While some early adopters of Generative AI (GenAI) for procurement are seeing benefits, many organizations are experiencing uneven ROI or falling short of expectations, according to Gartner.
Gartner has characterized this sentiment as having entered the trough of disillusionment
“GenAI is proving to deliver process efficiency, better data insights, and cost savings for procurement organizations,” said Kaitlynn Sommers, senior director analyst in Gartner's supply chain practice, in a statement.
“However, fragmented and low-quality data across procurement systems can hinder accurate outputs, and integrating stand-alone GenAI solutions with existing platforms is often complex, due to differing technical specifications. Despite these challenges, its applicability across the source-to-pay spectrum continues to drive strong interest and adoption." Sommers added.
The company offers a solution called Hype Cycle for Procurement & Sourcing solutions, which is a graphical depiction of a common pattern that arises with each new technology or other innovation through five phases of maturity and adoption. Chief procurement officers (CPOs) can use this research to find technology solutions that meet their needs.
Additional procurement technologies in the trough of disillusionment, where interest wanes after surpassing the peak of inflated expectation, include: sustainable procurement applications, prescriptive analytics, supplier diversity solutions and advanced contract analytics, with conversational AI in procurement now projected to become obsolete before reaching productivity
GenAI for Procurement: Applications
GenAI-enabled procurement applications will focus on automating time-consuming, repetitive tasks such as knowledge discovery, summarization, contextualization, workflow, and execution. As these tools are adopted, procurement organizations can expect to boost productivity and efficiency, reduce operational costs, and free up staff to focus on higher-value activities like strategic decision making and supplier management.
Text-to-process and workflow automation are emerging as common use cases for GenAI in procurement, enabling users to generate workflows or instruct agents using natural language. These capabilities support tasks such as automating contract management, project scoping, supplier recommendations, and autogenerating “Request For” documents (RFx). GenAI offers the potential for significant cost savings while maintaining or even improving output quality, and early adopters are positioned to gain a strategic edge over competitors.
GenAI for Procurement: Adoption Obstacles
Organizations face several obstacles in adopting GenAI for procurement, including fragmented and low-quality data, job security concerns, skepticism about AI-driven insights, and resistance to change. High and unpredictable costs, complex integration with existing systems, and emerging regulatory requirements further complicate adoption. Unclear regulations also raise concerns around privacy, intellectual property protection, and trust.
“Organizations that delay action on integrating GenAI into procurement processes risk falling behind as early adopters overcome these challenges and realize tangible benefits,” said Sommers. “Gartner projects that GenAI for procurement will become a fully productive technology within five years.”
CPOs seeking to integrate GenAI into their operations should:
- Invest in data infrastructure to standardize and integrate information across procurement systems for more reliable insights.
- Explore vendors offering embedded GenAI capabilities and assess how these solutions align with enterprise strategies and desired business outcomes.
- Evaluate process-specific AI tools for areas such as sourcing, contract management, and supplier risk where early adopters are seeing benefits.
- Prioritize change management by encouraging learning and adaptation of procurement processes using data insights and automation.
- Monitor evolving regulations to ensure compliant implementation and seek expert guidance as needed.
- Upskill teams in digital dexterity, human-machine interaction, and prompt engineering to prepare for more AI-enabled processes.
