At Knowledge 2025, Tom Smith talks with Dave Wright and Brian Solis from ServiceNow about how the AI Maturity Index helps enterprises evaluate and evolve their AI strategy. Grounded in research from 4,500 companies, this five-stage model reveals how to improve governance, drive adoption, and unlock business value from AI investments.
Highlights
Genesis and Goals of the AI Maturity Index (00:23)
The conversation takes place at the ServiceNow Knowledge 25 event in Las Vegas, featuring ServiceNow executives Wright and Solis. Wright explains the creation of the AI Maturity Index, driven by the need for a standard maturity index for customers deploying AI. The index was developed by interviewing 4,500 companies and identifying five criteria to rate maturity levels. The index provides a five-stage maturity model, allowing companies to plan and track their AI journey.
Industry and Geographic Insights (01:41)
Smith inquires if the surveyed companies are only ServiceNow customers or a broader range. Wright confirms the survey included ServiceNow customers and highlights the insights gained on differences based on industry and geography. The index offers a 3-D view of maturity progression, considering industry and geographic factors. Data governance and AI governance are critical; governance is often a barrier to AI advancement.
Counterintuitive Findings on AI Maturity (03:18)
Smith discusses the counterintuitive finding that AI maturity is down despite new technology and use cases. Wright attributes the decrease to the overwhelming number of new technologies and use cases, leading to a reevaluation of AI strategies. A CIO‘s comment at a meeting highlights the realization that AI’s potential is greater than initially understood. The discussion touches on the concept of a “control-delete moment,” where companies reconsider their AI strategies.
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Challenges and Opportunities in AI Deployment (05:59)
Solis and Wright discuss the complexity and speed of AI evolution, leading to a need for better understanding and governance. Global CIO Rachel Sandel was at the event. Her company, Orica, is mentioned as an example of a company with advanced AI governance structures. Rather than relying on isolated IT setups, the discussion points to centralized AI deployment as key to scaling impact across the organization. Solis notes that only 30% of companies deploy AI use cases across multiple functions, reinforcing silos rather than breaking them down.
Governance and Measurement in AI (08:44)
Wright and Solis discuss the need for better governance and data governance in AI deployment. The conversation shifts to measurement, with traditional IT metrics being reevaluated in the context of AI. New metrics like avoided failures and automated processes are introduced, reflecting the evolving nature of AI measurement. The discussion touches on measuring the value extracted from AI investments.
People Considerations and Organizational Impact (11:07)
Smith raises the issue of people considerations and organizational impact in AI deployment. Solis emphasizes the importance of talent and skills in AI roadmaps, with a focus on talent and skills as key pillars. The conversation explores the potential for AI to create new roles and opportunities for human-machine collaboration. Wright talks about the need for organizations to have tough conversations about AI’s impact on jobs and workforce transformation.
Industry Leadership in AI (16:02)
Smith asks about the top three industries leading in AI maturity: technology, heavy manufacturing, and banking. Wright attributes the leadership to these industries’ early adoption of automation, which facilitated rapid AI adoption. The discussion includes the balance between automation and innovation, with a caution against automating to mediocrity. Solis adds that customer experience should be a key focus in AI deployment, using Amazon as an example of successful automation and customer obsession.
Platform Approach and Business Transformation (21:46)
Smith inquires about the benefits of a platform approach in AI deployment. Wright explains that a platform approach provides centralized control and visibility, essential for managing AI agents. The AI Control Tower concept is introduced, allowing for the management of agents from different vendors within a unified platform. Solis talks about how businesses must remodel for an era of AI, moving beyond digital transformation to true business transformation.
Final Thoughts and Closing Remarks (24:52)
Wright and Solis discuss dreaming bigger in AI deployment. There’s a need for organizations to think beyond cost savings and focus on value creation and business transformation. The discussion concludes with a call to action for organizations to explore new possibilities with AI and reimagine their business models.
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