Issue 02

Last Spring, we published our first State of Data Teams Report based on a survey of data leaders about priorities, investments, and blockers. We re-ran the survey in December to see what changed, and the shift was clear: attitudes toward AI moved fast.

Here’s what’s changed heading into 2026.

Data teams are hitting the gas on AI – but trust remains a concern

In this most recent survey, “AI and automation” jumped to a top goal for data teams. Last Spring, only 3 respondents mentioned AI as a goal for their team.

But it’s not just a goal — data teams are already putting hours into activating AI. In a list of focus areas, AI ranked second, with the number of respondants calling it an important area increasing by 24% compared to the last study.

But moving full speed ahead on AI doesn't mean the road is entirely smooth; the majority of respondents said that data trust is the #1 concern around AI adoption (which was cited nearly twice as much as any other concern). To address trust and accuracy, data teams reported investing in a variety of governance layers for AI to be more accurate. Semantic layers, once controversial, are seen as essential to this shift.

And despite concerns about AI replacing data work, the opposite is happening. 58% of respondents reported their data team grew, up from 52% in spring — a signal that AI is reshaping data work, not eliminating the need for it.

Let's get into the details.

3 key takeaways

  • 575%

    increase in data leaders citing “AI” as their #1 goal this year

  • 31%

    of data leaders cite “trust” as the top concern with adopting AI on their data

  • 58%

    of data teams are growing. Only 3% are reducing staff

Table of contents

01

AI is a top priority for data teams

02

Trust and data accuracy are the biggest concerns for AI

03

Leaders and practitioners want different AI tools

04

Data teams are still growing as AI adoption accelerates

05

What does this all mean for 2026?

01

AI is a top priority for data teams

AI is one of the top goals for data teams

When we asked data teams about their top goals, they frequently mentioned AI & Automation, representing a dramatic shift in priorities over just six months. In Spring, AI barely registered as a concern — only 4% of respondents mentioned it as a top goal in free-text responses. By December, that number had surged to 27%.

This wasn't a gradual trend — it was a rapid acceleration. Teams moved from cautiously exploring AI to actively implementing it, driven by competitive pressure, leadership buy-in, and increasingly mature tooling options.

(note that these percentages add up to more than 100% because we allowed respondents to list multiple goals. It’s ok to have complex feelings)

AI is a top priority for data teams

On a scale of 1-5, how big of a priority is the following for you and your data team?

AI tooling focus surged while self-serve and data quality declined

AI is a top priority for data teams

AI has gone from excitement to implementation

When we asked data teams what responsibilities they were focusing on last spring, “AI tooling” was the penultimate choice, taking 6th out of 7th place. But by the end of the year, it jumped by 24%, knocking out ”enabling self-serve” as the second biggest area data teams are spending their time. This reveals that AI is not just a goal, but already adopted in data team workflows.

With trust being a common concern about AI-generated data insights, it’s unsurprising that data quality is still a top concern.

What is one big goal you have for your team in the next year?

"Move and build faster with AI."

CEO, 300-person Tech company

02

Trust and data accuracy are the biggest concerns for AI

The majority of people feel positively about AI, but data quality is the biggest barrier to adopting it.

In Spring, nearly half of respondents surveyed said AI was overhyped [or that they were worried about it taking jobs]. Now a vast majority have a positive sentiment toward AI, with 52% percent saying it’s a great tool for builders or a centerpiece of self-serve

When asked what the biggest barrier to adopting AI was, the majority of respondents reported a lack of trust . Data quality and concerns about AI hallucinations are slowing adoption and creating a new challenge for data teams to focus on.

Mid-level managers, who are closer to the data, feel the pain more than executives (+17% vs VPs at -15%).

Trust dominates AI adoption barriers

AI is a top priority for data teams

Data cleaning leads trusted context strategies

Data cleaning leads trusted context strategies

Teams are using a variety of methods to build context.

The dominant industry narrative in 2025 was that semantic models were the silver bullet to enabling AI on data. Today, data teams are investing in a variety of techniques, from better data organization to semantic layers, governance tools and AI rules files.

It’s an indication that data teams are taking a broader view of context as their organizations get serious about AI in production.

In a change from Spring, no one is questioning the value of semantic models now.

While semantic models aren't the dominant method for ensuring data quality, no one is questioning their value anymore. In Spring, a few respondents had doubts.

Now, the majority report having one in their BI tools and many who previously doubted them have adopted a standalone semantic layer, fearing vendor lock-in.

More customers are building semantic layers, and fewer are doubting their value.

More customers are building semantic layers, and fewer are doubting their value.

What is one big goal you have for your team in the next year?

"Standing up our Intelligence product for AI/ML use cases internally and externally."

CTO, 80-person data company

03

Leaders and practitioners want different AI tools

Data leaders envision more specialized AI-on-data tools

LLMs have had an immediate impact in helping handle data questions, with many data teams mentioning that they ask questions of data in tools like ChatGPT and Claude. However, with doubts mounting around trust and accuracy, a lower percentage of data leaders predict these tools will be used in the future.

The biggest growth area? BI tools - few of them are enabling AI in production today, but data leaders predict they will be an important AI tool for assisting with dashboard production and reporting.

This trend away from generic LLMs and toward specialized AI tools is representative of a future where AI is a table-stakes capability in data workflows. Note that 0% of data leaders predict that they’ll avoid the AI wave altogether.

What is the relationship between current and future AI tools (among data leaders)?

Shift from general LLMs toward specialized analytics AI tools

Shift from general LLMs toward specialized analytics AI tools

What is one big goal you have for your team in the next year?

"Enhance our user interface with data via an LLM."

Marketing Director, 430-person data software company

04

Data teams are still growing as AI adoption accelerates

As AI adoption accelerates, data teams continue to grow. Organizations are still investing in the people behind the operation. AI isn't replacing data professionals — it's making them more essential.

Spring

  • Growing: 34 (52%)

  • Flat/Steady state: 25 (38%)

  • Shrinking: 6 (9%)

Fall

  • Growing: 41 (58%)

  • Flat/Steady state: 27 (38%)

  • Shrinking: 3 (4%)

What is the state of your data team?

What is the state of your data team?

What is one big goal you have for your team in the next year?

"Scale agentic solutions for high volume work."

OD, 200-person web development agency

05

What does this all mean for 2026?

As we head into 2026, we expect to see more data teams use AI in their workflows - with an emphasis on providing trusted, accurate answers.

As teams race to implement AI, they're clear-eyed about what it takes to make it work. Context and trust aren't blockers they're waiting to solve — they're the foundation teams are actively building through cleaning, governance, and semantic modeling.

The challenges data teams are facing today - cleaning their data, moving faster, supporting self-serve, and delivering business impact, are familiar themes:

AI isn't changing what people need from analytics tools, it's amplifying those needs.

And despite all the noise about AI replacing jobs, a majority of data teams are growing. AI isn't eliminating the need for data professionals — it's creating demand for teams that can implement it thoughtfully.

AI adoption is happening fast. Teams succeeding with it aren't treating it as a magic solution — they're treating it as a tool that requires the same rigor, quality standards, and strategic thinking that good analytics has always demanded.

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