The year of GenAI

As much discussion and deployment of artificial intelligence took place in 2024, 2025 is shaping up to be an even bigger year. This year will likely see the acceleration of AI, and specifically Generative AI, into everyday business functions.

According to Gartner’s ’s 2024 Hype Cycle for Procurement and Sourcing Solutions, rapid adoption and multiple use cases will move GenAI into the “Plateau of Productivity” within two years.

“GenAI can already enhance many different workflows in procurement and 73% of procurement leaders at the start of the year expected to adopt the technology by the end 2024,” said Kaitlynn Sommers, senior director analyst with Gartner’s Supply Chain Practice. “This level of adoption, along with promising use cases, such as contract management, means GenAI 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.”

Gartner’s Hype Cycles are used by clients to identify what level of interest they should have in a technology or solution. There are five levels:

1. Innovation Trigger. This is typically early stage with a focus on a potential technology that is drawing interest.
2. Peak of Inflated Expectations. This is the stage where some success stories emerge, but many failures also take place. Companies may or may not take actions on the technology at this point.
3. Trough of Disillusionment. In this stage, interest wanes as experiments and implementations fail to deliver. Failures of companies producing the technology begin with fewer survivors emerging.
4. Slope of Enlightenment. More case studies emerge and the technology becomes more understood, with next-generation products arriving on the market.
5. Plateau of Productivity. This is the stage where mainstream adoption starts to take off.

Gen AI use cases expand

The past year has seen the number of Gen AI use cases expand, with additional capabilities being added by vendors across the sourcing and procurement landscape, Gartner noted. These include contract management, sourcing and supplier management with additional expected use cases to include supporting supplier performance management, P2P and analytics.

“The window for building competitive advantage through early adoption of GenAI in procurement is narrowing,” said 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 GenAI with their current systems.”

Sommers added that companies should look to launch “targeted use-case pilots” that can help clarify what capabilities are scalable. Also, monitor developments in the market and look for opportunities to leverage Gen AI without the need to build proprietary infrastructure.

Planning and AI

A research paper, Integration of Emerging Technologies AI and ML into Strategic Supply Chain Planning Processes to Enhance Decision-Making and Agility, published in the September 2024 issue of International Journal of Supply Chain Management, concluded that traditional supply chain planning needs to be reinvigorated, and AI and machine learning offer that opportunity.

Authors Jayapal Reddy Vummadi and Krishna Chaitanya Raja Hajarath reviewed literature and industry reports to evaluate the planning environment and the efficiency of current techniques to solve common problems.

“As per the views of Aljohani (2023), the supply chain landscape today faces an increase in the level of complexities and uncertainties; problems like unforeseeable customer demand, fluctuating commodity prices, intensifying political disagreements, and rapid technology changes have rendered the planning methods for supply chains as outdated,” they wrote. “The voice of supply chain managers is to make better and faster decisions and must have the capacity to reallocate resources to environmental changes. Implementing an emerging technology strategy that comprises artificial intelligence (AI), machine learning (ML), and big data analytics is a must to improve strategic decision-making and agility (Leewayhertz.com, 2024).”

They noted steps such as data collection and organization, data preparation and cleaning, and choosing AI algorithm selection as key early steps in integrating AI/ML into planning functions. After choosing the right AI technologies, the authors noted data modeling, system integration, testing and deployment and continuous improvement as important steps to achieving success.

“The MIT Center for Transportation and Logistics study predicts a revenue boost of up to 10% and a cost reduction of up to 5% that will be achieved due to the adoption of decision support of the supply chain based on AI analytic applications by the companies,” they said, adding that these savings may not be enough for companies to invest in advanced technologies before quickly adding that “adopting artificial intelligence (AI), machine learning, and big data analytics into the strategic planning of the supply chain is no longer an option.”

After laying out the challenges, the authors shifted to the limitations that current processes such as Excel forecasting have on a complex supply chain environment. “According to the World Economic Forum data, a typical firm will have more than 5,000 suppliers nowadays; the top 20% account for 80% of these expenses (Younis et al., 2022). Nonetheless, proactively dealing with the intricate blend of variables and dependencies is the core challenge, which conventional planning mechanisms alone cannot cope with,” they wrote.

“Age-old methods not supported by relatively innovative technologies cannot cope with erratic and globalized situations. This territory of outdated and trade-restricting policies is, in fact, the primary threat to building competitiveness,” the authors added.

The paper, while detailing the pros and cons of a series of advanced technologies that can help in this process, concluded that the “adoption of AI, ML, and big data by companies aiming to upgrade the quality of decision-making, flexibility, and speed has a multitude of possibilities. Such technologies help companies to get hold of a nearly boundless amount of data to uncover relevant insights and to ensure the optimal performance of a supply chain in real-time.”

The authors recommend companies embrace innovation, foster adaptability, and invest in talent and training that can use the tools deployed.

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