Unlocking of the future of Enterprise AI: Insights of the Servicenow AI maturity index
In Servicenow Knowledge 2025 in Las Vegas, I had the privilege of joining the innovation director Dave Wright, my boss, and Tom Smith, editor in chief, analyst, at Cloud warto discuss a fundamental development in the landscape of AI: the Servicenow AI maturity index. This comprehensive framework, based on ideas of more than 4,500 global organizations, offers a structured approach to evaluate and advance in AI strategies within companies.
Accelerate the success of the AI Enterprise: Introduction of the Servicenow AI maturity index
In Servicenow, we believe that the future is not something we enter, it is something that we architect and influence. Recently I had the opportunity to join Dave Wright and Tom Smith to explore ideas from the AI maturity index. This is not just another frame. It is a living plan, made up of a deep investigation in more than 4,500 global organizations, designed to help companies better understand where they are on their trip, and most importantly, how to advance with intention and confidence.
What is the maturity index of AI?
The maturity index of the organizations classifies in five different stages, providing a road map for companies to evaluate their current AI capacities and identify areas for growth. This model emphasizes the importance of governance, adoption and delivery of the tangible commercial value of AI investments
Why does this matter now
Ai is no longer a fashion word; It is a commercial imperative. But too often, organizations are submerged without a clear strategy or a government model. That’s where the AI maturity index enters. The path to excellence in AI in five different stages breaks up, helping leaders to identify where they are and what steps take to generate significant results.
In our conversation, we explore:
The origin of the index and how this concept came to life. Spoiler Alert: He was born from the need for a real and practice roadmap to help leaders to operationalize AI.
A surprising insights: Many organizations report a dip in the maturity of AI as they climb. It is not always a straight line. And understand why it is key to advance.
The future of governance and value: we unpack why maturity is not just about using AI, it is about aligning it with the purpose, trust and measurable impact.
If you are leading the digital or AI transformation, or simply trying to make sense to where your organization fits in this landscape that changes rapidly, this conversation is for you.
Watch the video here – And discover where AI is in the maturity curve.
Genesis and objectives of the maturity index of AI (00:23)
The conversation is held at the Servicenow Knowledge 25 event in Las Vegas, with Servicenow Wright and Solis executives. Wright explains the creation of the AI maturity index, driven by the need for a standard maturity index for customers implemented. The index was developed by interviewing 4,500 companies and identifying five criteria to qualify expiration levels. The index provides a five -stage maturity model, which allows companies to plan and track their AI trip.
Industry and Geographical Insights (01:41)
Smith asks if the companies surveyed are only Servicenow customers or a broader range. Wright confirms that the survey included Servicenow customers and highlights the ideas acquired about industry and geography differences. The index offers a three -dimensional vision of maturity progression, considering the industry and geographical factors. Data governance and AI governance are critical; Governance is often a barrier to the advance of AI.
Contraintuitive findings about AI’s maturity (03:18)
Smith discusses the contradictory finding that the maturity of AI has decreased despite the new technology and use cases. Wright attributes the decrease to the overwhelming amount of new technologies and use cases, which leads to a reassessment of AI strategies. The comment of a CIO at a meeting highlights the understanding that AI’s potential is greater than it was initially understood. The discussion touches the concept of a “control moment”, where companies reconside their AI strategies.
Challenges and opportunities in the deployment of AI (05:59)
Solis and Wright discuss the complexity and speed of the evolution of AI, which leads to the need for better understanding and governance. The Global Rachel Sandel CIO was in the event. Its company, Orica, is mentioned as an example of a company with advanced government structures of AI. Instead of trusting isolated IT configurations, the discussion points to the centralized implementation of AI as a key to climbing the impact throughout the organization. Solis points out that only 30% of companies implement cases of use of AI in multiple functions, reinforcing the silos instead of decomposing them.
Government and measurement in AI (08:44)
Wright and Solis discuss the need for better governance and data governance in AI implementation. The conversation changes to the measurement, with the traditional IT metrics that are re -evaluated in the context of AI. New metrics are introduced as avoiding failures and automated processes, reflecting the evolutionary nature of AI measurement. The discussion touches the measurement of the value extracted from the investments of AI.
People’s considerations and organizational impact (11:07)
Smith raises the issue of people’s considerations and the organizational impact on the deployment of AI. Solis emphasizes the importance of talent and skills in AI route, with an approach to talent and skills as key pillars. The conversation explores AI’s potential to create new roles and opportunities for the collaboration of the human machine. Wright talks about the need for organizations to have difficult conversations about the impact of AI on jobs and the transformation of the workforce.
Industry leadership in AI (16:02)
Smith asks about the three main industries that leaders in the maturity of AI: technology, heavy manufacturing and banking. Wright attributes leadership to the early adoption of the automation of these industries, which facilitated the rapid adoption of AI. The discussion includes the balance between automation and innovation, with a caution against the automation of mediocrity. Solis adds that the client’s experience should be a key approach in AI implementation, using Amazon as an example of successful automation and client obsession.
Platform and Commercial Transformation approach (21:46)
Smith asks about the benefits of a platform approach in AI’s implementation. Wright explains that a platform approach provides centralized control and visibility, essential to administer AI agents. The concept of AI control tower is introduced, which allows the management of agents from different suppliers within a unified platform. Solis talks about how companies must remodel for an era of AI, going beyond digital transformation to true commercial transformation.
Final thoughts and closing comments (24:52)
Wright and Solis discuss the most large in the deployment of AI. There is a need for organizations to think beyond cost savings and focus on the creation of value and commercial transformation. The discussion concludes with a call to action for organizations to explore new possibilities with AI and reinvent their business models.
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