Head over to our on-demand library to view sessions from VB Transform 2023. Register Here

Inflation and extended supply chain disruptions, among other geopolitical factors, will further complicate the world of supplier sourcing going into 2023. Now more than ever, it’s critical that procurement leaders base their spend management and sourcing strategies on highly accurate data. Doing so is paramount to finding new, lower-cost or diverse suppliers.

When implemented correctly, a diverse set of suppliers improves agility, helps navigate supply chain disruptions, improves brand reputation, facilitates innovation and increases competition. 

That’s where AI comes in. AI and machine learning (ML) unite to provide accurate data, cost-and time-saving processes and opportunities for business agility. Let’s discuss why these business advantages, and, by extension, AI, could be necessary for procurement moving forward.

AI and ML improve data reliability and enrich supplier diversity

Suppliers seldom update their data as often as necessary, which means data housed in manually updated platforms is usually outdated or simply inaccurate. This creates silos and wastes time for both procurement professionals and suppliers.


VB Transform 2023 On-Demand

Did you miss a session from VB Transform 2023? Register to access the on-demand library for all of our featured sessions.


Register Now

Additionally, manually updated procurement software leads to crossed wires. Let’s say, for example, multiple line items from the same supplier are inappropriately attributed to multiple vendors due to human error. This simple and common mistake leads to compliance issues and financial hiccups when accounts payable processes the contract on the back end.

Meanwhile, AI can quickly analyze associated keywords and bring separate spend categories from the same supplier under one umbrella. This untangles the accounts payable and contracts process by providing an accurate view of the supplier-procurement relationship over time. Similarly, AI and ML can work together to prepopulate supplier profiles with more accurate information during the analysis stage.

AI can use accurate data to “handpick” choice suppliers. If economic conditions require a procurement professional to find a lower-cost but equally qualified supplier, AI can provide relevant suggestions. Or, if a procurement leader is searching for an ESG-certified supplier with publicly available sustainability information, AI and ML can sift through many sources quickly to extract such information. This capability offers a far more diversified supplier base. It can also open the door to improved supplier diversity by highlighting small-business enterprises (SBEs), minority-owned enterprises (MBEs) and woman-owned enterprises (WBEs).

Supplier diversity has several mission-critical benefits. For one, a variety of minority-owned suppliers raises competition, thereby improving the quality of products and services. Moreover, supplier diversity provides peace of mind during extended supply chain disruption. With more reliable data sourced and maintained using AI technologies, organizations will be in a better position to shift to an alternate supplier if the primary one falls through. Supplier diversity programs are also essential for many moral and ethical reasons that reflect a company’s core values beyond its supply chain.

Automation mediates risk and restores time

As discussed, AI can identify possible suppliers’ ethical practices and sustainability initiatives. Automation takes risk mediation several steps further without extra leg work. AI-synthesized data can extract a wealth of raw supplier data, including their past and current corporate relationships, associations and length of time in business.

How does this vetting process mediate risk? AI systems constantly re-check supplier information to ensure ESG and diversity principles remain aligned with an organization’s supplier values. So, if a supplier is at risk of falling out of ESG compliance, procurement professionals know about it — and quickly.

Furthermore, contract lifecycle management (CLM) platforms embedded with AI can support the contract process. After auditing negotiations and contract language, advanced CLMs can flag areas of non-compliance to human admins. The “paper” trail created by AI tools also provides a holistic view of a contract, from inception to payment. This alleviates the burden of manual compliance verification, which is often unwieldy, especially with long-standing contracts.

More efficient operations

Industry leaders have expounded at length on the benefits of automation. But it’s important to stress how much time the procurement industry, in particular, stands to gain from orchestration.

Procurement leaders report that it takes an average of five weeks to find a supplier. The bulk of that time is spent sourcing supplier information. That process can be entirely automated using AI, with supplier profiles frequently updated to reflect changes. Consequently, procurement professionals have more time to focus on critical initiatives like spend management and high-level sourcing strategies that align with their company’s mission.

Most importantly, automation is critical for operational efficiency, which became the top business priority for nearly 78% of CPOs this year, according to Deloitte.

AI enables business agility

It makes sense that operational efficiency is top of mind for leaders. Rising inflation has led to layoffs and budget freezes. For the procurement sector, persisting supply chain difficulties only compound those issues. Now, leaders must hone in on strategies that help their teams do more with less while remaining agile to navigate evolving consumer demands and supply scarcities.

And yet most CPOs lack confidence in their organization’s business agility. According to Wakefield research, a majority (51%) of sourcing and procurement leaders say they were unprepared for the supply crises of the last 12 months.

Meanwhile, 50% of CPOs identified by Deloitte as “high-performing” reported at least some degree of AI maturing in their operations. These numbers suggest a stark contrast between procurement teams that have adopted AI — and therefore function at the top of their abilities — and teams that have yet to adopt AI, ML and automation-based solutions.

Improved data accuracy, enriched supplier diversity, risk mitigation and time-saving automated processes all contribute to enhanced business agility. When procurement professionals work with accurate, well-organized data, they can meet unpredictable supply chain demands. New suppliers are far easier and faster to identify. And with the right data platform, the contract process is far more straightforward, which makes pivoting on a dime more doable.

Predictive modeling

Moreover, AI allows procurement leaders to assess new market opportunities quickly. Using ML and sourcing histories, AI tools can provide personalized recommendations and strategic insights for future interactions, indicating opportunities for further cost-savings or business opportunities via new suppliers and regions to source from. And, the increased speed of sourcing via AI allows procurement professionals to contract with those new suppliers without weeks of delay.

Predictive modeling via AI provides an exciting avenue for increased agility. But this capability also demonstrates how AI and ML offer a competitive advantage while facilitating mission-critical tasks. As the procurement space becomes increasingly complex, this leg-up will prove not only valuable but crucial. The question for procurement leaders is: Who will adopt the tools of the future to innovate, and who may ultimately end up falling behind?

Arnold Liwanag is CTO at TealBook.


Welcome to the VentureBeat community!

DataDecisionMakers is where experts, including the technical people doing data work, can share data-related insights and innovation.

If you want to read about cutting-edge ideas and up-to-date information, best practices, and the future of data and data tech, join us at DataDecisionMakers.

You might even consider contributing an article of your own!

Read More From DataDecisionMakers