Let’s explore the distinction and why you should have a clear understanding of it, because it matters a lot to today’s businesses.
Here we go.
I guess by this point we’re all fairly familiar with ‘Individual AI’, from using something like ChatGPT, Claude, Perplexity AI, Google Gemini or Microsoft Copilot.
We can think of these as essentially being point solutions. They help you, as an individual, to complete a discrete task, right now. One person, one task.
As to what triggers your use of Individual AI, it’s whenever you need it and whenever it occurs to you to use it. Hopefully it helps you deliver a better, faster output. The overall impact is to make you more productive - there’s a personal productivity gain.
Defining Enterprise AI
When it comes to Enterprise AI - this is about helping everyone complete their work, in the same optimized way, every time. So, the scope is not just you, it’s your entire team or maybe an entire workflow.
And it’s not used as and when you think of it. Instead, it’s embedded into the systems you’re using, it’s part of the process, so it’s the default mode to use Enterprise AI.
Then the output is work that’s repeatable, measurable and auditable.
And the impact is operational transformation at scale, which I’ll break down in more detail below. But, in a nutshell: Individual AI makes you faster, and Enterprise AI makes your business faster.
In the context of Legal, I like to say: “It’s the practice of law versus the business of law.”
The other important thing to say about the difference between Individual AI and Enterprise AI is to do with knowledge.
The danger with Individual AI – a big one – is that people acquire knowledge, but it likely sits only in their heads, or it’s maybe kept in some random system not known to, or sanctioned by, the organization, potentially creating security concerns.
Worse still, when knowledge is in people’s heads, it can create continuity issues when they leave the organization because that knowledge then walks out the door with them.
With Enterprise AI, knowledge is captured and codified into playbooks that survive people leaving the business.
How does Enterprise AI benefit in-house legal teams? Simply because it boosts productivity and reduces risk.
For instance, work can be streamlined in a centralized task and reminder system. Better contracts can be drafted more quickly; recurring questions can be answered faster and with more consistency; large volumes of documents can be reviewed more quickly and more thoroughly.
With the visibility and agility conferred by real-time reporting and dashboards, teams can be managed more efficiently, bottlenecks eliminated, and workloads balanced across teams, helping to banish the dangers of burn-out.
In respect of risk, robust data security and systems’ integration mean that governance is better controlled, exposure is minimized more successfully, and costs on things like compliance or reliance on outside counsel, can be reduced.
As much as Individual AI offers significant personal productivity improvements, it’s not making the most of the technology. Meanwhile, Enterprise AI can yield consistent, reliable, accruing benefits.
You could say that the difference between Individual AI and Enterprise AI lies in the degree of impact. And when you understand that difference, you’re on the road to reducing cost, reducing risk and increasing revenue. Which is worth knowing.