What logistics leaders are really asking about AI
February 4, 2026
Artificial intelligence has been a constant topic in logistics and supply chain conversations over the last few years. In our recent fireside chat with Oana Jinga and Divya Gautam, we took a step back and asked a simple question: what do logistics leaders actually want to know about AI right now?
In the weeks leading up to the session, we collected questions from customers and from the wider industry on social media. What emerged was not hype-driven curiosity, but very practical concerns around data, decision-making, and real operational impact.
Below is a summary of the core themes and insights from the discussion.
Why AI has become central to logistics
AI is no longer a future concept for logistics teams. It has moved to the center of daily operations because complexity has increased faster than traditional tools can handle. Warehouses and supply chains now generate enormous volumes of data every day, from inventory movements to labour activity and system events.
The challenge is no longer getting data, but making sense of it quickly enough to act.
AI helps bridge this gap by continuously analysing operational data, identifying patterns humans would struggle to see, and supporting faster, more confident decisions. But this only works if the data feeding those models is accurate and current.
The hidden risk: Data decay
One of the most important topics discussed by Oana and Divya was data decay. Data decay happens when analytics and AI systems rely on stale or delayed information. When that occurs, even sophisticated models can produce misleading or incorrect outputs.
In practical terms, this can look like:
- Dashboards that no longer reflect what is really happening on the warehouse floor
- Forecasts that are technically correct, but operationally irrelevant
- AI systems that appear to “hallucinate” because their inputs are outdated
The consensus was clear: AI is only as good as the freshness of the data behind it. Real-time or near-real-time data streams are essential if AI is expected to support operational decisions rather than just retrospective reporting.
Can AI help prevent stockouts and overstocking?
A major question from the audience was how AI can help reduce stockouts, overstocking, and costly write-offs. These issues remain some of the biggest efficiency killers in warehouses.
The discussion highlighted that these problems are often symptoms of the same root cause: limited visibility into what is actually happening right now.
When data is delayed or incomplete, teams tend to compensate by:
- Holding excess inventory “just in case”
- Reacting too late to demand changes
- Missing early warning signs of anomalies
AI, when powered by continuous and reliable data, enables:
- Predictive analytics based on historical and real-time trends
- Early detection of unusual patterns or anomalies
- Better forecasting that adapts as conditions change
AI systems can scan data every day, spot trends, and surface insights at a speed no human team could match. People, in turn, apply context, judgment, and experience to act on those insights.
Rather than reacting after the fact, teams can anticipate issues before they become costly disruptions. The most successful implementations treat AI as a decision-support layer, not an autonomous black box.
Getting started the right way
For organisations looking to move beyond experimentation, this session emphasised a few practical starting points:
- Focus on data quality and timeliness first – without this, AI initiatives will stall.
- Start with clear operational problems – such as stock accuracy, forecasting, or throughput variability.
- Integrate AI into existing workflows – insights must be actionable, not isolated in reports.
AI delivers value when it is grounded in real operational needs and fed by reliable, real-time data.
Final thoughts
AI in logistics is no longer about asking if it will be adopted, but how it should be applied responsibly and effectively. The questions raised in this fireside chat show a maturing perspective across the industry: less hype, more focus on outcomes.
By addressing data decay, improving visibility, and aligning AI with real warehouse challenges, logistics teams can turn AI from a buzzword into a genuine competitive advantage.
If you missed the live session, this recap captures the key ideas, but you can also listen on-demand here.