Winning the Holiday Season with AI: Majaz on Smarter, Real-time Inventory Management

Every holiday season, the retail industry faces the same high-stakes paradox: demand surges to its peak, yet billions are lost to empty shelves, slow restocking, and disconnected planning that systems can’t keep up with. Traditional supply chain models built on reactive forecasting and siloed visibility are now hitting their breaking point.

But a quiet revolution is underway.

Agentic AI is reshaping inventory management from a slow, reactive process into a real-time, autonomous system that can sense, respond, and act at the speed of consumer demand. At the forefront of this shift is Majaz Mohammed, Vice President at Tredence, who is helping global retail leaders move from “monitoring inventory” to truly self-orchestrating supply chains.

In this interview, Majaz breaks down how AI agents can dynamically rebalance stock across regions, trigger supplier orders without human intervention, and eliminate the blind spots that cost retailers millions during the most profitable weeks of the year. He also shares what most leaders still get wrong about AI-enabled inventory management – and what needs to change before the next holiday surge hits.

For CTOs, business leaders, and retail executives, this conversation offers a front-row view into the future of supply chain execution. By bridging technology, operations, and revenue, Majaz outlines a clear roadmap for how companies can optimize inventory management during the busiest and most demanding season of the year.

AI & the Holiday Supply Chain

According to McKinsey, nearly 30% of annual U.S. retail sales occur during the Thanksgiving and Christmas holiday season. Yet each year, billions are lost to empty shelves, delayed replenishments, and mismatched promotions. What is your take on this? Can AI address this issue?

Majaz: The $1 trillion holiday season is a massive opportunity, but poor execution leaves billions on the  table. A lot of retailers still rely on last year’s patterns while consumer behavior shifts dramatically.

McKinsey’s data shows two-thirds of shoppers start before Black Friday, yet many supply chains aren’t ready until November.

AI fundamentally changes this equation. Agentic systems now monitor real-time signals-social chatter, weather; competitor moves and acts within 90 minutes. The key is granular personalization at scale. Instead of broad forecasts, AI models micro-segment and adjust daily. They spot trends as they emerge, not after stockouts happen. Traditional planning cycles take weeks, whereas AI reacts in hours.

In addition to planning better, AI models help course correct in real time by detecting sales trends and repositioning inventory to priority areas and avoiding lost sales and excess inventory issues.

That makes empty shelves during holidays preventable and manageable. It’s about implementation speed and data infrastructure. Companies moving now will dominate this season.

In retail, speed is everything – but so is accuracy. How do you strike the right balance between automation and human oversight during peak demand cycles like Thanksgiving and Christmas?

Majaz: You’re right. One doesn’t need to choose between speed and accuracy. You have to design systems where AI handles the volume while humans drive exceptions and strategy.

During peak periods, we can’t have analysts manually review thousands of SKUs daily. AI excels here-monitoring inventory levels, triggering replenishments, adjusting allocations across stores in real-time. A recent report shows companies using agentic AI were able to reduce operational costs  by 20-35% while improving availability.

But humans remain critical for the context AI misses. When a viral TikTok suddenly spikes demand for an unexpected product, or supplier issues emerge, experienced planners make judgment calls AI can’t. We build “human-in-the-loop” systems where AI flags anomalies-unusual demand patterns, margin risks, supplier delays, and escalates to our teams. Think of it as AI providing the radar, humans steering the ship.

In the holiday supply chain, speed wins the moment – but intelligence wins the season.

Technology and Transformation

Agentic AI promises real-time decision-making. What’s the key to ensuring those decisions are trusted by human operators – especially when millions of dollars in margin are at stake?

Majaz: Trust in agentic AI rests on three pillars: transparency, guardrails, and track record.

When AI recommends shifting millions in inventory or launching promotions, teams must understand what data drove that decision. Was it demand signals, competitor moves or inventory positions. Therefore, explainability matters.

Second, establish boundaries. Set margin thresholds and inventory constraints upfront. AI operates within these guardrails, escalating decisions beyond parameters to humans. This prevents catastrophic mistakes while maintaining speed.

Third, prove it works. Start with lower-risk categories and measure relentlessly. Track forecast accuracy, promotion effectiveness, and margin impact. When AI consistently outperforms manual decisions over weeks, trust builds naturally.

What role do you see emerging technologies like digital twins and generative AI playing in next-generation supply chain orchestration?

Majaz: Digital twins and generative AI are transforming supply chains from reactive systems into predictive, self-optimizing networks.

Digital twins create virtual replicas of your entire supply chain-warehouses, inventory, and transportation networks. You can simulate scenarios before they happen. What if a supplier delays shipments during peak season? How does a promotional spike impact the distribution center?

Test strategies without risking real operations. This matters especially given a recent report saying that two-thirds of consumers now start holiday shopping before Black Friday. As a result, traditional planning cycles can’t keep up without simulation capabilities.

For example, a $100B+ CPG company utilized digital twins to simulate downstream effects, warehouse capacity issues, etc. caused by supplier delays and was able to avert a $90M sales loss within 6 months.

Generative AI accelerates decision-making dramatically. It analyzes massive datasets which could include demand patterns, weather, social sentiment and generates optimized replenishment plans, promotional strategies, or allocation decisions in minutes, not days.

The real power comes from combining them. Digital twins run thousands of scenarios; generative AI identifies optimal paths and automates execution. Based on search results on agentic AI applications, systems can now make autonomous decisions within 90 minutes of detecting market signals.

Leadership and Strategy

How are modern leaders using technology to go beyond efficiency and embed true resilience into their supply chain strategies?

Majaz: Major U.S. retailers such as Target and Walmart are increasingly using AI-driven systems to predict and avoid stock-outs and mismatches across their networks. For example, Target’s Inventory Ledger has scaled to cover billions of weekly predictions. Today companies are focused on building resilience and agile supply chains that adapt to evolving global trade dynamics.

Inflation and tariffs are driving consumer uncertainty across the globe.

In such a scenario, real resilience means diversification through data. Leaders use analytics to map supplier networks in real-time, identifying single points of failure before they break. They model alternative sourcing scenarios continuously, not just during crises. When tariffs shift or suppliers falter, systems automatically evaluate backup options.

Inventory strategy has evolved too. Instead of choosing between lean or bloated, AI-driven approaches adjust safety stock dynamically based on demand volatility and supplier reliability scores.

The key shift is from prevention to adaptation. You can’t prevent every disruption, but you can build systems that sense changes early and respond autonomously.

What advice would you give to CTOs and supply chain heads who want to implement agentic AI but face legacy systems and data silos?

Majaz: Start with data infrastructure, not flashy AI projects. Legacy systems and silos kill most AI initiatives before they begin. Pick one high-impact problem like demand forecasting, replenishment, or promotional optimization, where better data could move the needle.

Second, build data bridges, not replacements. You don’t need to rip out legacy systems immediately. Use APIs and middleware to extract data from ERP, WMS, and POS systems into a unified layer. According to Snowflake’s research findings, modern data platforms allow companies to consolidate siloed data while legacy systems continue operating. This lets you test AI without massive disruption.

Third, prove value fast. Pilot Agentic AI on a single category or region. Retailers using AI at scale achieve micro-level personalization and faster trend response, but you need executive buy-in first. Demonstrate ROI in 90 days, then expand.

Fourth, invest in data quality now. AI trained on bad data makes bad decisions. Clean, standardized data beats sophisticated algorithms every time.

The Bigger Picture

Looking ahead to 2026, what does a “self-healing supply chain” look like in your view? A self-healing supply chain should ideally anticipate, prevent and auto-correct disruptions before they cascade.

Majaz: Picture this: A supplier shipment delays in Asia. Instead of waiting for humans to notice, the system instantly simulates impact across your network, identifies which stores will stock out, automatically reroutes inventory from nearby distribution centers, and notifies affected stores within minutes. That’s self-healing in action.

The foundation is real-time visibility. Sensors, IoT devices, and APIs feed continuous data on inventory positions, transit status, demand signals, weather patterns. AI models constantly compare actual vs. expected performance, flagging anomalies immediately.

Then comes autonomous response. Agentic AI systems can now make decisions within 90 minutes of detecting signals. Critical capabilities include: predictive maintenance on logistics assets, dynamic supplier failover when quality issues emerge, automated demand-supply rebalancing, and self-optimizing inventory allocation.

Given McKinsey’s finding that consumers increasingly shop early and trade down based on uncertainty, self-healing systems must adapt to volatile demand patterns autonomously.

By 2026, leading supply chains will handle most issues semi-automatically with agents assisting humans in decision making.

How do you see the human role evolving in the future as AI agents take on more of the operational load?

Majaz: The role of humans will become all the more important as it will shift from operator to orchestrator, from executing tasks to designing strategies and managing exceptions.

AI handles repetitive decisions-replenishment triggers, routine allocations, standard promotional responses. But strategic thinking becomes more valuable. Someone needs to set the objectives AI optimizes toward. Do we prioritize margin or market share this quarter? How aggressive should promotions be? For example- retailers must thread the needle of running smart promotions that drive incrementality while working within the P&L. That’s a judgment call requiring business context AI lacks.

Exception management matters more. When AI flags unusual patterns-unexpected demand spikes, supplier anomalies, margin risks etc, experienced supply chain leaders investigate root causes and adjust strategies.

Humans also govern AI behavior-setting guardrails, validating decisions, ensuring ethical considerations. As AI becomes more autonomous, oversight and accountability become critical leadership functions.

And finally, if you had to sum up 2025 in one phrase for all tech and business leaders, what would it be?

Majaz: Execute in real time or become irrelevant. 2025 separates the adaptive from the extinct.

Companies will have to deploy AI-driven decision-making now or spend the next year explaining why competitors took their market share.

Closing thoughts

This conversation with Majaz Mohammed makes it clear: The holiday season won’t get easier – consumer expectations won’t soften, disruptions won’t slow down, and competitors won’t wait. But with agentic AI, retail, business and tech leaders are empowered to take control of the situation. They can predict earlier, respond faster, and recover instantly.

Majaz’s message for leaders cuts through the noise: you don’t need a bigger warehouse – you need a smarter supply chain. And the smartest ones are already learning, adapting, and self-correcting on their own.

As Majaz says, agentic AI is no longer an experimental capability – it is becoming the backbone of holiday readiness, operational resilience, and revenue protection. Moreover, AI isn’t replacing supply chain leaders – it’s elevating them. As routine decisions are automated, human talent can finally focus on strategy, creativity, and exception-handling.

As leaders step into an even more demanding 2026, one truth stands out: the future belongs to orchestrators, not operators. Those who invest in AI-driven or so-called intelligent supply chains today are the ones who will define the winners’ circle tomorrow.