Beyond the Hype: The Rise of Agentic AI

Artificial intelligence has been making headlines for years, mostly for its flashier tricks: generating text, images or even music. But behind the scenes, a quieter revolution is happening. While generative AI grabs attention for what it can create, another kind of AI is quietly getting things done.

Meet agentic AI – the type of intelligence that not only analyses information but also takes action, autonomously and purposefully, within rules set by humans. Think of it as an AI that knows how to get things done without needing someone to hold its hand.

What is Agentic AI?

At its core, agentic AI refers to systems designed to pursue defined goals with a high degree of autonomy – making decisions, taking actions, and completing tasks end-to-end. When appropriate, these systems can loop in humans for validation in specific scenarios or to inform the next strategic decision.

Unlike traditional AI, which tends to be reactive and rules based, agentic AI adapts dynamically to new data and changing conditions. And while generative AI is all about producing content or insights, agentic AI is action oriented. It is designed to handle entire processes, collaborating with humans to deliver measurable results.

Why this Matters for the Enterprise?

This distinction matters most in the enterprise world, where the value of AI lies not in novelty, but in improving efficiency, reducing risk, and helping people make better decisions.

Take procurement, for example. Agentic AI can operate across the full Source-to-Pay lifecycle, deploying specialised agents to handle supplier evaluation, contract compliance, and purchase order approvals. The result? Not just faster processes, but smarter, more consistent decisions.

Platforms such as JAGGAER One are bringing these capabilities to life. Within the JAGGAER One platform, the JAI intelligent assistant guides users through sourcing and supplier management, surfaces risk insights, and automates routine procurement decisions – all while staying within governance boundaries.

How Agentic AI Works

Agentic AI achieves this level of autonomy through a mix of advanced data analysis, machine learning and continuous feedback loops. By pulling data from multiple sources, it can spot patterns, anticipate outcomes, and take appropriate action. Over time, it refines its decisions based on results and feedback.

But do not think of it as a free agent. Its autonomy operates within human set parameters to ensure alignment with organisational strategy, regulatory requirements, and ethical standards.

Governance, Risk and Compliance: Built-In Boundaries

Those boundaries are crucial. In regulated environments like public sector procurement, agentic AI must comply with frameworks, policies, and risk tolerances. Its recommendations must be explainable and traceable, especially when public funds or sensitive information are involved.

And just like a human worker, the AI is limited by the systems and data it can access. Poor input leads to poor output, no matter how intelligent the agent may seem.

Real-World Impact in Australia

The impact is already visible in Australia. In manufacturing, procurement teams constantly juggle cost, quality, and supply chain resilience. Agentic AI can assess supplier performance, predict inventory needs, and monitor external signals like weather events, strikes, or market shifts. By providing early warnings and automated responses, it enables organisations to move from reactive problem solving to proactive management.

In education, where budgets are tight and accountability is critical, agentic AI makes procurement more transparent and data driven. It can analyse supplier performance, track spending patterns, identify cost saving opportunities, and improve contract management by flagging risks or summarising key terms. This allows institutions to make faster, more informed decisions, directing resources where they matter most.

Public sector organisations are seeing similar benefits. Recent Australian government initiatives have piloted AI tools to automate compliance checks against pre-approved supplier panels and procurement frameworks. These systems reduce manual effort, increase auditability and speed up procurement cycles without compromising governance. Agentic AI also continuously evaluates supplier risk, automates contract reviews, and streamlines routine tasks such as purchase order approvals, helping teams focus on higher value work.

Ethical and Organisational Considerations

Of course, the rise of agentic AI raises ethical and organisational questions. Accountability is central – when an AI driven decision has financial or operational consequences, it is critical to know who is responsible. Transparency, auditability, and governance structures ensure human oversight remains in place.

Workforce impact is another consideration. As AI takes on more complex tasks, organisations must manage change thoughtfully, investing in retraining and redefining roles. When used correctly, agentic AI should augment and expand human expertise rather than replacing it.

Technical Challenges to Address

From a technical perspective, challenges remain. High quality, real-time data is essential, yet many organisations still struggle with fragmented information. Models must balance competing priorities such as cost, risk, and sustainability, while remaining intuitive for users.

Continuous learning is also key, allowing systems to adapt to shifting markets, regulations, and organisational strategies.

Preparing for Adoption

For organisations considering adoption, the first step is ensuring data readiness. Strengthening data management practices, selecting proven technologies, and embedding ethical and scalable AI solutions are all essential.

Finally, success depends on change management: preparing teams for new workflows, and fostering a culture where AI is seen as a partner rather than a mysterious black box.

AI should be viewed less as a replacement for people and more as an enabler that accelerates value creation and improves outcomes. It supports teams by streamlining processes at speed, raising quality, and freeing time for strategic, high-impact work. In doing so, AI is shifting the mindset from “doing more with less” to “doing more with higher quality and greater confidence.”

Agentic AI is about moving from insight to action. For Australian organisations facing complexity, cost pressures, and regulatory demands, responsible adoption offers real world value – improving efficiency, reducing risk, and helping teams make smarter decisions today and in the future.