How is Sievo Guiding the use of AI in Procurement?

Sievo's guide on AI is navigating through the noise to focus on procurement, looking how AI can enhance its operations

The procurement analytics partner for data-driven leaders and enterprises, Sievo has published 'the ultimate guide for AI in procurement', an online guide and e-book on the subject.

This guide navigates through the hype and buzzwords surrounding artificial intelligence (AI) in procurement, focusing on real business opportunities. It explores subjects such as machine learning for spend analysis and explains cognitive procurement.

Sievo also explains how AI can enhance procurement operations by automating tasks, providing insights, and improving decision-making.

What does the guide include?

This guide has 11 chapters, exploring subjects including how AI can enhance procurement operations by automating tasks, providing insights, and improving decision-making. 
The text distinguishes between strong and narrow AI, emphasising that current applications in procurement reflect narrow AI, which addresses specific challenges. "All known applications of AI in procurement today reflect narrow AI, which is also referred to as weak AI," the guide says. "They provide smart solutions to very specific and predefined challenges. While they may be limited in scope, narrow AI has the more immediate potential for improving operational efficiency."​​​​​​​

What Procurement AI is NOT

The guide also looks at what AI is not, saying there is a lot of hype and misunderstandings surrounding artificial intelligence. 
The text adds: "It should not be seen as a replacement for human procurement expertise, or be considered a new team member capable of driving organisational change, strategic sourcing, or realised savings. For Procurement, AI is not a magic solution you can tap to solve your problems. All AI solutions today in Procurement will require active expert guidance and oversight."

Types of AI in Procurement

Sievo also discusses the types of AI relevant to procurement, including:

  • Artificial Intelligence (AI): any algorithms exhibiting any behaviour considered ‘smart.’ Machine Learning (ML): algorithms that detect patterns and use them for prediction or decision making
  • Natural Language Processing (NLP): algorithms that can interpret, transform, and generate human language
  • Robotic Process Automation (RPA): algorithms that mimic human actions to reduce repetitive, simple tasks. RPA is generally not considered a form of AI.

It also provides examples of AI applications in procurement, such as spend analysis, anomaly detection, and contract management. This is in addition to outlining steps for implementing procurement automation and insights into the future potential of AI in procurement.

The guide also provides insights into the application of supervised learning with procurement data, machine learning in procurement, natural language processing (NLP) in procurement, and cognitive procurement.

It outlines how supervised learning involves human supervision to train AI algorithms, focusing on classifying procurement data. Machine learning, a subset of AI, is explored in terms of its applications, including supervised, unsupervised, reinforcement, and deep learning.

Sievo's 'the ultimate guide for AI in procurement' looks at the possibilities of machine learning in procurement (Credit: Sievo)

Where AI can support procurement

The guide highlights key areas where AI can support procurement, such as making better decisions, identifying new opportunities, and automating manual tasks

Key facts
  • Make better decisions – artificial intelligence can provide timely analytics and data-driven insights to make better sourcing decisions
  • Identify new opportunities – shifting through vast amounts of data, AI can uncover new savings or revenue opportunities
  • Improve operations – artificial intelligence has the potential to streamline or align internal business operations, even in large organisations with many business units or geographic locations
  • Automate manual tasks – AI can automate many time-consuming tasks, such as monthly processes, or Procurement performance reporting
  • Free up time – by taking care of more routine tasks, AI can free up Procurement resources for more creative or strategic tasks like key supplier relationship management
  • Capture or apply scarce knowledge – artificial intelligence can help Procurement organisations capture relevant new sources of data, for example, from external data sources like the Internet
  • Identify new suppliers or markets – with access to vast amounts of external data, AI can help identify new suppliers or even new markets to enter
  • Optimise supplier relationships – AI has the potential to make supplier relationship management more data-informed.

The guide delves into machine learning for spend analysis, specifically spend classification challenges and solutions. It also discusses NLP's role in contract management and other procurement tasks.

Furthermore, it introduces cognitive procurement, explaining its principles and challenges.

It concludes with AI best practices in procurement and speculations on the future of procurement with AI, emphasising automation, value creation, spend transparency, and agile supplier ecosystems.


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