Did you miss Ardent Partners’ recent webinar, Evolution of AI in Procurement: The Agentic Age, that shed light on how artificial Intelligence (AI) has come a long way — from basic rule-based systems to today’s advanced models that can learn, reason, solve problems, and even create.
The session featured industry experts from Ardent Partners, as well as Fairmarkit’s CEO, Kevin Frechette, and Coca-Cola Europacific Partners’ Director – Procurement CoE, Anthony Breach, who explored artificial intelligence in procurement and its evolution from generative AI to Agentic AI that actively pursue objectives and take meaningful action in the real world.
In this four-part (+ bonus article) series, we feature articles based on the webcast discussion as well as this link to the full event.
Today, in Part Five (Bonus Article), we explore how artificial intelligence is evolving rapidly, with generative AI already delivering measurable business value and agentic AI poised to accelerate adoption by autonomously executing tasks and decisions. This shift represents a major change in how humans interact with technology, moving from structured interfaces to natural, conversational systems that deeply integrate into daily tools and workflows. As businesses navigate cultural and technological challenges, a phased adoption approach will empower employees to become co-creators of AI agents, positioning early adopters for significant competitive advantage in the coming years.
The Evolution of AI in Business: From Generative to Agentic and the Road Ahead
Artificial intelligence is advancing at a pace few predicted, and businesses are only beginning to grasp the transformative power it holds. One of the best pieces of advice for anyone curious about AI’s potential is simple: just start experimenting. Whether you’re using ChatGPT in its basic form or exploring more advanced implementations, the real learning begins through hands-on, interactive conversations. For many first-timers, it’s a mind-expanding moment when they realize just how capable these models already are.
AI Value Arriving Sooner Rather Than Later
During discussions about AI adoption, a recurring framework often comes up: Amara’s Law, which reminds us that people tend to overestimate the impact of new technologies in the short term, while underestimating their long-term significance. When applied to the current state of Generative AI (GenAI) and Agentic AI, this perspective provides a useful lens for understanding where we are and where we’re heading.
Right now, GenAI is moving beyond early experimentation and beginning to deliver tangible business value, albeit in pockets rather than across entire enterprises. If the current trajectory holds, 2025 will mark the point where organizations begin to see measurable ROI from GenAI. Mass adoption, however, is expected to take hold around 2026. Agentic AI — the next evolution, where autonomous agents not only process information but also take action — is still largely in the experimental phase. Yet, the consensus is that its path to business value might be even shorter, with meaningful contributions arriving as early as 2026.
Looking beyond the hype cycle, the integration of AI, and particularly large language models, into daily life is poised to happen much faster than with past technological trends. The vision isn’t necessarily that every individual will sit and interact with ChatGPT all day, but rather that AI will become so embedded in the systems around us that it becomes invisible: in cars, televisions, appliances — even fridges, much like smart devices already have. The normalization of this technology will happen not because people consciously adopt it, but because it will simply become part of the everyday tools they already use.
Changing Interfaces of AI
One critical element accelerating this shift is the move away from traditional interfaces. Historically, humans have had to interact with machines through structured commands or rigid software systems. Agentic AI changes the game entirely by enabling natural conversations, where the system understands intent, executes tasks autonomously, and even self-corrects to provide better answers over time. The difference is more than just convenience — it represents a fundamental leap in how humans and technology collaborate.
Already, some companies are piloting systems where agentic AI takes on roles traditionally filled by humans, such as strategic procurement analysis and consultancy. In these cases, agents do the heavy lifting of data gathering and initial insight generation, freeing human teams to focus on higher-order decision-making. As these solutions mature, what is today internal experimentation will soon become commercialized products, reshaping industries at a pace few are fully prepared for.
A key point is the distinction between GenAI and agentic AI. The two are not interchangeable, although they often work together. Generative AI focuses on creating content or answers based on prompts, while Agentic AI takes it a step further, acting on those outputs and executing workflows, decisions, and actions autonomously. When combined, these systems can be force multipliers for each other, providing an experience and outcome far beyond what either can achieve alone.
At Fairmarkit, for example, the vision for the agentic layer is clear: a three-tiered structure where users interact with a network of agents, and those agents, in turn, leverage GenAI and the platform’s underlying technology to execute tasks. Each layer — end users, digital agents, and the technology backbone — plays a distinct role, and together they create a seamless ecosystem for procurement and beyond.
The Future Is a Phased Approach
One of the challenges businesses face isn’t just technological but cultural. The rapid emergence of AI capabilities has outpaced organizational readiness. Many leadership teams are still grappling with how to integrate AI into their strategies, and frontline employees, while often the most aware of day-to-day workflows, may hesitate to embrace agents out of concern for job security. It’s an understandable fear, but history has shown that technology tends not to eliminate jobs wholesale — it reshapes them. As with the advent of computers, AI will automate repetitive tasks, allowing humans to focus on creative, strategic, and interpersonal work.
The path forward will likely involve a phased approach: initial deployment led by business units in collaboration with tech teams, followed by increasing democratization as employees grow more comfortable building and refining agents themselves. Over the next 12 to 24 months, expect to see the beginning of this transition, as more frontline workers shift from being users to co-creators of AI agents.
Ultimately, the evolution from Generative to Agentic AI marks more than just another step in digital transformation. It signals a paradigm shift in how organizations operate, how individuals interact with technology, and how value is created. The companies and individuals who embrace this change early — not just in theory, but in practice — stand to gain a significant competitive edge as this new era of AI-driven collaboration takes hold.
