An efficient business-to-business (B2B) procurement process can separate the wheat from the chaff. Procurement has the potential to become a strategic lever for value creation and protection, going beyond traditional cost, quality and delivery metrics.
With the news that Feenix.ai has integrated with Buy with AWS, offering custom storefronts for streamlined software procurement, simplifying AWS Marketplace purchases for B2B customers, savvy B2B executives are wising up to the idea that artificial intelligence (AI) is emerging as a force in procurement.
AI’s ability to process vast amounts of data and identify patterns has reshaped procurement processes. From supplier selection and demand forecasting to dynamic pricing and risk management, AI tools are helping procurement teams make faster, smarter decisions.
Yet, for all its promise, one critical factor can be overlooked by firms rushing to slap the latest technology on their procurement workflows: data quality.
Without clean, accurate and well-organized data, even the most advanced AI systems are like race cars with flat tires — loaded with potential but incapable of achieving peak performance. AI is only as good as the data it ingests, and if data is incomplete or outdated, AI outputs will reflect those flaws, resulting in bad decisions or missed opportunities.
Companies that treat their data as a strategic asset — not just a byproduct of operations — may be the ones best positioned to realize AI’s potential.
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Unlike business-to-consumer environments, where data is often standardized and centralized, B2B procurement is more fragmented. Companies deal with a vast number of suppliers, each with its own data formats, pricing structures, and compliance requirements.
Data silos, manual entry errors, and inconsistent recordkeeping compound the problem, creating a patchwork of unreliable information. Errors in supplier data, for example, can lead to overpayments, duplicate orders, or missed contract obligations — inefficiencies AI could exacerbate if left unchecked.
“To truly unlock the power of AI, especially in a B2B world, you really need to have tremendous amounts of real-world business data to train the AI,” Rajiv Ramachandran, senior VP product strategy and management at Coupa, told PYMNTS.
Another problem is scalability. As datasets grow in size and complexity, AI systems must be able to process and analyze data efficiently without performance degradation. The relationship between AI and data quality is symbiotic. High-quality data enhances the accuracy, speed and reliability of AI algorithms, allowing B2B firms to realize benefits such as enhanced supplier management, accurate spend analysis, and even risk mitigation.
The payoff of prioritizing data quality is significant. A firm that invests in data accuracy can unlock the full potential of AI, leading to streamlined operations, better supplier relationships, and a healthier bottom line. Moreover, high-quality data can make it easier to adapt to emerging technologies and shifting markets.
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AI-driven insights can identify the best suppliers based on performance metrics, sustainability practices, or pricing trends — if the input data is reliable. And by combining internal data with market intelligence, AI can help forecast commodity prices, enabling negotiation and mitigation strategies.
AI and other technologies can reduce the time spent on product selection and quoting by up to 80%, Forest Flager, CEO and co-founder at Parspec, told PYMNTS. This efficiency improvement enables distributors to respond faster, interact with more buyers, and make better recommendations for projects.
Per the study “Digital Payments: Modernizing Procurement Processes,” a PYMNTS and Corcentric collaboration, 31% of retailers are investing in these procurement systems, with an additional 53% planning to do so. Similarly, 42% of manufacturers have already initiated upgrades to their procurement technology, and 44% are in the process of doing the same.
“By harnessing AI, [companies] can rapidly consolidate, classify, and categorize vast amounts of spend data, offering insights that were previously difficult or impossible to obtain through traditional methods,” Nitin Upadhyay, the chief data and innovation officer at RobobAI, told PYMNTS.
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