Considering Robotics to Update Your Procure-to-Pay Infrastructure? Here are Some Thoughts & Ideas


The role of emerging technologies in source-to-pay automation

Advances in software and artificial intelligence have vastly expanded both the number of activities that can be automated and the degree of automation that is possible. For example, the McKinsey Global Institute has found that across occupations, activities accounting for 46 percent of US workers’ time could be automated using already-demonstrated technologies.

We believe that five emerging technologies are particularly pertinent to source-to-pay:

  • Robotic process automation uses simple rules to emulate repeatable tasks that would otherwise be performed by human users of software. One large advantage of RPA is that, unlike traditional system-integration approaches requiring access to each relevant software program’s underlying code, the bots that perform specific tasks only need to access other software in the same way a human user would. A consequence is that bots can only perform tasks in situations where there is no room for interpretation—but many tasks in source-to-pay (such as invoice upload and approval) are sufficiently unambiguous to make deployment worthwhile.

RPA can, therefore, help companies stitch together their existing systems, eliminating low-value manual interventions at the interfaces between systems and process steps. A large basic-materials company found that by deploying bots to scan and code invoices directly into its core enterprise-resource-planning (ERP) system, it could reduce invoice-processing costs by 80 percent.

  • Machine-learning algorithms, unlike RPA, can handle tasks that involve complex rules and require some form of pattern recognition to be executed correctly. Machine learning is therefore suitable for tasks that traditionally require some level of human judgment, such as the assignment of transactions to formal spend categories and subcategories—a crucial first step in uncovering sourcing opportunities. A financial institution that is currently deploying machine learning for these judgments has greatly increased the accuracy and speed of essential analyses, such as the concentration of spend by supplier for a given category. As a result, the institution is finding procurement savings much more quickly than was previously possible.

At a global technology services company, machine learning now guides its negotiation approach, tracking and evaluating the success of different negotiation tactics in different settings. The resulting patterns lead to specific recommendations on which type of negotiation is likely to be most successful in a particular situation.

  • Smart work-flow technologies can link tasks conducted by different people and machines into a coherent process with well-defined handoffs—even if the combination of tasks differs markedly from case to case, as in risk-management processes for supplier qualification. A business-services company is piloting a smart work-flow solution that dynamically routes work between procurement and financial systems according to whichever risk-management logic applies to a given supply contract. The system then governs the assignment of tasks across all participants in onboarding, risk-assessment, and supplier-certification activities, and may eventually cover supplier performance management as well.
  • Natural-language-processing (NLP) technologies process textual data and provide a convenient way for purchasers to document requirements without resorting to drop-down menus or structured lists. The technologies are already in use in consumer environments, guiding customers to appropriate technical-support resources or extracting insights about product performance from social-media streams. In procurement, they may assist in organizing many types of unstructured information.

The example that opened this article may sound like science fiction, but a European multinational is already piloting such technologies (in combination with RPA) to digitize its sourcing approach for its long-tail spend—the long list of small purchases that together may account for only a few percentage points of the budget. Robots in each major external-spend category process incoming orders from the business, using NLP to interpret free-form text and match order requirements to particular groups of suppliers. The procurement system then automatically sends out requests for bids, which the robot can compare. An internal buyer is notified once the bids have been received so that he or she can decide which bid to accept based on the information provided by the robot.

  • Cognitive agents can be deployed whenever a deep knowledge base must be quickly searched to determine the right course of action. The chatbots that several financial-service institutions now use in assisting contact-center staff can answer a wide range of customer queries by selecting appropriate responses from a previously documented set of answers. In the source-to-pay process, vendor and business-procurement help desks often involve similar types of interactions, pointing toward a similar solution.

As the capabilities of cognitive agents improve, they may also become useful for even more complex tasks, such as estimating an item’s global-sourcing potential by comparing its cost, quality, and technology requirements with databases of similar products and sourcing decisions. By analyzing supplier capabilities, cognitive agents may even be able to make recommendations on the selection of specific suppliers.

Because source-to-pay is such a complex, diverse set of activities, it has not until now been clear exactly how much of the entire end-to-end process is suitable for automation or where the primary sources of value lie.

We therefore undertook a new type of analysis that decomposed source-to-pay into a large number of discrete tasks. That let us assess how easily each task can be automated using currently available technologies, and which types of those technologies are most appropriate to achieve that level of automation.

The automation requirements of any task will depend on the specific combination of capabilities required, together with the complexity associated with the relevant capabilities. For example, some tasks might require a modest amount of pattern recognition (for example, the data-lookup functions used in spreadsheets), while others may require the ability to recognize highly complex patterns (for instance, assessing which suppliers are most likely to cease being a sustainable source of supply).

Seeing the potential across the entire source-to-pay process

We mapped the complexity requirements across each of the 18 capabilities, using a highly granular 240 item taxonomy covering every task in the end-to-end source-to-pay process. While a majority of the 18 capabilities are required at many stages of the process, the level of complexity associated with these capabilities is fairly low. In most cases, existing technologies can meet those requirements.

Our analysis shows that, overall, 56 percent of the tasks associated with the source-to-pay process are fully or largely automatable using existing technologies. That’s a significant finding, suggesting that source-to-pay activities as a whole are more suitable for automation than is a typical US-based job.

Unsurprisingly, the automation opportunity is highest in the more transactional parts of the process: in placing and receiving orders, 88 percent of tasks can be automated, and the figure rises to 93 percent in payment processing. Moreover, even the strategic elements of source-to-pay show considerable automation potential.

Choosing the right automation technologies

Although each task within the end-to-end source-to-pay process needs to be evaluated individually, some generalizations can be drawn by comparing the requirements of different parts of the process with the capabilities offered by different automation approaches

  • NLP technologies are applicable across many tasks in the process, as a bridge between human inputs and the most structured data used by machines.
  • RPA is most applicable to the more transactional activities associated with procure-to-invoice and invoice-to-pay activities.
  • Smart workflows apply to more-complex transactional activities, and especially in vendor management with its high degree of contextual information and its need to coordinate among multiple parties.
  • Machine-learning and cognitive-agent capabilities are most applicable to the more complex activities associated with the initial parts of the source-to-pay process, such as the development of spend-category strategies and the identification and selection of potential suppliers.

The starting point in source-to-pay automation

For companies, the next step is to identify the best targets for automation within their own processes. Organizations can do this by first evaluating the current level of automation they have implemented, compared with what is technically achievable for each task in the source-to-pay process. They can then estimate the value of closing each gap.

This value will come both from the amount of work that can be automated and from likely improvements in compliance, cycle times, and payment terms associated with each step. Any gains must be set against the cost and complexity of implementing suitable technologies. Robotic process automation and smart work-flow solutions are likely to be quicker and cheaper to implement than sophisticated machine-learning technologies and cognitive agents, for example.

The emerging picture of the end-to-end process will provide a comparison of the relative benefits of addressing each step within it, set against the relative cost and difficulty of automating those steps.


The digital landscape is moving quickly. Companies that are prepared to experiment while taking a thoughtful, focused approach to the application of these technologies are likely to reap savings worth as much as 3.5 percent of all external spend. Even more important, by freeing sourcing personnel from routine tasks, automation allows them to spend time pursuing innovative sources of additional value.

In any organization, procurement and accounts payable activities are inextricably linked. Becoming efficient in both types of activities should result in clear benefits to the entire procure-to-pay process, from the procurement function to accounts payable. APQC has identified business drivers of an integrated procure-to-pay process that leads to improvements and business results. One of these drivers is the automation of procure-to-pay activities. Organizations that do not take steps to automate transactional processes simply cannot match the speed, efficiency and effectiveness of those that do.

Automation’s primary influence is on the efficiency of procurement staff. The adoption of automated purchase order processing allows procurement staff to be more efficient and productive. This, in turn, can reduce staffing costs associated with purchase order processing. It can also allow the organization to shift employees from the more basic task of processing purchase orders to more value-added activities within the procurement function.

Organizations that automate the procure-to-pay processes achieve faster cycle times and more efficient purchase order processing.

Along with shorter purchase order cycle times and more purchase orders processed per FTE, organizations with automated procure-to-pay processes have shorter supplier lead times.

There is a 2-day difference in supplier lead time among top performers. The difference is even more prominent at the median, with organizations that have invested in e-procurement having nine fewer days of supplier lead time than their counterparts without automated procure-to-pay processes. The increased data visibility created by e-procurement systems between organizations and their suppliers is a key contributor to this difference.

It can be challenging for organizations to determine how to approach moving from manual procurement activities to an automated focus. Professional services firm McGladrey has identified several steps to effectively guide the transition to an automated procure-to-pay process.

  • Develop an overall strategy for managing procurement and accounts payable within the organization.
  • Assess what technology is currently available in-house. Many organizations already have procurement automation modules available as part of their ERP systems. Using a module from an already deployed system will come with the added benefit of integration.
  • Look outside the organization to leverage specialized procure-to-pay solutions that take advantage of lessons that others have already learned. Many of the existing best-of-breed solutions for procurement come with effective best practices programmed into the package. Solutions to consider include document management tools, e-invoicing and automated requisition systems with tracking for workflow and approvals tied back to internal controls.

Organizations that have automated their procure-to-pay processes using e-procurement systems clearly have advantages over their counterparts that have not adopted automation. These organizations have faster and more efficient purchase order processing, which can allow them to shift their procurement staff members to other activities that provide a higher value to the enterprise. They also have more mature supplier relationships that can reduce the amount of lead time taken by their suppliers for ordered materials.

Modernization of Indirect Purchasing

Procurement is not the only function within the enterprise interested in the strategic benefits associated with modernizing the procure-to-pay process. APQC recently conducted a survey of procurement and finance professionals to learn about organizations’ plans for modernizing the purchasing and accounts payable aspects of the procure-to-pay process for indirect purchases.

The results indicate that nearly 85 percent of the survey respondents (both in the procurement and accounts payable functions) believe their procure-to-pay process would benefit from modernization efforts.

Modernization Across Areas

Many organizations recognize the need to modernize and streamline their procure-to-pay process. In many organizations, this has taken the form of adopting e-procurement or e-sourcing technology.

However, technology adoption has not been consistent across all areas involved in procure-to-pay. According to benchmarking in accounts payable, organizations manually key a median of 60 percent of their invoices into their financial system. Top-performing organizations have pushed that percentage down to 43 percent, but this still leaves quite a bit of room for improvement.

To reap the most benefits from modernizing and streamlining the procure-to-pay process, organizations should evaluate how activities are performed both within the procurement function and the accounts payable group.

By identifying areas in which modernization can help to streamline activities, organizations take a step toward the following goals:

  • enhancing service to internal customers, reducing cost
  • enhancing staff productivity
  • bolstering policy enforcement and controls
  • increasing their ability to identify and reduce maverick spending
  • enhancing collaboration with vendors

The technologies that support a modernized procure-to-pay process can lead to multiple benefits. For example, e-catalogs accessible via online portals make it easy for employees to order supplies from approved vendors and at pre-negotiated prices.

Analytics software can help organizations easily analyze patterns of spending, which in turn can inform pricing negotiations. Cloud-based technology such as the e-procurement system, with its embedded collaboration, workflows, and analytics – can reduce the processing burden on both procurement and accounts payable employees.

As with any technology, organizations may have concerns that adoption will not result in promised cost savings or that there will be change management issues. For cloud technology, in particular, organizations may be concerned about the potential for IT security risk. Organizations can address these concerns by using a pilot approach to roll out the new technology.

By testing the technology’s use for a specific task or area, organizations can layout mitigation plans for their concerns and then expand adoption over time.