Shri Pradeep Panicker, CEO, DIAL, expects AI to make its greatest impact where large volumes of data meet operational decision-making.
Readiness, not enthusiasm, determines AI’s lasting value
Demand forecasting and capacity planning will grow more accurate, helping organisations anticipate shipment volumes and allocate resources efficiently, while route and network optimisation will move goods faster and more cost-effectively, with greater resilience to disruption. Intelligent inventory management and automation will lift warehouse productivity, and real-time visibility, predictive maintenance and risk management will address delays, equipment failures and disruptions proactively.
Four Foundations of Adoption
Organisations exploring AI should focus on four key areas- Data Readiness demands accurate, integrated and real-time data; Business Value comes from prioritising use cases that solve operational challenges, such as demand forecasting, route optimisation, warehouse efficiency or shipment visibility; Technology Infrastructure must support AI integration, automation and data sharing; while People and Governance call for workforce upskilling, change management, cybersecurity and regulatory compliance. Ultimately, AI initiatives should align with clear business objectives and deliver measurable improvements in efficiency, resilience, customer experience and cost optimisation.
Barriers on the Road
Despite its potential, adoption remains slower than expected. Data is the biggest hurdle: stakeholders on different systems create fragmented, inconsistent data that limits the accuracy of AI-driven insights. Legacy infrastructure also makes implementation difficult, as older systems often need upgrades before AI can be deployed effectively. High implementation costs, concerns around data security and regulatory compliance, and a shortage of AI and analytics talent add further barriers. Finally, many organisations are still adapting to the cultural and process changes involved, making change management a critical success factor. If data and change management are the hurdles, the journey begins with choosing the right problem to solve.
