AI Agents and the Fight for Customer Data

**George Fraser** (0:00)
There is a new reason to have all your data in one place, which is AI agents need context. If you don't do that, then it's sort of like using ChatGBT from before ChatGBT was connected to the Internet. Postgres, contrary to popular belief, is very old technology. It is not a good database simply because it was written a long time ago, it has a lot of technical debt.

**Martin Casado** (0:19)
Asatya has said that there's going to be the collapse of SaaS. Do you think that SaaS apocalypse is a thing and we're going to see a massive shift?

**George Fraser** (0:26)
The bigger threat is that AI native companies will just zoom and catch up to the established incumbents and maybe be better.

**Martin Casado** (0:35)
Like we'll actually have an HR and that HR team will onboard AI as they come, they'll be part of teams, they'll join the Slack. In that world, these aren't software, that's actually more seats, more consumption of software.
Do you think that for enterprise agents we're moving more to these, you treat them like humans or do you think that that's too far?

**SPEAKER_3** (0:55)
For years, companies built data infrastructure to answer questions about the business. Now, they're building it for AI.
As agents become more capable, the challenge is no longer collecting data. It's making sure the right systems can access the right context at the right time. That shift is forcing companies to rethink everything from data platforms and APIs to enterprise software and systems of record. Martin Casado speaks with Fivetran co-founder and CEO, George Fraser, about AI data infrastructure and why the next wave of enterprise software may look very different from the last.

**Martin Casado** (1:35)
So our guest today is George Fraser, who is the CEO of Fivetran. Fivetran announced the merger with dbt. So maybe to start, just give a quick overview of what Fivetran does.

**George Fraser** (1:46)
So Fivetran, we've been around for a while. We've been around since 2013, had customers since 2015

**Martin Casado** (1:52)
2013, 10 years?

**George Fraser** (1:53)
Yeah, exactly. I've been doing this long enough that a slide about the past state in my own slides is the same slide as the future state from when I started. But what Fivetran does is we help our customers get all of their data from all their systems like Salesforce, NetSuite, all their SaaS tools, their own databases into one place.
Getting all your data in one place is not a new thing. Businesses have had the need to do this since filing cabinets. The primary reason historically that people use Fivetran to get all their data in one place was to do business intelligence, was to build reports about things like what's your revenue, what's going on with your sales team, what are we forecasting for this quarter, all those great things. Now, there is a new reason to have all your data in one place, which is if you want to use AI agents in business, AI agents need context. It turns out that the same data foundations that work well for business intelligence and reporting with some additions and some modifications actually can work really well for AI agents as well.

**Martin Casado** (2:55)
Let me talk about a sector of the industry which is under a lot of change because of AI. So maybe could you give a high level overview of how it is evolving? What are some of the considerations about the shifts in data? In particular, we're seeing a lot of changes how vendors view their own data, how the big labs use data. So just talk a bit about what the industry is.

**George Fraser** (3:17)
The thing about data in the context of business is it is always born somewhere else. It's always born in the systems of record like Salesforce, like Workday, like SAP.
And even if it's your own applications data, if you're a software company and you run your own database, the data is born in that database. And since, as I said, at one time in Memorial, businesses have had the need for internal use to centralize a copy of all their data in another location. It doesn't work to just go and do all of your reporting and ask all your questions in each system individually. Some kinds of questions require you to look across the entire system. And so that is not new. However, these AI agents are new. And there has been, in the last year, a reaction which really started with the stock market. As we saw, the SaaS apocalypse happened. And as we saw, the stock prices of all of these systems of record that I'm talking about plummet. People view them as under threat from AIs. We have seen some of these companies start to think that a great strategy for dealing with AI might be to lock it out and to say, your data is our data now, and you can't take it elsewhere. And if you want to use AI on it, you have to use the AI tools that we provide. Notably, just a couple of weeks ago, SAP announced a new API policy that literally said, all AI agent access was banned, except in a way specifically approved by SAP. Now, if you're an SAP user, don't panic.