This article originally appeared in the May/June issue of Procurement Pro.
By Simon Thompson, VP Northern Europe, JAEGGAER
In an era where artificial intelligence (AI) is rapidly evolving, reshaping industries worldwide, procurement is emerging as a key area for AI-driven transformation. One of the most promising advancements in this space is Agentic AI. Agentic AI can independently make decisions, execute tasks, continuously improve its performance based on real-time feedback, and adapt to dynamic environments without requiring constant human oversight.
Understanding agentic AI and its impact on procurement
Unlike traditional AI systems, which rely on pre-programmed rules and human intervention, Agentic AI dynamically responds to new data and evolving scenarios. It actively achieves specific objectives through autonomous action and collaboration with human stakeholders. In procurement, Agentic AI could be instrumental in optimising processes across the Source-to-Pay (S2P) cycle by deploying multiple domain-specific AI agents to handle distinct tasks efficiently. Businesses are beginning to integrate Agentic AI to enhance procurement operations, driving efficiency, cost savings, and smarter decision-making across sectors.
In manufacturing, for example, AI can evaluate supplier relationships to ensure reliability and cost effectiveness or predict inventory needs to prevent overstocking or shortages.
Ethical and technical challenges
The benefits that Agentic AI can bring to procurement are undeniable, from increased efficiency to cost savings and improved decision making. However, its adoption is not without challenges, particularly when considering ethical and technical implications. Businesses must navigate these complexities to ensure responsible and effective implementation.
One of the primary ethical concerns surrounding the use of Agentic AI is accountability. Who is responsible when AI-driven decisions lead to unintended consequences? Unlike human decision-makers, AI lacks moral reasoning, making it difficult to assign blame or responsibility. This issue becomes even more pronounced in high-stakes procurement decisions, where errors can have significant financial and reputational repercussions.
Another major concern is job displacement. While automation can streamline procurement processes, it may also reduce the need for human intervention, raising fears of workforce reductions. To address this, it is fundamental to adopt a proactive approach, investing in employee upskilling and creating opportunities for redeployment. By doing so, businesses can harness AI’s benefits while mitigating any possible social impact.
Additionally, transparency is another crucial topic that merits attention. AI systems must operate in a manner that is explainable and aligned with ethical and legal standards, and procurement professionals need to understand how AI reaches decisions to ensure fairness and maintain trust. If AI is perceived as a ‘black box’ making arbitrary choices, its adoption could face resistance.
Beyond ethical concerns, there are significant technical hurdles that businesses must overcome. The quality of data is paramount, as AI systems rely on accurate, real-time data from diverse sources to function effectively. Poor data quality can lead to flawed decisions, undermining the very efficiencies AI aims to deliver. Therefore, businesses must prioritise data integrity and governance.
Moreover, AI decision-making models must account for multiple, often conflicting factors such as cost, quality, and risk. Developing sophisticated algorithms capable of balancing these elements is a complex task, requiring continual refinement and testing.
Finally, AI’s ability to learn and adapt is critical. The procurement landscape is constantly evolving due to market fluctuations, regulatory changes, and external disruptions. For AI to remain effective, it must be capable of continuous learning and adaptation. This requires ongoing monitoring as well as cycles of updates and improvements, adding another layer of complexity to its implementation.
Implementing Agentic AI in procurement
As procurement teams increasingly turn to Agentic AI, the challenge is not just about implementation but about doing so responsibly. These goals cannot be achieved without a structured approach:
- Prioritise data management – conducting thorough audits to identify inconsistencies and establishing clear protocols for accurate, real-time data collection can help build a solid foundation for AI-driven decision-making. Without reliable data, even the most advanced AI systems risk producing flawed outcomes
- Select the right technology – carefully select AI solutions that align with strategic objectives, ensuring they are scalable and can integrate seamlessly with existing procurement systems. Adopting AI without considering its compatibility with current infrastructure can lead to inefficiencies and increased operational risks
- Prepare for change – employees need proper training to work alongside AI, and workflows must be refined to optimise efficiency. More importantly, organisations should cultivate a culture that embraces innovation rather than fears it. Resistance to AI adoption often stems from uncertainty, and a well-managed change process can mitigate these concerns
Agentic AI represents a transformative shift in procurement, enabling businesses to automate complex processes, enhance decision-making, and drive greater efficiency. However, successful adoption requires a balance between technological advancement, ethical considerations, and strategic implementation. As organisations embrace Agentic AI, they stand to gain a competitive edge in an increasingly dynamic procurement landscape.
