New research from ThoughtSpot, surveying 1,200 data and business leaders globally, reveals a growing performance divide between organisations that have operationalised AI and those still experimenting. 

The research revealed that the real measure of maturity isn’t just AI adoption; it’s how quickly and confidently they can deliver trusted answers at scale and turn those answers into decisive action.

As 74% of businesses race toward full GenAI maturity within three years, the report uncovers a stark “Legacy Latency Crisis.” While the average company still waits a long time for insights, “Leading” organisations that have already operationalised Gen AI and Agentic AI across broader business functions, are accessing trusted answers instantaneously (for 53% of their workforce).

AI Maturity is a Flywheel

The report claims that AI maturity creates a self-funding flywheel. High-performing organisations aren’t just experimenting, they are delivering immediate value by placing trustworthy insights at the heart of their architecture. 

These trusted insights are in turn driving higher organisational adoption, resulting in more measurable outcomes. These success metrics are fuelling increased investment in future projects.

93% of AI Leaders plan to increase budgets in 2026, compared to 60% of those still in the experimentation phase. Overall, 11% of companies plan to increase their funding of AI projects by over 50% this year, while a further 34% plan to increase their AI budgets by at least 10%.

Amongst surveyed AI leaders, 95% reported being very confident in the insights delivered via their analytics and intelligence platforms. This compares to only 45% of businesses in the experimental phase, highlighting the critical role trust plays in making projects widely operational.

Nearly 40% of businesses are still stuck waiting over 24 hours for single insights, with 24% forced to wait upwards of a week, the report finds. In an era of instantaneous decision making, this bottleneck is hindering progress.

The Operating Model Revolution

The transition to “Agentic Analytics” – where AI agents alert, explain, and take action – is fundamentally altering the corporate structure.

Only 9% of mature organisations attempt to build agentic AI entirely in-house, preferring to work with specialist partners to scale faster.

82% of leaders recognise that upskilling and reskilling employees is the most critical impact of the agentic era. Leading organisations are showing a willingness to invest in this upskilling. 

Half of all organisations are already providing leadership training to ensure the smooth rollout of AI projects, while 34% have also implemented a full change management strategy to ensure all employees are informed and knowledgeable about AI initiatives.

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Organisations are largely split between implementing centralised AI management (38%) and a hybrid, federated approach (38%). To date, only 16% are pursuing more decentralised AI strategies.

“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.”