Boardrooms demand tougher AI returns & stronger data

Boardrooms will place tougher demands on artificial intelligence projects in 2026 as pressure grows for clear returns, according to forecasts from data management company Ataccama. Senior executives at the firm also expect natural language tools to change how staff access data and predict that agentic AI will prompt an expansion, rather than a contraction, of data teams.

Mike McKee, Chief Executive of Ataccama, said corporate leaders are moving past experimental deployments and fear of missing out on AI. Budget scrutiny is increasing as wider economic conditions remain uncertain and as organisations review early generative AI experiments.

"AI investment is no longer about FOMO. Boards and CFOs want answers about what's working, where it's paying off, and why it matters now. 2026 will be a year of focus. Flashy experiments and perpetual pilots will lose funding. Projects that deliver measurable outcomes will move to the center of the roadmap," said McKee, CEO, Ataccama.

McKee said AI projects that sit on strong data foundations are more likely to survive this next phase. He linked durable returns to data quality and governance rather than to front-end interfaces.

"This is good news for the AI strategies built on a strong data foundation. When data is high quality, well-governed, and explainable, AI becomes a durable asset instead of a fragile demo. That durability shows up in cost reduction, faster cycle times, and smarter automation, not just nicer dashboards.

"Go-to-market motions will evolve too. Enterprise buyers are already sceptical of black-box pitches and generic "AI-powered" slides. They want real solutions to real problems, especially in high-stakes sectors like financial services and insurance. To win in 2026, vendors will need to prove they can embed into daily operations, not just inspire a workshop," said McKee.

Natural language shift

Ataccama's Chief Product Officer, Jay Limburn, expects natural language interfaces to become a mainstream entry point for business users who want to interrogate corporate data. He highlighted text-to-SQL technologies, which translate written questions into database queries, as a key area that is moving into day-to-day use.

"For over a decade, business users have asked when they'll be able to "just ask a question" and get a real answer from their data. In 2026, that question finally becomes routine. Text-to-SQL moves from proof-of-concept to production, not because of flashy frontends, but because the foundations underneath - semantics, lineage, and quality - are strong enough to support it," said Limburn.

Limburn said that broader access through natural language will change, rather than displace, the role of data analysts inside companies. He expects analysts to spend less time acting as translators between business teams and complex data environments.

"This shift doesn't remove analysts from the equation; it amplifies them. Analysts spend less time translating vague requests and more time interrogating the business itself. Meanwhile, users across marketing, operations, and finance gain direct access to governed answers, not guesswork.

"It works because the stack is now ready. Definitions are consistent, pipelines are observable, and trust has been earned upstream. The result is not just faster queries, but a new rhythm of decision-making where insight becomes conversational, not transactional," said Limburn.

Agentic AI impact

Corey Keyser, Head of AI at Ataccama, said organisations are rethinking earlier expectations that automation and agents would significantly reduce headcount in data teams. He expects agentic AI, which can perform tasks such as querying, cleaning and documenting data, to lower the marginal cost of generating insights and to stimulate higher demand for data work.

"For years people have predicted that AI will hollow out data teams, yet the closer you get to real deployments, the harder that story is to believe. Once agents take over the repetitive work of querying, cleaning, documenting, and validating data, the cost of generating an insight will begin falling toward zero. And when the cost of something useful drops, demand rises. We've seen this pattern with steam engines, banking, spreadsheets, and cloud compute, and data will follow the same curve," said Keyser.

Keyser said easier access to data and analysis is likely to change behaviours in business units that have not traditionally engaged with central data groups. He expects a rise in AI-literate staff across operational functions and a larger need for oversight.

"As agents make it painless to ask and answer questions, entirely new parts of the business will begin participating. Units that once relied on instinct will start relying on evidence because the barrier to entry disappears. Teams that never submitted a single ticket to the data group will suddenly ask dozens of questions a week. Embedded analysts will become more valuable because they can focus on interpreting results instead of wrestling with pipelines. AI-literate subject matter experts will appear in corners of the organization where no data capability existed before, and governance teams will grow because a surge in activity always requires more oversight. The surface area will expand quickly.

"The organizations that adopt agents will discover something counterintuitive. They won't end up with fewer data workers, but more. This is Jevons paradox applied to analytics. When insight becomes easier, curiosity will expand and decision-making will accelerate. Agents won't replace the people closest to the business, instead, they'll raise the value of the work those people do. Data teams will grow because the value of data will finally become impossible to ignore," said Keyser.