3 Ways AI is Changing Procurement for the Better

How artificial intelligence is helping procurement leaders optimize cost and performance

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When people think of digital transformation and artificial intelligence (AI), procurement is not necessarily the first thing that comes to mind. However, in between increasing supply chain disruptions, inflation and commodity price volatility, amongst other challenges, the need to automate and optimize procurement processes has never been greater.

Data-rich and task-heavy, procurement provides fertile ground for a wide variety of AI applications. These new, cutting-edge solutions not only have the potential to accelerate processes across the source-to-pay lifecycle (S2P), but increase visibility into spend and enable enhanced, data-driven decision making. 


AI-Powered Spend Analytics

Optimizing spend while ensuring compliance, quality and other strategic objectives are met can be quite the juggling act. Simply put, spend analytics use AI to provide transparency and insight into where cash is spent. However, AI-powered spend analytics go beyond standard procurement intelligence by:

  • Using ABC analysis to identify savings opportunities
    Performing tail spend analysis to help organizations further increase the efficiency of procurement processes
  • Analyzing supplier relationship management (SRM) data help organizations consolidate vendors and leverage those relationships to obtain deeper discounts
  • Identifying spend anomalies
  • Predicting future spend, a capability that is especially helpful when formulating yearly budgets
  • Highlighting supply-market exposure and associated risks
  • Determining and communicating the environmental and social impact of purchasing decisions 

In fact, the Hackett Group predicts that the use of data visualization tools and advanced analytics in procurement will grow by 19% in 2022 compared to 2021, making it one of the most popular areas of procuretech. In addition, researchers from Deloitte found that project that use AI spend classification have typically been able to achieve around 97% accuracy in the classification of data, leading to “increased precision in the analysis of spend information, driving more value for an organization.

 

New Supplier Search Accelerated 

Finding new suppliers is no easy task. In fact, on average, the new vendor search process requires 40 hours of manpower and takes about 3 months. This makes keeping up with market fluctuations in real-time next to impossible.

With the help of natural language processing (NLP) and robotic process automation (RPA), organizations can scan through thousands of vendor websites per day and assess them using pre-defined criteria. In just a few hours, organizations can obtain a shortlist of suppliers that align with their unique financial, sustainability and regulatory compliance objectives. They can also automate supplier risk monitoring and scorecard creation. 

 

Contract Analytics

At any given time, procurement professionals may be juggling hundreds if not thousands of supplier contracts. Ensuring that the deliverables outlined in these contracts are met while constantly being on the look for potential areas of risk as well as optimization can be incredibly arduous if not impossible.

AI-powered contract analytics can help solve many of these problems by using NLP and machine learning to “read” as well as “manage” thousands of contracts at once. While unstructured data and document processing tools extract data from contrast, machine learning techniques can be used to make sense of the massive amounts of data pulled from these documents. 

For example, AI algorithms can automatically classify both the document as a whole and specific clauses. If there are any deviations in the language, the AI application will flag it and notify the appropriate humans. Increasingly, these tools can also be used to automate contract creation. 

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