Artificial intelligence (AI) is evolving at such a rapid pace that some industries are struggling to keep up — and procurement is apparently no exception.
In a Supply Chain Brain article summarizing the results of a recent study conducted by Digital Procurement World (DPW), Managing Editor Helen Atkinson says there is a “staggering disconnect” between what procurement teams would like to achieve with AI and the reality they experience.
Citing Matthias Gutzmann, the founder of DPW who previewed the study at the company’s DPW Amsterdam 2024 conference in October, Atkinson writes that this disconnect means procurement teams must “seek the insights, technology, and partnerships needed to ‘think and act ten times bigger than their current capacity.’”
Here, we’ll take a quick look at the role of AI in procurement, dig into factors the study indicates may be hindering progress, and ponder whether specialized AI could help move things along.
The role of AI in procurement
In a recent post for AIMultiple Research, principal analyst Cem Dilmegani and Ezgi Alp, PhD describe the importance and benefits of using AI in procurement.
Underscoring the critical role of data for various purposes in procurement, they note that the “vast” amount of both structured and unstructured data involved makes analysis with traditional software a challenge.
“Machine learning models and generative AI are built to process such existing data and derive insights,” Dilmegani and Alp write. “This makes procurement an ideal fit for AI because AI algorithms can provide insights and help companies make better decisions. … Artificial intelligence (AI) can transform procurement from a reactive to a proactive function that generates insights and improves operational efficiency.”
In this context, they list ten common use cases for AI in procurement:
- Contract management: “AI-powered contract management tools unify contract lifecycle management, and contract data extraction. …”
- Supplier risk management: “AI adopts big data methodologies to screen millions of existing data sources, providing alerts on potential risk positions across the supply chain processes. …”
- Spend analysis & classification: “AI-powered spend classification algorithms dynamically search through line-item details and flag keywords to tie to spend categories. …”
- Anomaly detection: “AI can process vast amounts of data to provide real-time updates on anomalies and changes in the operating environment. …”
- Automated compliance: “AI can structure contract, invoice, and purchase order data to automatically identify and highlight non-compliance issues. …”
- Accounts payable (AP) automation: “AI and machine learning automate the AP process, reducing the number of human touches per invoice. …”
- Invoice data extraction: “Generative AI solutions, including computer vision and natural language processing (NLP), automate the extraction of invoice data. …”
- Procurement chatbots: “AI-powered procurement B2B chatbots provide support for procurement queries via text interface. …”
- Strategic sourcing: “AI and machine learning are used to recognize bid sheets and develop specialized category-specific eSourcing bots for raw materials, maintenance, and repairs. …”
- Global sourcing: “AI tools enable businesses to harness market data-driven insights for high-level sourcing strategies. …”
When deployed for use cases like these, Dilmegani and Alp say companies can enjoy five primary benefits of using AI in procurement:
- Enhanced decision making
- Streamlined operations
- Cost savings
- Robust supplier relationship management
- Risk mitigation
DPW’s “10X Procurement” Study
DPW says it “accelerates innovation by connecting startups, tech leaders, major corporations and investors” and believes in the “power of technology to transform business and society, enabling leaders to respond to the most pressing challenges facing corporations today.” DPW Amsterdam, where the study’s results were released, is the company’s annual flagship conference and described as the “most prominent event in procurement and supply chain for innovation leaders worldwide.”
In a summary of study results published by CPO Strategy, post author Harry Menear says findings revealed an anticipated growth in AI adoption of a whopping 187% in the next year — despite the current reality that only 20% of teams are using AI “at scale.”
In a collaboration between DPW and Professor Remko van Hoek from the University of Arkansas, the survey polled over 200 global procurement leaders. Results revealed a “‘staggering disconnect’ between the appetite for digital transformation among procurement teams and their ability to actually execute those transformations,” writes Menear.
“Technology is advancing at the speed of light – but procurement leaders are struggling to drive change at the same rate,” said Gutzmann. “There’s a disconnect between the ambition to transform and the readiness to make it happen.”
Gutzmann is cited as also noting that the 10X Procurement study results underscore the fact that “while procurement is on the brink of something groundbreaking, teams are ill-equipped to harness that potential.”
Key findings as described by Menear include:
- A gaping skills gap: “Procurement technology providers are sounding the alarm on a widening skills gap, citing a 30-35% shortfall in critical capabilities such as change management, openness to AI, and digital acumen, threatening the success of procurement’s digital transformation efforts.”
- A rise in tech adoption, but an “underutilization” that “hampers progress”: “Despite AI making waves across industries, just 20% of respondents are adopting or scaling AI within their procurement functions, and procurement processes remain only 50% automated on average. …”
- An anticipated “digital revolution in procurement” in 2025: “Looking ahead, respondents predict a dramatic 187% increase in AI adoption and scaling in 2025 across procurement processes and tech stacks. …”
- A “culture lag” that negatively impacts digital transformation, despite “clear road maps”: “While many procurement teams boast clear roadmaps for digital transformation, DPW’s report finds that the culture required to embrace and sustain this change remains underdeveloped. …”
- A “new playbook” that prioritizes “agility and innovation” over cost savings: “A large number of respondents were found to put cost savings before other objectives.” However, organizations that prioritize agility and resilience “consistently see better results than their peers,” underscoring the “urgent need for procurement to redefine success metrics and shift away from rigid cost-saving goals toward more innovative, relationship-driven strategies that drive more resilience.”
Could specialized AI help move things along?
In our article last week — “Can Specialized AI Be a Game-Changer for Supply Chain Management?” — we discussed some of the difficulties organizations are facing to make the most of AI and how moving from a general AI framework to one that focuses on solving industry- and company-specific problems can help.
According to AI provider UiPath, specialized AI refers to “artificial intelligence systems that are designed and trained to excel at specific tasks or domains. Unlike general-purpose generative AI tools that aim to replicate human intelligence across a wide range of capabilities, specialized AI focuses on mastering a particular skill or solving a specific problem.”
Within the broader AI landscape, UiPath says specialized AI is a “crucial” piece of the larger AI puzzle: “It’s a practical application of AI that’s already making waves in the real world, solving specific problems and transforming industries.”
UiPath says the magic of specialized AI models is that they work as “precision instruments designed for a specific job.” Part of this capability lies in the fact that they’re built to “take full advantage of a company’s private, internal data and business context” to do so. As a result, they’re often considered part of the organization’s proprietary intellectual property (IP).
The company says specialized AI can be a game-changer for businesses, since it helps:
- Enhance efficiency: “… by focusing on a specific task or domain, specialized AI solutions can outperform their general-purpose AI counterparts in terms of speed and accuracy. …”
- Increase accuracy: “… with supervised or semi-supervised training on relevant data sets, these AI models become incredibly accurate at their designated tasks. …”
- Provide enterprise-specific solutions: “… specialized AI models can be customized to address the unique challenges and opportunities of … specific enterprise and business operations. …”
- Reduce associated costs: “… compared to the costly development of generative AI and large language models, specialized AI solutions can be more budget friendly. …”
Within the challenges procurement teams face to deploy AI effectively, perhaps specialized AI can play a role in helping them embrace the “10X procurement” mindset DPW says will be critical to optimizing success.
