Generative AI
AI-enhanced order fulfilment: boosting efficiency and accuracy in supply chains
Order fulfilment continues to be a high-stakes process for businesses worldwide. Delays, errors, or stockouts can lead to lost revenue and eroded customer trust. Therefore, as markets grow more competitive, many organisations are now turning to AI operations management to enhance speed, accuracy, and overall performance.
Global spending on AI in supply chains is in fact expected to reach USD 157.6 billion by 2033, reflecting an unprecedented compound annual growth rate (CAGR) of 42.7% between 2024 and 2033.
A significant part of this shift involves using generative AI for business processes, which allows systems to learn from large datasets, adapt to real-time changes, and deliver faster outcomes than before. Traditionally, for instance, predicting demand fluctuations was a tedious and tardy task, often relying on historical sales figures and basic market indicators. However, now, with generative AI solutions, businesses can easily pull together vast amounts of information, from social media trends to regional buying patterns, and forecast potential spikes or drops in demand. As such, businesses can have better and faster forecasts, allowing them to fine-tune their inventory levels and allocate resources more precisely. This reduces both the risk of overstocking—which can inflate holding costs—and the risk of stockouts that compromise timely deliveries.
In addition to predicting demand fluctuations, generative AI for business processes can also enhance inventory strategies. For example, if a popular item sells out quickly in one region, generative AI solutions can recommend re-routing stock from a nearby location where demand is currently lower. These instantaneous adjustments help organisations remain agile and avoid disappointing customers with prolonged waits. This concept also extends to transport networks. When machine-learning models, for instance, detect any shipment delays at a particular hub, generative AI can automatically advise an alternative route or carrier, thus reducing any ripple effects on final delivery times.
Another dimension in which AI is changing the game for supply chains is end-to-end operations management. In practice, order fulfilment typically spans manufacturers, suppliers, distributors, and retailers—each with their unique systems and procedures. Intelligent process automation can help bridge these gaps by providing a unified platform that keeps everyone informed. For example, if a production facility suddenly encounters a raw material shortage, the AI operations management system can instantly alert the distribution centre, allowing logistics teams to either source alternatives or update the final delivery estimates.
Intelligent process automation can also help eliminate repetitive tasks that are highly prone to human error, such as cross-checking shipping addresses, confirming stock availability and generating transport documents. This not only speeds up the process but also allows the personnel to focus on strategic tasks, such as negotiating contracts or exploring new supply chain partnerships.
Adding to the list of tasks it can perform, intelligent process automation can also enhance fraud detection and cybersecurity by analysing real-time transaction data and quickly flagging suspicious patterns. It can even be used to automate compliance checks and help institutions navigate complex regulations such as AML and KYC, reducing risk and safeguarding financial assets.
Clearly, the benefits of generative AI solutions make it one of the most promising solutions for supply chain businesses. However, implementing any new tool comes with its own set of challenges and considerations. Implementing AI successfully, so to speak, calls for clean, standardised data. The stronger the underlying data quality, the more accurate the AI-driven insights.
Additionally, organisations need to go slow while integrating AI. They should start by identifying areas with the highest volume of repetitive tasks or the greatest risk of human error and automating these processes first, before expanding AI’s role to more complex operational decisions, such as multi-node route optimisation or dynamic pricing based on demand levels.
In the future, generative AI will certainly expand to even more areas, such as robotics. Many organisations have already started experimenting with integrating generative AI solutions into advanced robotics for warehouse tasks. Robots guided by AI algorithms can pick and pack items more quickly than human counterparts, reducing errors and speeding up delivery times. While human oversight remains crucial, especially for solving complex or unusual scenarios, this trend points to a future where AI and human expertise complement each other to boost supply chain efficiency.
Those who act quickly stand to outperform competitors with speedier deliveries and dependable service in a market where customers expect the very best.
How can Infosys BPM help?
Infosys BPM’s AI-first Operations uses generative AI solutions to make order fulfilment faster and more accurate. We help businesses automate routine tasks, improve decision-making, and reduce errors. With intelligent process automation, we optimise workflows, ensuring smooth operations and better customer satisfaction. Our approach brings not only efficiency at scale but also makes supply chains more resilient and responsive to demand shifts.
