Not All AI Is Created Equal: A Practical Overview

In the rush of headlines and hype, it’s easy to lump everything under one convenient label: “AI.” It’s a tidy term, but one that’s starting to outgrow its usefulness. Because not all AI is created equal.

This isn’t a deep-dive or technical explainer—it’s a plain-language overview to help clarify the kinds of AI systems we’re seeing today, the ones just over the horizon, and the ones still a way off. From the smart tools many of us already use, to the emerging world of autonomous agents, to the still-theoretical promise of Artificial General Intelligence (AGI), understanding these differences matters.

Why? Because the type of AI in use isn’t just about capability—it affects how we interact with it, how we govern it, and how we prepare for what’s next.

AI – The Helpful Tools We Already Use

Let’s start with what most people already know. Today’s AI is excellent at doing specific tasks. It summarises documents, filters spam, translates text, flags suspicious activity, recommends what to watch next. These systems work by recognising patterns and responding to prompts.

They’re reactive, not proactive. They don’t have goals or opinions. They don’t operate independently. Think of them as incredibly clever tools—specialised, efficient, and increasingly indispensable—but still tools. They wait for instructions.

Agentic AI – The Systems That Act

Agentic AI changes the story. These are systems that are designed not just to respond, but to take initiative, pursue goals, and act independently within a defined scope.

If today’s AI is a calculator, Agentic AI is a project coordinator. It can:

• Spot scheduling conflicts

• Rebook your calendar

• Send reminders

• Take follow-up actions

…without being prompted to do so.

Agentic AI is no longer just a helpful assistant. It’s an actor. That autonomy brings opportunity—more automation, less micro-management—but it also introduces new challenges. These systems can act in unexpected ways. They need boundaries, governance, and clear accountability, especially when their decisions impact real-world outcomes.

AGI – The Still-Theoretical Leap

Further down the track is AGI—Artificial General Intelligence.

This is still theoretical, but it’s where things get really interesting. AGI would be able to understand, learn, and reason across multiple domains—just like a human (and potentially, much faster than one). It wouldn’t need to be trained for each new task. It would generalise. It might draft a contract, debate its terms, suggest improvements, and understand the broader commercial context—without switching models or calling in human help.

AGI doesn’t exist yet. But the conversation about it is already shaping decisions today—from regulation to risk planning to infrastructure.

Why This Matters for the Legal Industry

For the legal profession, these distinctions aren’t just theoretical—they’re practical and increasingly urgent.

So far, traditional AI has brought useful efficiencies:

• Faster document review

• Quicker legal research

• Automated time tracking

• Streamlined drafting

All good. But the lawyer is still firmly in the driver’s seat.

With Agentic AI, that begins to change.

Imagine a system that:

• Observes your workday

• Builds your timesheet from meetings, documents, and emails

• Drafts follow-ups

• Flags billing gaps

• Notifies the finance team—without being asked

Or one that:

• Notices a matter has stalled

• Prompts the responsible party

• Suggests the next step

• Escalates if needed

We’re already seeing:

• Matter Progress Agents

• Knowledge Management Agents

• Compliance Monitors

• Client Interaction Bots

• Resourcing Assistants

These aren’t just automations. They’re systems that behave like team members—coordinating, initiating, and learning.

That’s a big shift. And it means law firms need to think not just about efficiency—but about oversight, accountability, and cultural impact.

And What About AGI?

If Agentic AI reshapes daily workflows, AGI would reshape the entire profession.

An AGI might:

• Understand a complex brief

• Interpret jurisdictional nuance

• Simulate litigation strategy

• Draft and negotiate documents

• Predict outcomes

• Generate entirely new legal arguments

It might do all this faster and more consistently than any human team. That kind of capability—if it ever arrives—raises serious questions:

• Can a machine hold legal privilege?

• Who is liable for its decisions?

• Do clients want “human” lawyers, or just high-quality outcomes?

• What happens to professional identity when AI understands the law?

These aren’t questions we need to answer today—but they are questions worth preparing for.

Looking Ahead

We’re moving from smart tools to intelligent agents—and possibly, one day, to reasoning machines.

Every step along this spectrum changes what it means to work, to advise, and to decide. For law firms, this isn’t about being first. It’s about being thoughtful.

• Understand the difference between tools, agents, and general intelligence

• Define clear boundaries for autonomy

• Equip teams to collaborate with AI, not just click buttons

• Align technology use with values, ethics, and accountability

Because in a world where intelligence is no longer uniquely human, it’s the clarity of your intent, and the integrity of your systems, that will matter most.