From AI Adoption to Real Business Value
This week I had the opportunity to attend Enterprise AI at Microsoft, an event held at Microsoft’s campus in Mountain View and organized by Open Future Forum.
After listening to the different sessions and discussions, I left with four main takeaways:
- AI adoption is not the goal. Business impact is.
- Don’t just add AI to existing processes. Rethink them.
- Good AI needs good data and business context.
- AI agents need permissions and governance.
For me, these four points summarize an important change in the Enterprise AI conversation: we are moving from experimenting with what AI can do to understanding how to create real, measurable and secure business value with it.
1. AI adoption is not the goal. Business impact is.
One of the ideas that stayed with me was how we measure AI success.
It’s easy to measure adoption: licenses, active users, prompts or usage. But none of these necessarily tell us if the business is actually improving.
I think the questions should be much simpler:
Are we doing something faster? Are we improving quality? Are we reducing repetitive work? Are we making better decisions?
AI adoption can be a useful indicator, but it shouldn’t be the final KPI. The real measure of success should be the impact on the business.
2. Don’t just add AI to existing processes. Rethink them.
One of the easiest ways to introduce AI is to take an existing process and add an AI tool somewhere in the middle.
That can help, but it can also mean using new technology to make an old process slightly faster.
A question from the session that I found much more interesting was:
If we designed this process today, knowing what AI can do, would we design it in the same way?
Maybe some steps can disappear. Maybe information can be prepared automatically. Maybe people only need to intervene when judgment or a decision is required.
For me, this is where AI starts becoming business transformation rather than just another productivity tool.
3. Good AI needs good data and business context.
This was also a good reminder that AI doesn’t magically solve data problems.
In fact, it can make them more visible.
An AI agent needs to understand more than where the data is stored. It needs to understand what that data means: what is revenue, which customer information is correct, how different datasets are related, or which business rules should be applied.
This reinforced something I see in my day-to-day work: projects like Master Data, data governance or common business definitions may not sound like AI projects, but they are quickly becoming part of the AI foundation.
The better the context we give AI, the more useful and reliable its answers can become.
4. AI agents need permissions and governance.
This was probably one of the most practical takeaways for me.
As we move from AI assistants that answer questions to AI agents that can access systems and perform actions, identity and permissions become critical.
A simple example discussed during the session explains it very well.
Imagine a Sales Manager and someone from Finance asking the same AI agent the same question. If the Sales Manager doesn’t have permission to access compensation data, the agent shouldn’t reveal it. A Finance user with the appropriate permissions may receive a different answer.
The principle is simple:
AI should inherit permissions, not bypass them.
And when agents start performing actions, we also need traceability. We should know who requested an action, which agent performed it, what information it accessed, whether it was authorized and what happened afterwards.
Permissions, identity and auditability shouldn’t be something we think about after deploying AI agents. They need to be part of the architecture from the beginning.
From AI experimentation to AI transformation
My main reflection after the event is that the Enterprise AI conversation is becoming more mature.
The interesting questions are no longer only about what AI can do.
They are becoming:
Where does AI really create value?
Which processes should we redesign?
Is our data ready?
Who should be allowed to access what?
How are we going to measure the result?
And there was one simple exercise from the session that I particularly liked.
Ask every member of the leadership team independently:
“In one sentence, what would AI success look like for our business?”
Then compare the answers.
Before talking about models, agents or technology, we need to agree on what we are actually trying to achieve.
That, for me, was the biggest takeaway from the session:
Enterprise AI is becoming less about AI itself and much more about business transformation.
