Tight margins, rising labor costs, regular disruptions, and customers' expectations of fast and flawless deliveries mean supply chain companies often fight to maintain market share and delivery excellence. Generative AI (Gen AI) helps C-suite leaders, managers, and analysts surface connected data and insights to fuel better in-the-moment decisions and longer-term planning. 

Operational data is being created every millisecond by warehouse machinery, software, and business processes, but for most companies, it’s locked away in silos and wildly differentiated formats. Leaders and managers are stuck making decisions based on old reports, limited dashboard views, dizzying spreadsheets, and gut instinct. 

My colleague, Ashwin Sridhar, Head of Supply Chain & Logistics Solutions at Google Cloud, shares his perspective. “Without the full picture, it’s difficult for warehouse and supply chain leaders to get ahead of disruption and to solve repeated issues that drain efficiency and affect the customer experience. Gen AI enables non-technical employees to enhance decision-making, explore root causes, and improve ground planning with critical data.” 

Gen AI transforms warehouse decision-making and operations to reduce costs, diminish order-to-ship times, and shrink risk. 

Leveraging AI to anticipate disruptions is top of mind for supply chain leaders, according to The Hackett Group’s 2025 Study. The study indicates 89% of supply chain executives are actively scaling AI to improve decision-making; 75% cite economic uncertainty as a major risk, driving urgency around AI adoption, with key investment areas in predictive analytics, autonomous planning, digital twins, AI-driven procurement, and workforce upskilling.

Supply chain leaders are aware of the measures they should take to shore up resilience and reduce costs, especially those who have been leading their companies through disruptions over the past several years. But what’s been missing from the decision-making process for many is the confidence and new insights that come from the ability to connect the dots across all their data — historical and real-time, warehouse and enterprise, and traditionally incompatible formats.

Gen AI interfaces that use retrieval-augmented generation (RAG) to access protected company data safely help non-technical leaders and analysts quickly and rigorously dig into this complex information. Gen AI identifies patterns, trends, and anomalies amid petabyte-scale data volumes and summarizes the findings for easy digestion, sharing, and further exploration. Rather than poring over spreadsheets and dashboards, Gen AI works with natural-language questions.

Leveraging AI-driven data analytics in the warehouse helps supply chain companies pursue more profound efficiencies, cost savings, and competitive advantages. These include:

• Demand and inventory planning: Gen AI can act as a planning copilot, helping teams make sense of demand, inventory, and supply signals that were previously scattered across reports, emails, and local spreadsheets. Instead of manually stitching together history, promotions, and constraints, planners can ask questions in natural language, for example, “Where are we most at risk of stockouts next week given current inbound delays?” and get a synthesized view with key drivers highlighted. Agentic AI then goes a step further by proposing replenishment moves or safety stock adjustments and packaging them into scenarios that can be compared, simulated, and approved by the planning team. 

• Operational efficiency on the floor: On the warehouse floor, Gen AI can continuously analyze order profiles, congestion, service levels, and asset utilization to uncover bottlenecks that are hard to see in real time. A supervisor can simply ask, “Why did we miss our outbound SLA on the late wave yesterday?” and receive a breakdown of root causes across labor, equipment, and order mix. Agentic AI can then suggest concrete actions: from adjusting wave release patterns and task interleaving rules to reallocating work between zones and present these changes to supervisors for review before they are applied in the warehouse control and execution systems. 

• Staffing allocation and training: Labor remains one of the largest and most variable cost drivers in warehousing. Gen AI can help leaders forecast labor needs by correlating order volumes, skills, seasonality, and historical performance, and then summarizing where capacity gaps are most likely to appear. In parallel, an AI copilot can support associates with on-the-job guidance, answering “how do I…” questions or walking them through unfamiliar tasks. With agentic AI, these insights can be translated into proposed shift plans, cross-training recommendations, and real-time task reassignments during the day, always with clear explanations and human supervisors retaining final approval.

• Machine maintenance and asset reliability: As data from conveyors, automated storage and retrieval systems (AS/RS), automated mobile robots (AMRs), and other equipment becomes richer, Gen AI can help maintenance teams make sense of fault codes, error logs, condition monitoring data, and work history. Instead of reading through dozens of tickets, technicians can ask, “What patterns do you see behind last month’s unplanned downtime on this aisle?” and receive a concise explanation with likely root causes. Agentic AI can automatically open and pre-populate work orders in the enterprise asset management (EAM) system, group related tasks, and propose optimal timing for interventions that minimize impact on throughput, with technicians and planners validating and adjusting those plans.

And it’s no longer just about tables and charts. Today’s Gen AI systems can increasingly work across text, structured data, images, video, and sensor streams, for example, combining warehouse management system (WMS) and warehouse control system (WCS) data with camera feeds and telemetry from material handling equipment, to give a more complete, real-time picture of what is happening in your operations.

Gen AI-driven data-dives help organizations determine where to best focus their investments and efforts, from optimizing inventory location and storage to determining which shifts or operators need retraining to understand the value and risk of extending the lifespan of current equipment. 

Gen AI, for example, can examine the correlation between late, damaged, or missing shipments and particular SKUs, sites, shifts, or operators. Then it provides a digestible summary and recommended solutions with impact analysis.

Forward-thinking leaders recognize that Gen AI's strategic value transcends warehouse operational efficiencies. Gen AI and data-driven operations are inextricably linked, creating transformative power across the enterprise. The ability to effectively harness and utilize data is key.

Gen AI and data-driven operations go hand in hand. The ability to truly utilize valuable data from warehouse equipment and other business systems has widespread transformative power.

Traditional AI analyzes data to recognize patterns, make predictions, or classify information. Gen AI creates new content, such as exploratory analytics summaries and suggested actions. 

This functionality creates reports and stakeholder updates, enabling more data-driven knowledge to spread throughout the organization without taxing busy managers, analysts, and other employees. Instead of laboring to gather the proper data, analyze it, and write a report, they call on Gen AI to assist and edit the results. 

The employees closest to daily processes often possess invaluable insights into operational inefficiencies and improvement ideas. Gen AI makes it easier to explore, model, and iterate ideas submitted by employees who may recognize — and be frustrated by — bottlenecks and redundancies that are less obvious to leaders. 

According to the 2025 Employee Experience Trends report by Perceptyx, only 31% of employees strongly agreed that their opinions mattered at work. Listening to employees' ideas leads to surprising operational efficiency and productivity gains. It demonstrates that company leaders care about what employees think — a key to engaging employees in an industry with high turnover. 

Finally, partnering with technology vendors that prioritize AI capabilities makes it easier to leverage future innovations. Technology companies like Dematic and Google Cloud are exploring the role of Gen AI in more intelligent automation. 

Much of today’s automation is based on thresholds. An autonomous action occurs if a fixed input set reaches a certain threshold. Dematic is exploring the ability for more prescriptive automation based on multi-AI agent systems that include Gen AI, which brings in much data to base decisions. As we bring innovations like this to production, we like to work with early adopter clients to explore the possibilities together.

Organizations access cutting-edge AI capabilities and future innovations beyond basic threshold-based automation by partnering with technology partners, including Dematic and Google Cloud. These partners explore prescriptive automation through multi-AI agent systems incorporating Gen AI. 

As AI plays a more active role in warehouse and supply chain operations, strong guardrails are just as important as powerful models. Role-based access control, secure retrieval of data from trusted sources, audit trails, and human-in-the-loop approvals ensure that copilots and agents act on the right information and stay within clearly defined boundaries. Dematic and Google Cloud design Gen AI and agentic solutions with enterprise-grade security, governance, and compliance at their core, so customers can innovate confidently without compromising safety, data protection, or regulatory requirements.

Dematic’s Gen AI solutions are built on Google Cloud’s modern data and AI stack, bringing together BigQuery, Gemini models, Vertex AI, and Dematic’s own software products such as Dematic Control Tower and Sprocket enterprise asset management (EAM). This combination gives customers a governed, scalable way to connect operational data, expose it through natural-language experiences, and embed AI directly into the decisions that matter most.

Just as important, these capabilities form a practical foundation for agentic AI in the warehouse. By anchoring goal-driven AI agents in proven Dematic applications and co-engineered Google Cloud integrations, customers can move step-by-step from better visibility and decision support toward more prescriptive, semi-autonomous operations, always with humans in control of the objectives and constraints.

For organizations navigating tight margins, labor constraints, and rising service expectations, this partnership offers a proven path to turn Gen AI from an experiment into a durable advantage across their warehouse and broader supply chain network.