ThoughtSpot releases new operating model report for analytics
ThoughtSpot released The New Operating Model for Analytics, a global study of 1,200 data and business leaders, and reported an increasing focus on Artificial Intelligence (AI) training and formal change management as organizations moved to scale trusted AI and agentic analytics; the report framed maturity as the ability to deliver trusted answers at scale and convert those answers into decisive action.
The report said 74% of businesses expected to reach full Generative AI (GenAI) maturity within three years and identified a “Legacy Latency Crisis” in which many organizations continued to wait extended periods for insights, while groups classified as Leading reported that 53% of their workforce accessed trusted answers instantaneously.
The report described Agentic Analytics as an approach in which AI agents alert, explain, and take action, and it noted that the platform capabilities discussed included specialized Spotter agents that automated stages of the analytics workflow, the combination of agentic AI with natural language search, access via web and mobile, and an embedded low-code option for integrating analytics into applications.
Research for the report was conducted by Sapio Research and surveyed over 1,200 data and business leaders; findings included that 93% of AI Leaders planned to increase budgets in 2026 compared with 60% of organizations in an experimentation phase, that 11% of companies planned to raise AI funding by more than 50%, that 34% planned at least a 10% increase, that 95% of AI Leaders reported high confidence in their insights versus 45% in the experimental phase, and that nearly 40% of businesses waited over 24 hours for single insights while 24% waited a week or more.
“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 report also reported that only 9% of mature organizations attempted to build agentic AI entirely in-house, that 82% of leaders identified upskilling and reskilling as the primary workforce impact, that half of organizations provided leadership training and 34% implemented full change management strategies, and that organizations were split on governance with 38% favoring centralized AI management, 38% favoring a hybrid approach, and 16% pursuing decentralized strategies.