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Keith McFarlane is the chief technology officer at Palo Alto, California-based Globality<\/a>, a provider of artificial intelligence-enabled procurement automation software.\u00a0Views are the author\u2019s own.<\/p>\n<\/div>\n With the rise of agentic\u00a0artificial intelligence, I believe we\u2019re heading for a golden age in procurement. <\/span><\/span><\/p>\n Research by HFS shows that AI-driven sourcing already delivers 20% cost savings<\/a>, making it one of the lowest-risk, highest-reward applications of AI for enterprises. <\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n Agentic AI represents a giant leap forward, helping procurement intelligently select high-value suppliers, proactively manage risk, and customize buying for different product categories and markets. This is particularly important now, as business leaders focus on optimizing both short-term profitability and sustainable business value in a turbulent economy.<\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n While the \u201cage of agents\u201d concept is gaining traction, its origins date back decades. Early AI research in the mid-1950s<\/span> led to innovations like Shakey the Robot<\/span>, a 1960s system demonstrating basic decision-making. These laid the foundation<\/span> for intelligent agents in robotics and automation.<\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n Advancements in autonomous computing have since enabled systems like the Mars Perseverance Rover and AlphaGo. Now, generative agents, powered by large language models, are not just learning but actively executing complex business tasks with remarkable efficiency.<\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n It\u2019s <\/span><\/span><\/span><\/span>important to first distinguish agentic AI from other forms of automation. True agentic systems possess several defining characteristics: they are goal-oriented, autonomous, adaptive, and interactive. This means they operate with a clear purpose, make independent decisions within defined constraints, adjust to evolving environments, and communicate effectively with users and other systems.<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n In contrast, traditional bots, narrowly focused AI applications, and early workflow automation tools like first-wave robotic process automation do not necessarily qualify as agentic AI. While they may perform useful tasks, they lack the dynamic decision-making, contextual awareness, and adaptability that defines true agency.<\/p>\n For an AI system to be truly agentic, it must deeply understand its environment. This means perceiving external conditions, processing contextual data, and dynamically determining the best courses of action in real time. Beyond reactive responses, agentic AI actively sets and pursues goals, ensuring its actions align with broader strategic objectives.<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n Memory is also critical. An agent must retain past interactions and experiences to refine its decision-making over time. By leveraging memory, it can continuously improve its performance, adapt to changing circumstances, and enhance its ability to serve users effectively.<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n Beyond reasoning and decision-making, agentic AI interacts with the world through various input and output mechanisms. This can include processing chat-based queries, analyzing sensor data, responding to time-based triggers, or accessing external services via a<\/span><\/span><\/span><\/span><\/span><\/span><\/span>pplication programming interfaces, or APIs. <\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n The ability to take meaningful actions, whether engaging in conversation, setting reminders, or initiating workflows, is what distinguishes truly agentic AI from traditional automation.<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n Procurement is an ideal use case for agentic<\/span><\/span><\/span><\/span><\/span> AI because it\u2019s inherently complex, data-driven and prone to human error.\u00a0No procurement team has enough people to engage every business stakeholder as quickly, easily and effectively as the CFO and CEO demand. The complexity is too great, and time is too limited.\u00a0<\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n Agentic AI can transform procurement in many ways, including supplier discovery, benchmarking, proposal analysis and negotiation.<\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n Negotiation can be particularly tricky in that it requires balancing price, trends, scope, timeline, talent and methodology. Some excel at it, while others avoid it. An agent can evaluate proposals holistically, provide tailored negotiation strategies, and suggest concrete actions based on all relevant data.<\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n What sets agentic AI apart from previous technological innovations that have impacted procurement is its ability to autonomously conduct multi-round negotiations, adhering to predefined rules. This ensures the best possible outcomes without sacrificing control or flexibility.<\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n<\/p><\/div>\n\u2018Age of a<\/span><\/span><\/span><\/span><\/span><\/span>gents\u2019 becoming reality<\/span><\/span><\/span><\/span><\/span><\/span><\/h3>\n
Agents vs. traditional bots<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/h3>\n
Benefits for procurement<\/span><\/span><\/span><\/span><\/span><\/span><\/h3>\n