Gartner: Gen AI for Procurement Enters Next Phase

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Kaitlynn Sommers, Senior Director Analyst with Gartner’s Supply Chain Practice
Consulting giant Gartner claims generative AI for procurement has reached the ‘peak of inflated expectations’ – the second of five phases in its Hype Cycle

Gartner’s Hype Cycle has become a useful tool for clients looking to understand the promise of an emerging technology within the context of their industry and individual appetite for risk.

It provides a graphic representation of the maturity and adoption of technologies and applications, as well as how they are potentially relevant to solving real business problems and exploiting new opportunities.

Now, in its latest report, the 2024 Hype Cycle for Procurement and Sourcing Solutions, the consulting giant claims generative AI for procurement has reached the second of five phases in the Hype Cycle, the ‘peak of inflated expectations’ – moving on from the initial ‘innovation trigger’ phase. 

Describing the peak of inflated expectations, Gartner says: “Early publicity produces a number of success stories – often accompanied by scores of failures. Some companies take action; many do not.”

The firm goes on to predict that rapid adoption and a multitude of credible use cases will quickly move GenAI to the fifth phase of the cycle, ‘plateau of productivity’, within two years.

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How does the Gartner Hype Cycle work?

Gartner’s Hype Cycle drills down in the five key phases of a technology’s life cycle.

These phases are as follows:

  • Innovation trigger: A potential technology breakthrough kicks things off. Early proof-of-concept stories and media interest trigger significant publicity. Often no usable products exist and commercial viability is unproven.
  • Peak of inflated expectations: Early publicity produces a number of success stories – often accompanied by scores of failures. Some companies take action; many do not.
  • Trough of disillusionment: Interest wanes as experiments and implementations fail to deliver. Producers of the technology shake out or fail. Investments continue only if the surviving providers improve their products to the satisfaction of early adopters.
  • Slope of enlightenment: More instances of how the technology can benefit the enterprise start to crystalise and become more widely understood. Second- and third-generation products appear from technology providers. More enterprises fund pilots; conservative companies remain cautious.
  • Plateau of productivity: Mainstream adoption starts to take off. Criteria for assessing provider viability are more clearly defined. The technology's broad market applicability and relevance are clearly paying off.

Gen AI moving faster than most tech

Gartner’s latest Hype Cycle for Procurement and Sourcing Solutions evaluated more than 20 technologies impacting the procurement landscape in 2024.

Researchers discovered that many core procurement technologies were continuing to mature and increase in adoption across organisations. 

Gartner has unveiled its 2024 Hype Cycle for Procurement and Sourcing Solutions

Meanwhile, a set of emerging, AI-driven technologies neared the peak of inflated expectations, including Gen AI, autonomous sourcing, predictive analytics and conversational AI. 

“Gen AI can already enhance many different workflows in procurement,” explains Kaitlynn Sommers, Senior Director Analyst with Gartner’s Supply Chain Practice. “At the start of the year, 73% of procurement leaders expected to adopt the technology by the end 2024.

"This level of adoption, along with promising use cases such as contract management, means Gen AI will rapidly move through the Hype Cycle and reach the plateau of productivity at a faster rate than is typical for most emerging technologies in procurement.”

Window for competitive advantage 'narrowing'

Over the past 12 months, Gen AI use-case availability has expanded greatly, with even more capabilities being added by vendors across the sourcing and procurement landscape every month. 

Early prominent use cases include contract management, sourcing and supplier management. Additional anticipated use cases include supporting supplier performance management, P2P and analytics.

What’s more, procurement technology vendors are integrating third-party large language models (LLMs) to provide more affordable access to Gen AI capabilities in line with digital process support. These LLMs can adapt to provide recommendations and support based on each organisation’s data and an individual’s procurement role, such as category manager or buyer. 

“The window for building competitive advantage through early adoption of Gen AI in procurement is narrowing,” adds Sommers. “Despite this, procurement technology leaders should remain aware of the obstacles to successful implementations, notably in the areas of data quality and integration of Gen AI with their current systems.”

Sommers recommends that the best way to solve these challenges is to launch targeted use-case pilots that will help clarify what capabilities can be scaled, while also monitoring developments from current vendors for opportunities to utilise GenAI without the need to build proprietary infrastructure from scratch.

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