AI Summary View: Reactive to Proactive.

While recently preparing for the "AI in Procurement" webinar, I tried to step back and consider the broader picture of AI’s developmental streams.

Spoiler alert: (Unfortunately,) it wasn't sponsored by Microsoft. As a business user predestined to utilize the Office 365 suite, I found no better way to natively use existing Microsoft apps spiced with AI.

Diagram illustrating the evolution from Reactive AI to Proactive AI, featuring intersections of Assistive AI, Agentic AI, and Generative AI. It highlights key elements like Robotic Process Automation (RPA) for simple tasks, Intelligent Process Automation (IPA) for process creation, and Autonomous Agents for contextual adaptation. Microsoft Copilot and Power Automate are shown as tools enhancing AI capabilities.

Robotic Process Automation (RPA)

I realized this recently when I started operating Microsoft Power Automate (formerly Microsoft Flow). RPA has been with us for years, nicely packaged and readily available, yet I expected it to come prepackaged with some e-procurement or ERP platforms.

RPA excels at executing simple, structured workflows with military precision: invoice data entry, PO validation, or three-way matching.

There is no learning, no adaptation - just relentless rule-following. There’s immense value in reliability and speed for repetitive tasks, which you can implement without waiting for the "digital transformation".

Intelligent Process Automation (IPA)

Then Microsoft suggested moving up a level to IPA, where task automation gets a pinch of AI. IPA combines RPA with NLP, essential ML, and rule-based decision trees. You’ll see it in chatbots that understand queries or tools that triage supplier emails and route them appropriately. It’s brighter and fancier but still reactive.

Assistive AI

Now, we’re stepping into more complex territory. This stream supports decision-making, often by crunching through volumes of data to present recommendations. Contract risk scoring, supplier evaluation models, or spend forecasting tools fit this space. It doesn’t do much on its own- but it gives us humans sharper tools.

Agentic AI

But what about AI that acts? Agentic AI can plan and execute multi-step tasks. It can take a business goal like “optimize supplier onboarding” and map out a series of steps: gathering data, reaching out, sending reminders, and escalating when stuck.

It’s still semi-dependent but shows initiative. It’s like hiring an intern who reads the policies and procedures.

Generative AI

Then comes the much-hyped Generative AI, which everyone and their dog is trying to embed in their products. Many of us still believe this is the very essence of AI.

It’s creative, learns fast, and creates content - from supplier emails to contract clauses to market reports.

It's a data junky - you have to feed it quality content and manage it with infamous prompts. Once again, the prompt thing is what many people believe they need to study to master AI.

Autonomous Agents

This is the final frontier (by far). Such agents are context-aware, goal-driven, and capable of managing other agents. They escalate and adapt. Think of an AI that not only negotiates terms with a supplier’s bot but also checks compliance and integrates with your ERP. It's a wishful vision, but we’re edging closer with every new release.

The name of the game: Reactive to Proactive

Essentially, the AI journey goes from “tell me what to do” to “I’ll figure it out.”

While Generative gets the limelight, the real workhorses are the agentic and autonomous types, which we need to master soon.

That’s where future value will be created—not from copy-pasting tender templates but systems that navigate ambiguity and act autonomously within governance remits.