing deals.
AI agents will execute purchases and replenishments at scale. For single-person households or dual-income families, repeat purchases are perfect candidates for full delegation.
Walmart partnered with OpenAI to allow purchases directly through ChatGPT. During Thanksgiving week, $60 billion of the $350 billion in shopping was AI-enabled – consumers using AI for recommendations and product inquiries.
The sourcing implication: contracts will evolve from click-through rate SLAs to conversion and accuracy-based metrics. Return on ad spend will need to account for agentic AI enablement.
Omnichannel is dead – ecosystem orchestration is the new game. Firms will shift from owning transactions to orchestrating ecosystems.
The retail app as a standalone experience is "almost dead". Consumers demand lifestyle ecosystems where commerce, loyalty, payments, and services collapse into super-app experiences.
This has happened in APAC already. The real shift will come to the US and North America in 2026.
Kroger partnered with Uber Eats for food delivery alongside grocery.
Walmart partnered with ChatGPT for integrated payment and conversation.
The sourcing pivot: from channel vendors to ecosystem integrators, with cross-brand and cross-industry alliances becoming the norm. Winners will stop building channels and start building ecosystems.
What to do about this:
→ Benchmark your operating model maturity against top performers. Don't ask "are we bigger than competitors". Start asking "are we structurally faster and more agile?" Evaluate your D2C portfolio acceleration, supply chain localization, and recurring revenue engines against industry leaders.
→ Budget data infrastructure at 3-5x your generative AI spend. If you're planning $1M in GenAI investments, ensure $3-5M flows into data quality, integration, and analytics infrastructure. AI in silos will fail.
→ Pilot physical AI in one supply chain node. Choose a high-friction area (last-mile delivery, warehouse picking, inventory replenishment) and test autonomous systems. Measure stockout reduction and inventory cost as primary KPIs.
Disclaimer
This newsletter is for informational purposes only and summarizes public sources and podcast discussions at a high level. It is not legal, financial, tax, security, or implementation advice, and it does not endorse any product, vendor, or approach. Retail environments, laws, and technologies change quickly; details may be incomplete or out of date. Always validate requirements, security, data protection, labor, and accessibility implications for your organization, and consult qualified advisors before making decisions or changes. All trademarks and brands are the property of their respective owners.
