A substantial value of AI is realized today in terms of increasing productivity through automation. Enterprises are now leveraging AI to drive better, faster, and smarter business decisions. For companies already at the forefront of using AI, 41% of them say they have improved decision-making as a result, according to the PWC 2022 AI Business Survey.

In traditional data-driven decision-making, data experts present curated dashboards and insights to business users. Those users in turn use these insights to make strategic and tactical decisions. The decisions are influenced by cognitive biases and intuition, which doesn’t always lead to optimal outcomes.

In order to fully leverage the power of data, organizations can use AI to improve decision-making. AI will not only derive insights from a pool of data points; it can also enable decisions by providing recommendations for users to agree or disagree with. AI-driven decision-making is already part of our lives. For example, AI-decision making in healthcare quickly analyzes medical imaging reports and suggests diagnoses to doctors to improve medical outcomes. In the energy and utilities sector, AI-driven intelligence makes automated decisions on energy and cost savings to maximize investments and asset performance.

Similarly, procurement teams can benefit from using AI to drive decisions to source smartly and manage supplier risks. Moreover, using AI in this way can help manufacturers derive higher value from their procurement teams.

Smarter sourcing

In a recent survey, Gartner found that 65% of decisions made today are more complex than they were two years ago, involving more stakeholders and choices because they reflect today’s more interconnected and faster-changing business environment. Procurement teams see the impact of this because they must spend considerable time doing the following groundwork before they can make a decision:

  • Getting the right information for various categories
  • Collating information from disparate sources about product categories
  • Correlating data to get a full view of insights, patterns, and trends
  • Gaining visibility about the dynamic external factors (macroeconomic, geopolitical, risks, and other market factors) impacting procurement
  • Blending internal and external data sources to pick the most favorable option.

Furthermore, buyers need to factor in disruptions such as COVID lockdowns in China and the Ukraine war that also impact the supply chain. With the risk landscape constantly evolving, purchase decisions become a long and tedious process that can interfere with agility and supply chain resilience.

Using AI to improve decision-making lets a procurement team correlate supplier information with category intelligence, industry benchmarks, risks, macroeconomic factors, and market indicators and recommend decisions on:

  • Future price revisions of materials and services and the right time to buy
  • The optimal contract duration for the buyer
  • Real-time information on sourcing categories impacted because of disruptions
  • Alternate suppliers present in the market

AI-driven decision-making can help buyers with smarter and more effective sourcing outcomes. Companies can use AI technology to go through tons of data, correlate data, and provide buyers with appropriate decisions to act upon. Such AI-driven decisions insulate buyers from cognitive biases that influence their judgments and opinions. Using AI to improve procurement decisions also frees up time so buyers can focus on strategic sourcing activities.

Managing supplier risk

AI can offer tremendous benefits by identifying and managing risks for suppliers, as well as surfacing the risks earlier in the procurement cycle. Proactive mitigation of risks can build resilience for businesses and avoid operational disruptions. Research that Gartner published in 2019 revealed that 89% of companies had experienced risk to a supplier in the previous five years, most commonly involving the supplier’s capacity, financials, or regulatory compliance. (Of course, the events of the last 2 ½ years have focused the world’s attention on supply chain risks and even elevated them to pop culture memes.)

The traditional ways of managing supplier risk involve calculating risk scores of various indicators in finance, compliance and regulatory, legal, brand, environment, sustainability, and governance. These risk indicators often get collected manually and at times through external agencies to arrive at the required scores. The scores are then typically added to the supplier’s performance score (which also factors in on-time delivery, quality, and pricing competitiveness) to report its total risk exposure.

However, gathering and analyzing these scores manually is a laborious process that can’t keep up with the pace of today’s digital, global business. Post-COVID, organizations need to move towards real-time monitoring of these risk factors.

AI removes the need for human intervention in continuously monitoring risk exposure. It can assess supplier portfolios for various potential risks and propose alternative suppliers to balance the risk exposure of buyers. With AI-driven decision-making, buyers can receive alerts about any unexpected or sudden changes to supplier risk exposure in near real-time. AI can present decisions to the buyers concerning which suppliers to choose and engage strategically and operationally to avoid supplier failures and disruptions.

It’s clear that AI-driven decision-making is the way forward for procurement today to maximize value for their organizations. If you are looking for help you with smarter sourcing decisions & managing supplier risk, we can help you achieve it via our ignio™ Cognitive Procurement cloud platform.

ignio™ Cognitive Procurement product continuously assesses categories, suppliers, and procurement spending patterns and blends this information with external market data to recommend ways of optimizing sourcing and managing supplier risks. To learn more, have a quick look at our product video and brochure