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7 Procure to Pay AI Use Cases in 2024

Procure to Pay (P2P) process is crucial for any businesses’ success. Where monolithic solutions such as P2P suites fall short, companies can look for specialized solutions that leverage AI in procure-to-pay (P2P) cycles.

For example, Gartner predicts that APAI and supplier e-invoicing will increase from ~$925 million in 2021 to ~$1.75 billion through 2026.1 To give businesses concrete ideas on the integration of AI, we will examine 7 use cases.

1. Sourcing automation & analytics: 

AI can be used to automate several sourcing tasks including need identification, creation of requisitions and supplier identification.

Sourcing bots, that can be configured without coding, can:

  • enable progress visualization
  • trigger and configure events
  • identify optimal award scenarios for user review

Case studies show that examples such as sourcing transportation bots can2:

  • create validation rules for mandatory field controls
  • analyze carrier bids
  • benchmark cost performance
  • optimize scenario analysis

2. Finding suppliers

AI can help identify potential suppliers. With use of various criteria like cost, quality, delivery time, ESG compliance and reliability, it can review a significant amount of data to find the best match. 3 It can:

  • compare supplier performance against contracts and enable you to identify differences immediately, eliminating the need for lengthy research
  • message category managers and invite suppliers

3. Real-time auctions

After detection of potential suppliers, sourcing bots can:

  • open and close bidding rounds
  • analyze bids
  • record bids and negotiations for traceability and policy compliance

4. Accounts payable invoice automation (APAI):

APAI captures invoices and automates the invoice process. APAI solutions match invoices against POs or, in the absence of a PO, support coding and approval processes. These solutions can handle exceptions that might arise from transport and labor costs or additional charges not mentioned in the POs, and bring visibility to both e-invoices with structured data or scanned invoices.

With the rise of deep learning and transformer architecture in the mid 2010s, Hypatos, launched a solution bringing deep learning to invoice processing significantly increasing the automation rate.

Hypatos’ products, with their continual learning abilities offer end-to-end AP automation for enterprises. The company leverages transformer architecture to classify, capture, validate and process invoices. This provides a level of automation that P2P suites typically can not match. (Figure 1)

Hypatos’ integration capabilities.

5. AP compliance:

AI can be utilized in monitoring, analyzing and mitigating enterprise spend risks in procurement. (Figure 2) AI can monitor data across:

  • travel and expense
  • purchase and fleet cards
  • payables, procurement and general ledger

The algorithms can: detect fraud, misuse, errors, and risk patterns, to manage SOX, FCPA, improper pay and do-not-pay audits and sign-offs. They can also detect duplicates and improper POs along with the procurement process and stop the payment.

Purchase card monitoring example. Source: Oversight.4

6. Analytics: 

In procurement, AI powered analytics platforms can:

  • clean, normalize, and categorize spend data and supplier activity from ERPs, contract repositories, travel and expenses, and corporate P-card transactions.
  • automate analysis of current data and historical patterns within an organization

Case studies indicate that these platforms can be used in 5 :

  • connecting, cleaning, and unifying spend data across several countries’ legacy systems including Concur.
  • creating dashboards for analyzing spend, contract, and purchase processes, understanding P-card spend, and PO compliance violations.
  • enabling real-time access to spend reports to check inefficiencies and opportunities.

7. Cloud cost management & optimization

A specific analytics application is cloud optimization in procurement. Cloud spending is growing, and cloud vendors are extracting high margins. 6 In 2022, Amazon Web Services (AWS) generated revenues of 80 billion U.S. dollars- for their services. 7 Therefore, there is significant potential to optimize cloud costs through:

  • Evaluating resources
  • Identifying reserved instances
  • Optimizing the framework
  • Optimizing storage and subscriptions
  • Assigning monitoring tasks to specific employees
  • Benefiting from offers

Case studies show that AI can can 8 :

  • provide real-time data about cloud performance, anomalies
  • collect and analyze the usage data of infrastructure
  • include EC2 schedulers that allows engineers to automate turn on/off cycles of idle EC2 instances
  • include Resource Cleaners that identifies and disposes of idle cloud resources such as EBS, EIP and ELB


Are there P2P solutions that already integrate AI?

APAI solutions are mostly independent solutions. Yet, they can be also included in procure-to-pay suites, or are extensions of financial solutions. Some P2P solutions such as Basware and Medius also offer APAI integration. 

What are the possible benefits of implementing AI in procurement solutions?

  • better cash flow
  • possible reduction in invoice costs
  • more efficient PO management
  • scalable vendor relations
  • secured compliance process

What are the types of AI solutions in procurement?

What challenges might organizations face when adopting AI in their procurement cycle?

  • Resistance from employees who are used to manual processes and may lack the skills needed for this digital transformation.
  • The effective use of P2P AI might require significant amount of historical data; insufficient or poor-quality data can weaken the system’s effectiveness.
  • Integrating AI to other systems trouble-free is not always easy. Technical issues or compatibility problems can occur and hinder the potential of AI to reduce costs and improve efficiency.
  • While AI can significantly helps in decision-making by analyzing data, it should complement human intelligence. Employees should be trained to work alongside AI tools.

For further questions regarding the topic, reach out to us:

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Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
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Burak Ceylan
Burak is an Industry Analyst in AIMultiple. He received his Masters' degree in Political Science from Middle East Technical University. He has background in researching location-based platforms.

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