Vultr released its annual AI maturity report, Navigating the Path to AI Success. The new report examines how leading organizations are leveraging artificial intelligence (AI) to drive superior business outcomes. With 87-91% of enterprises already reporting that AI adoption has led to measurable improvements in customer satisfaction, revenue, and market share, the business case for accelerating AI maturity has never been stronger. 

"AI maturity is no longer a distant aspiration, but a present-day imperative for organizations seeking to lead in their industries," said Kevin Cochrane, CMO of Vultr. "Organizations at the most mature stage of AI are raising the bar for success and outpacing their less mature peers in financial performance, innovation, and operational efficiency." 

Commissioned by Vultr and conducted by S&P Global Market Intelligence, the study surveyed over 2,000 AI-savvy executives and decision-makers across 12 countries. Respondents represented industries such as healthcare, government, retail, manufacturing, financial services, telecom, energy, travel, hospitality, media, gaming, and entertainment. While all respondents were using AI to some extent, the study employed a three-stage model to characterize the level of AI maturity in the respondents' organizations: Operational, Accelerated, and Transformational. The report also presents a qualitative perspective on AI use by enterprises of varying sizes, based on in-depth interviews with AI decision-makers and practitioners. 

Building on last year's inaugural report, which established the link between model diversity and AI maturity, this year's findings confirm that organizations are accelerating multi-model strategies to strengthen their competitive edge. Key findings from the report include:

• AI maturity delivers measurable advantage: 81% of Transformational organizations report better or significantly better financial performance-25 points above Operational peers.
• Capital is following AI workloads: 63% of Transformational firms already put more than 41% of their IT budget in cloud, pushing the enterprise average cloud share toward 43 % in 2025.
• Leaders scale through diverse, open model portfolios: Transformational organizations run 29% more distinct models than Operational peers and have grown average model counts 24% YoY.
• Execution constraints: Hardware and data pipelines slow scale-up, with top blockers being GPU capacity/performance (55%), security & compliance (45%), and real-time inference limits such as compute (54%) and storage throughput (53%).
• A decisive pivot away from hyperscalers is underway: 30% of respondents plan to build new GenAI projects with neocloud providers vs. 18% with hyperscalers.

As AI becomes embedded across more business functions-projected to reach 80% penetration within the next two years-enterprises investing in open model portfolios are achieving higher model diversity and year-over-year growth in deployed models compared to their less mature peers. The shift toward multi-model strategies and away from reliance on a single cloud provider is empowering organizations to tailor AI deployments to their unique needs and regulatory environments, supporting greater flexibility, security, and innovation in enterprise AI deployments. 

"As competitive pressures mount, AI has become a clear differentiator," Cochrane added. "The data shows that a comprehensive, multi-model approach to AI, supported by strategic investment and open, secure ecosystems, delivers measurable business value. For enterprises, the message is clear: those who commit to advancing their AI capabilities now will unlock new levels of innovation, efficiency, and competitive advantage in the years ahead."