From building stacks to building impact
AI is everywhere... except in production
Let’s take a moment to appreciate just how far data has come. Twenty years ago, “self-serve analytics” meant someone emailed you a spreadsheet (and sometimes it still does ;) ). Since then, we’ve leapt into BI tools, the cloud and modern data stack, machine learning and predictive techniques, and now we’re co-piloting our analytics with generative AI. Kinda wild, right?!
And yet… for all the excitement (and vendor hype) around AI, the impact so far has been pretty limited.
We sent this survey to over 2,000 data leaders — from early-stage startups to global enterprises — and while most of them said they’re excited about AI, most leaders told us they’re still underwhelmed by the tools available, and few have seen AI drive meaningful change in their workflows just yet.
Illustration by Hex
Meanwhile, data teams are still growing, and self-serve remains a goal, not a reality. Leaders agree that AI could help, but no one we spoke with has seen it working well at scale. IC business analysts have transformed into critical data teams, and in 2025 they’re being asked to do more than just manage tools: they’re expected to drive real business impact. This report explores what’s standing in the way and where forward-thinking teams are focusing their energy next.
Data teams are still bogged down supporting data trust
As data leaders are shifting to support the company’s biggest initiatives and prepare for AI, it makes sense that data trust is the area where the majority of people are spending their time.
We’ve all been there — you share your perfectly crafted report with the business stakeholder and they say “I don’t think that’s right, where did this number come from? It’s not the same as what’s in my spreadsheet.”
Trust directly impacts key business priorities like self-service analytics adoption, AI model accuracy, and leadership's confidence in data-driven decisions. If they can’t trust their own data, why adopt new technology? It’ll just make the problem worse.
[We need to] develop our "data platform" foundations — currently a lot of our metrics and capabilities are individual product-specific, with a lot of "scrappy" 0 to 1 work that needs to be integrated together.
Director of Product & Customer Analytics (700 person org)
84% of data leaders rated data quality and reliability their highest area of focus
Answers to “which of these describes your data team?”
While the data leaders in our study have confidence that they are helping their organizations, a high percentage of them refer to themselves as “Data Plumbers,” showing that a big part of the job is still ensuring data availability and reliability.
With so many data leaders focused on this challenge, let's explore how they're working to solve it.
Some organizations are investing in new technology to help solve the problem. 50% of those surveyed said they have made an investment in a semantic layer to help build some consistency and reliability around data. But some leaders expressed skepticism that this is actually helping.
The recent trend of BI products bundling in a proprietary semantic layer has done more harm than good. Of the customers who said “we have a semantic layer as part of our BI tool,” 57% are considering a change in tools this year.
In essence, an investment in data quality should be an investment in AI. Improved data trust should help improve the trust in AI-generated insights. But when data quality is built around adoption of a point solution, it only increases sprawl.
I guess we technically have two [semantic layers] (dbt and LookML since we use Looker -- and maybe a third emerging in Hightouch?). We try to keep most of our modeling in dbt and make the other layers thin/dumb.
VP of Data (600 person org)
Illustration by Hex
How data leaders are thinking about data quality
Look for opportunities to unify tooling - it will benefit your bottom line, make it easier to centralize on a single trust layer for data, and enhance your AI investments.
Data teams are anticipating AI-driven transformation, growing their teams to support it, and have identified self-serve as a key opportunity area. But there are other tasks threatening to take the teams’ attention and prioritization away from innovative areas.
Unify legacy systems & reports. Improve data culture. Begin to build innovative data products.
VP of Data (14,000 person org)
Develop our "data platform" foundations — currently a lot of our metrics and capabilities are individual product-specific, with a lot of "scrappy" 0 to 1 work that needs to be integrated together.
Director of Product & Customer Analytics (700 person org)