Deepak Lakshmanan

Why retail leaders can’t afford to misread this moment: the U.S. economy is growing, but the consumer is splitting into two different species By Carsten KrauseJanuary 13, 2026 At NRF, the economist panel didn’t sugarcoat it. Mark Mathews set the tone with the kind of line that sounds casual… until you realize it’s a warning: “What a year.” That wasn’t nostalgia. That was triage. Because 2025 delivered a rare combination: policy shocks that actually moved the needle, an AI capex surge that’s starting to resemble a new industrial cycle, and a consumer who keeps spending while loudly insisting the economy is terrible. #nrf #NRF2026 https://lnkd.in/exFYiD-s

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Why retail leaders can’t afford to misread this moment: the U.S. economy is growing, but the consumer is splitting into two different species By Carsten KrauseJanuary 13, 2026 At NRF, the economist panel didn’t sugarcoat it. Mark Mathews set the tone with the kind of line that sounds casual… until you realize it’s a warning: “What a year.” That wasn’t nostalgia. That was triage. Because 2025 delivered a rare combination: policy shocks that actually moved the needle, an AI capex surge that’s starting to resemble a new industrial cycle, and a consumer who keeps spending while loudly insisting the economy is terrible. #nrf #NRF2026 https://lnkd.in/exFYiD-s

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The next competitive advantage isn't a product. It's the agentic enterprise. The organizations that get there first won't just operate differently — they'll feel different to their customers. Imagine a brand that anticipates, acts, and adapts in real time across every touchpoint. Not because humans are working faster, but because intelligence is woven into the architecture itself. The technology exists today. What's missing isn't the AI — it's the foundation ready to support it. Here's what it actually takes to build it: 1. Architect backward from the customer outcome Most organizations start with an AI use case. That's a mistake. Start with the connected experience you want the customer to feel, then build the systems, workflows, and data flows to make it possible. Architecture should follow ambition, not the other way around. 2. Your data is your ceiling Agentic AI doesn't run on static reports. It runs on a unified, real-time data layer, one that connects every signal, every system, every moment of truth. In a human workflow, data latency is often invisible. In an agentic one, it's a critical failure point. Siloed data doesn't slow agents down; it makes them dangerous. 3. Design for orchestration, not just integration Traditional integrations were built for human-speed processes. Agentic workflows require something fundamentally different: an event-driven, governed layer where agents can act across systems without creating cascading dependencies. Without it, agents don't fail gracefully; they fail silently. 4. Governance must be architectural, not procedural This is where most enterprises are most exposed. You cannot manually audit decisions made at machine speed. Trust must be baked into the design, with traceable audit trails, explainability, and guardrails that are compliance by design, not a policy document sitting on a shelf. If you can't explain what your agents did and why, you don't have governance. You have a liability. 5. Build a leadership coalition, not just a champion Technology alone doesn't transform a company; culture does. You don't need a single AI evangelist. You need a coalition of executive sponsors across business, technology, and risk who are equally accountable for outcomes. Transformation is a movement, not a program, and movements need more than one voice. The agentic enterprise isn't a destination. It's a posture. The question isn't whether your industry will be reshaped by agentic AI. The question is whether your people will be the ones doing the reshaping, with intelligence working alongside them, not instead of them. #AgenticAI #EnterpriseArchitecture #DigitalTransformation #AILeadership #CIO #FutureOfWork #TrustByDesign Just thinking!! Views are my own!!

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I’ve seen most retail innovation conversations start in the wrong place. We start with the solution - AI, automation, and analytics. But sustainable value creation in retail usually starts somewhere more practical. Rent is largely fixed. COGS is negotiated over time. Shrink is complex and multifaceted. Labor deployment — how hours are allocated inside the four walls — is different. It is a daily operational decision and one of the most controllable expenses on the P&L. As retail technology continues to evolve, this is the lens I find most useful: Where does it directly improve how labor is deployed?

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Target bringing shopping journeys into ChatGPT is a sign of strategic shift not just novelty feature: - OpenAI makes push as commerce platform to monetise its models and own more distribution - Target is showing retail moving to where customer attention is - Conversational AI becomes revenue driver and designers will increasingly factor in conversion metrics https://lnkd.in/esXXU6ds

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Generic AI shopping agents will struggle with grocery retail as individual products might seem unremarkable, collectively they reveal highly personal preferences that prove difficult to automate. This challenge reveals a significant opportunity for retailers themselves.

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🛒 How the SPAR Framework Powers AI in POS & Retail |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| In retail and POS ecosystems, AI isn’t about replacing point of cashiers—it’s about making smarter, real-time decisions across millions of transactions. The SPAR loop (Sense → Plan → Act → Reflect) provides a structured way to build AI agents that are reliable, adaptive, and aligned to business goals. Here’s how SPAR can guide scalable architecture in POS/retail:   1. Sense Gather data and signals from diverse sources: ◦ Point-of-sale terminals (transaction logs, item scans) ◦ Inventory / supply chain systems (stock levels, delivery timings) ◦ Customer profiles & loyalty data ◦ Real-time context (weather, store footfall, local promos) This sensory layer ensures retailer AI “knows the state” before making decisions.   2. Plan/Process Generate decision paths based on sensed data and business goals: ◦ Dynamic pricing or discount decisions ◦ Inventory replenishment thresholds ◦ Personalised cross-sell / upsell recommendations ◦ Fraud risk scoring and flagging The planning engine evaluates trade-offs (profit vs. margin vs. stock risk) and lays down execution paths.   3. Act Execute the chosen plan via system integrations: ◦ Push price updates to POS terminals or digital systems ◦ Trigger stock replenishment orders ◦ Deliver personalized offers at checkout ◦ Block suspicious payments or require extra verification “Act” is where AI becomes tangible in customer experience and operations.   4. Reflect/Refine Evaluate outcomes & improve continuously: ◦ Did the discount uplift sales as expected? ◦ Were items overstocked or understocked? ◦ Did any fraud go undetected? ◦ Use this feedback to re-train, refine thresholds, or adjust decision weights Reflection closes the loop so agents improve over time rather than staying static.   🎯 Why SPAR Matters in Retail / POS AI ‣ Reliability & Governance: Avoid brittle, one-off AI models by building continuous feedback loops and enhancements. ‣ Scalability: SPAR enables modular agent components per store, region, range and/or product line. ‣ Business Alignment: Each cycle ties back to metrics (margin, conversion, stock turns) — not just technical accuracy. ‣ Future-Proofing: As new signals or systems emerge (e.g. IoT sensors, external demand data), they plug into “Sense” or “Reflect” without rearchitecting the whole system.   If you’re architecting AI for POS, retail, or omni-channel systems, consider SPAR as your foundational mental model. It elevates AI from proof-of-concept to resilient, meaningful business instrument. What retail AI scenario would you apply SPAR to next? #EnterpriseArchitecture #RetailAI #POS #ComposableArchitecture #SPARFramework #DigitalTransformation #MachineLearning #EdgeAI

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The Body Shop has chosen RELEX Solutions to advance its global supply chain transformation and accelerate its goal of becoming an AI- and data-first beauty company.  With improved forecasting and replenishment across stores, franchises, and distribution centers, The Body Shop will reduce waste, a value central to its ethos, while increasing availability and collaboration across its network.   See how this progress supports more sustainable and purposeful growth: https://relex.link/4o24CnM 

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🚨 Digital Superintelligence will be here in 10 years (or less). Eric Schmidt (former Google CEO) predicts: 👉 You’ll have the sum of Einstein + Da Vinci in your pocket. 👉 AI will soon generate its own “scaffolding” - building knowledge on top of knowledge. 👉 The real bottleneck isn’t chips. It’s electricity. That’s not science fiction. That’s the reality leaders need to prepare for today. As a retailer, the implications are massive: 🛒 Customer Experience → Shoppers won’t tolerate friction. They’ll expect retailers (and their agents) to anticipate their needs before they even ask. 📦 Supply Chain → Inventory, routing, and fulfillment will shift from reactive to self-orchestrating. 👩‍💻 Workforce → Store associates and support agents won’t be replaced — they’ll be augmented. ⚡ Infrastructure → With AI workloads consuming gigawatts, tech leaders must rethink sustainability, green compute, and energy partnerships. I see this as more than a technology upgrade. It’s a technology reset. The winners won’t just “adopt AI.” They’ll reimagine every customer journey, every supply chain decision, every frontline interaction through the lens of superintelligence. Because the question isn’t if digital superintelligence will transform retail. It’s: Will your company still be stuck in Level 1 service while competitors leap to Level 3 and beyond? #AI #Data #RetailInnovation #Superintelligence #CustomerExperience #Leadership #Google #RetailTechnology #Technology #AshleyFurniture #AshleyHomestores #Ashley | FDE LLC, d/b/a Ashley Northeast

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Retail Can’t Run 2026 on a 2006 Playbook A lot of apparel, footwear, and CPG companies are still running the same processes, systems, and tech stacks they used 15+ years ago. On-prem servers, manual workflows, siloed ERPs, Excel forecasting… meanwhile, newer brands are scaling like orchestras — cloud-first, data-driven, automated, and customer-obsessed. Here’s the hard truth: If legacy retailers don’t modernize now, they won’t just fall behind — they’ll get left out of the conversation entirely. ⸻ What’s Breaking Today • On-prem and custom ERP code = slow, expensive, risky. • Manual allocations + Excel forecasting can’t keep up with real-time demand. • Disconnected systems create inconsistent inventory and poor customer experience. • Outdated infrastructure increases security + compliance exposure. ⸻ What Modern Retailers Are Doing • Cloud-first ecosystems for ERP, WMS, PLM, data. • Real-time data + governance as the backbone of operations. • AI-driven forecasting, allocation, pricing, and automation. • Customer-centric operating models instead of channel-centric ones. ⸻ The Next 3–5 Years • AI becomes the decision engine across supply chain + merchandising. • Zero-trust security becomes mandatory, not optional. • Modular cloud stacks replace monolithic ERPs. • Robotics + automation transform DC operations. • ESG transparency becomes a compliance requirement, not marketing copy. ⸻ The ROI Is Clear Modernization pays back fast: • Better inventory accuracy • Lower operating cost • Faster fulfillment • Higher margins • Real-time visibility from factory → DC → customer Staying legacy is what’s actually expensive. ⸻ Bottom Line 2026 is around the corner. The retailers who modernize will scale. The ones who don’t… won’t. It’s time to upgrade the orchestra — or risk playing last year’s music in a market that’s already moved on.

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👻 "I don't see dead people... I see AI agents. Everywhere." I was enjoying a relaxing afternoon of reading when I realised that almost 90% of what I was consuming was about AI. That’s when it hit me — "I see Agents.” 😄 Jokes aside, it’s clear that AI isn’t just a buzzword anymore. It’s quietly becoming the next phase of the digital transformation we’ve talked about for years. Just like mobile and cloud once reshaped the way we work and interact, intelligent agents are now starting to transform how people buy, decide, and connect. I came across a good reading that explores how buying behavior is shifting — and why companies will need to adapt quickly to stay ahead. This another step in the Transformation. Are we ready for it? https://lnkd.in/dEC6-ckj And also if you don't know about the agents fully click here https://lnkd.in/dpaEHJqK #AI #DigitalTransformation #AgentsEverywhere #NextTransformation #FutureOfWork

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Still stuck in the era of sharing data through spreadsheets and sftp? There's better a way. Check out this blog by Roberto Robles and myself to find out more about how my favorite unsung hero, #DeltaSharing, allows organizations to share live datasets across different platforms and tech stacks without ever moving or copying the physical data, shifting collaboration timelines from days to seconds. #CPG #Retail #DataSharing #Databricks

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Retail is one of the most operationally intense environments, thousands of products, hundreds of decisions per shift, constant customer expectations, an agentic ecosystem can empower retail teams. The associate stays in charge, but doesn’t drown in operational noise, teams focus on thinking, strategy, and creativity, while agents do the routine analytics and preparation. The system works for them, not instead of them. retail jobs are already changing same as every generation before us. Cashiers became customer advisors, Inventory workers became omni channel coordinators, store managers became data driven leaders. Agentic Systems continue that journey, this transition is not about removing humans, It’s about removing the parts of the job humans never enjoyed like > Hunting for gaps and doing repetitive routine store walks > Reconciling spreadsheets > Checking compliance manually > Jumping between portals By offloading these to agentic systems and keeping humans in control , we build a retail environment where: > Associates get more time with customers > Store managers spend more time leading people > Buyers spend more time negotiating and selecting great products > Corporate leaders spend more time shaping strategy People do the human work. Agents do the machine work. This is the evolution of work, the same evolution every generation experiences. And it’s one where humans move up the value curve, not off it. #EnterpriseAI #RetailOS #FutureOfRetail #AgenticEcosystems #HumanInTheLoop #HumanCentricAI #AIInRetail

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As customers are becoming more conscious of data privacy and selective, retailers are increasingly looking at optimizing the touchpoints, orchestrating channels, fine tuning tailored campaigns, and moving away from "spray and pray" to "start with dart" approach to augment both physical and digital customer footprints. Glad to be featured in Adtech Today Middle East to discuss how generative AI with the right mix of agentic workflows can maximize conversion, and what it takes for retailers to get there

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According to the company, Albertsons Companies India marks a strategic investment in developing scalable technology solutions aimed at modernizing core systems, integrating data science and artificial intelligence (AI), and accelerating enterprise-wide retail transformation. #DigitalTransformation #GroceryRetail #RetailTech

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