AI leaders pull ahead as laggards face analytics latency

Organisations that have moved artificial intelligence from trials into day-to-day operations are pulling ahead of those still testing the technology, according to a global ThoughtSpot survey of data and business leaders.

Based on responses from 1,200 leaders, the research points to differences in confidence, analysis speed, and budget plans between "leading" organisations and those still experimenting. It also highlights training and organisational change as priorities as companies prepare to expand the use of AI in analytics.

Trust gap

Confidence in analytics platforms was one of the biggest dividing lines. Among AI leaders, 95% reported being highly confident in the insights delivered by their analytics and intelligence platforms, compared with 45% of organisations still experimenting.

ThoughtSpot framed trust as a prerequisite for wider adoption, with more confident organisations more likely to roll out AI-driven analytics across teams. The results suggest confidence in data and outputs determines whether AI projects move beyond early use cases.

Latency problem

The findings also point to a "Legacy Latency Crisis" in how long analytics workflows take to deliver answers. Nearly 40% of businesses said they still wait more than 24 hours for a single insight, and around 24% said they can wait more than a week.

By contrast, organisations that have operationalised generative AI and agent-based approaches across broader business functions reported faster access to answers. In these leading organisations, 53% of the workforce can access "trusted answers" instantaneously, according to the survey.

The gap reflects both technology choices and operating practices, including data management, analytics governance, and how widely access is distributed to business users.

Budget intentions

Planned investment also differs sharply. Among AI leaders, 93% plan to increase budgets in 2026, compared with 60% of organisations in the experimentation phase.

Overall, 11% of respondents plan to increase funding for AI projects by more than 50% this year, and a further 34% plan to raise AI budgets by at least 10%.

Some 74% of businesses expect to reach full generative AI maturity within three years, although the results suggest many still face delays in analytics delivery and uncertainty about output quality.

From pilots to production

ThoughtSpot positioned the shift from prototypes to production as as much an organisational challenge as a technical one, citing alignment with business strategy and workforce readiness as recurring gaps in AI programmes.

"The data confirms that the gap between AI evaluations and those in production is the fastest-widening divide in enterprise performance," said Cindi Howson, Chief Data & AI Strategy Officer at ThoughtSpot.

"Moving from a prototype to production stage is less about technical maturity and more about organisational readiness. Key steps such as aligning to business value or ensuring AI literacy company wide are often forgotten as companies rush to implement AI-anything. What this report shows is the critical role that alignment to business strategy and people change management play in achieving AI maturity," Howson said.

Agentic analytics

The report focused on "agentic analytics", in which AI systems generate alerts, provide explanations, and take action based on defined processes. ThoughtSpot argued the shift changes how organisations structure analytics teams and governance.

Mature organisations also appear to favour partnerships over full in-house builds. Only 9% said they attempt to build agentic AI entirely in-house, according to the findings.

Operating models varied. Respondents split evenly between centralised AI management and hybrid federated approaches, with 38% selecting each. A smaller share, 16%, said they are pursuing decentralised AI strategies.

Training focus

Workforce development was the most widely cited priority in the "agentic era". Some 82% of leaders said upskilling and reskilling employees is the most critical impact of the shift.

Half of organisations said they already provide leadership training linked to AI roll-outs. Around 34% said they have implemented a full change management strategy so employees receive information and guidance about AI initiatives.

The report draws on research by Sapio Research, which surveyed data and business leaders about current and planned work involving generative AI and agentic AI projects.