Automation of manual order handling through AI

Many B2B organizations face increasing demands for faster, more accurate, and more digital customer interactions – without additional resources. When orders sit idle in inboxes waiting to be processed, not only does it slow down customers – it also ties up cash that could otherwise support growth. Sales order handling is a recurring pressure point where manual processes lead to delays, errors, and dissatisfied customers. Consequently, automating sales orders is not just about efficiency, but about strengthening the customer journey through faster and more accurate order placement, freeing up employee time and improving cash flow.

However, this is easier said than done. Order handling is often complex, cutting across systems and involving validations, pricing structures, and credit checks. Especially in the early stages of the order-to-cash process (order intake), where customer interactions are managed locally by sales teams, processes tend to be more decentralized to meet specific customer needs - making them harder to harmonize and optimize than activities further downstream. At the same time, many organizations still struggle with manual data entry, unclear responsibilities, and underutilized systems.

Below, we highlight three key focus areas that KPMG considers critical to achieving a more intelligent and automated sales order process.

1. Create transparency in manual processess

The first step toward automation is to gain full visibility into how sales orders are actually handled today. Without a clear understanding of where time and resources are being consumed, automation risks targeting symptoms rather than root causes.

In most organizations, order handling remains highly manual and fragmented. Teams often spend significant effort navigating complex rules, validations, and exceptions that slow down throughput and increase the risk of errors. Common examples include:

Manually entering order data from emails, customer portals, or PDF documents
Repeated validations of pricing, credit limits, or master data across different systems
Interruptions in the order-to-cash process caused by missing information, errors, and poor data quality, which cause a high sales order rework rate, credit notes, etc.

These inefficiencies add up to lost productivity, delayed order fulfillment, and dissatisfied customers. Just as importantly, they delay invoicing and cash collection – tying up working capital while orders sit idle in inboxes or intermediate steps. More importantly, they prevent employees from focusing on higher-value tasks such as strengthening customer relationships or improving service levels.

Process mining and comparing the existing process to good practice provides powerful ways to create this transparency by highlighting where the real bottlenecks occur. By visualizing the end-to-end flow of order handling, organizations can identify inefficiencies, measure their impact, and build a fact-based case for change. These insights form the foundation for redesigning processes and targeting automation where it will generate the greatest business value.

2. Leverage AI to automate order handling

Once pain points are clear, the next step is to replace manual tasks with intelligent automation. Traditional RPA, Robotic Process Automation, can handle repetitive tasks, but it often struggles when inputs are unstructured, when rules vary, or when exceptions occur. This is where AI adds a new dimension – enabling automation to manage complexity that previously required human judgment.

In practice, this means that systems can:

Read and interpret inputs from diverse sources such as emails, PDF documents, and customer portals – removing the need for employees to re-enter data manually.
Apply logical rules automatically for order validation, pricing, credit checks, and master data management – ensuring compliance and consistency without slowing down the process.
Integrate seamlessly across systems, partners, and geographies via service-oriented APIs – extending automation beyond a single function to the entire order-to-cash chain.

One solution that supports this transition is KPMG’s partner, the AI-based platform Go Autonomous. The platform automatically extracts, converts, and validates order data so it is entered directly into the ERP system, and the customer receives an order confirmation. Autonomous Commerce significantly reduces processing time, minimizes errors, and accelerates order handling – creating measurable benefits for both the organization and its customers. Faster order handling also means faster invoicing and payments – helping to release cash earlier and strengthen liquidity, not just reduce manual workload.

AI-driven automation not only reduces manual touchpoints but also improves accuracy, compliance, and scalability. By combining structured business rules with the flexibility of AI, organizations can create an order handling process that is faster, smarter, and future-ready.

3. Build a strong business case and scale fast

Even the most advanced AI solutions will struggle to gain traction if the business case is unclear. Building a solid foundation for sales order automation starts with identifying where automation can deliver the biggest, most immediate impact – typically in processes that are both high-volume and highly repetitive. These are the activities that consume disproportionate amounts of time, add limited business value, and are well-suited for automation.

To ensure momentum and measurable outcomes, organizations should:

Start small, but scale fast – focus first on automating repetitive, high-volume processes to demonstrate value quickly.
Benchmark against competitors – quantify improvements in speed, accuracy, cash conversion, and cost to validate the business case and secure leadership buy-in.
Show cash flow and FTE impact – faster order handling frees up employees’ time and releases working capital tied up in manual processes.
Integrate automation with B2B webshops and digital channels – creating a seamless customer experience where orders are processed instantly, strengthening both efficiency and customer satisfaction.

A phased approach is often the most effective way: Begin with a pilot, use results to prove value, and then expand automation step by step across markets, systems, and geographies. By taking this route, organizations can avoid lengthy “big bang” projects, reduce risk, and build organizational confidence in the new way of working.

Ultimately, a well-structured business case ensures that automation is not seen as a technology experiment, but as a strategic initiative that supports growth, competitiveness, and long-term digital maturity.

Conclusion

Automating sales orders with AI is more than a technology upgrade – it is a strategic opportunity to strengthen competitiveness. Organizations that move from manual data entry to intelligent, autonomous processes can achieve significant benefits in terms of reduced costs, faster throughput, improved cash flow, and greater employee and customer satisfaction.

By combining insights from process mining, AI-driven automation, and a clear business case, organizations can take a decisive step toward a more mature, efficient, and digital future.