ThoughtSpot released “The New Operating Model for Analytics,” a global study of 1,200 data and business leaders revealing 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” organizations – those which have already operationalized Gen AI and Agentic AI use cases across broader business functions – have broken the cycle, with 53% of their workforce accessing trusted answers instantaneously.
AI Maturity is a Flywheel
The report proves that AI maturity creates a self-funding flywheel. High-performing organizations 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 organizational adoption, resulting in more measurable outcomes. With these success metrics fuelling increased investment in future projects:
- The Investment Surge: 93% of AI Leaders plan to increase budgets in 2026, compared to 60% of those still in the experimentation phase. Overall, one in ten (11%) companies plan to increase their funding of AI projects by over 50% this year, while a further one third (34%) plan to increase their AI budgets by at least 10%.
- Trust Leads to Adoption: 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 turning projects widely operational.
- The Cost of Delay: Nearly 40% of businesses are still stuck waiting over 24 hours for single insights, with one quarter (24%) forced to wait upwards of a week. In an era of instantaneous decision making, this bottleneck is becoming a primary threat to market relevance.
“The data confirms a widening performance gap between those companies still with AI prototypes and those who have moved to production,” 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 organizational 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.”
The Operating Model Revolution
The transition to “Agentic Analytics”—where AI agents alert, explain, and take action—is fundamentally altering the corporate structure:
- Building vs. Buying: Only 9% of mature organizations attempt to build agentic AI entirely in-house, preferring to partner for best-of-breed architecture to scale faster.
- The Talent Shift: 82% of leaders recognize that upskilling and reskilling employees is the most critical impact of the agentic era. Leading organizations are showing a willingness to invest in this upskilling. Half of all organizations 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, knowledgeable and set up to succeed in the AI age.
- Long Term Planning: To best set the base for long term success, organizations are largely split between implementing centralized AI management (38%) and a hybrid, federated approach (38%). To date, only 16% are pursuing more decentralized AI strategies.
To download a copy of the full report and to see the wider trends driving decision making across businesses, please visit here.
