There’s a contradiction at the heart of AI adoption: AI technology is rapidly accelerating, yet organizational maturity is slipping. Despite exponential growth in AI capabilities, the average organizational AI maturity score fell by 9 points year-over-year, according to ServiceNow and Oxford Economics’ 2025 AI Maturity Index.
The truth is that exponential growth and rapid acceleration are exactly the problems. When innovation moves at Mach 10 speed, it can be difficult to rein it in before it inevitably takes another leap forward.
So, how can organizations keep up and continue raising their AI maturity despite such rapid growth?
The Numbers Behind the Paradox
This year’s AI Maturity Index scores organizations across five key dimensions:
- Leadership and strategy.
- Workflow integration.
- Talent and workforce.
- Governance.
- Value realization.
The resulting score provides a holistic measurement designed to capture whether companies are using AI and whether they’re doing it effectively, ethically and at scale. But this year’s results send mixed messages:
- The average AI maturity score declined by 9 points compared to last year.
- Only 18% of surveyed organizations scored above 50 on the 100-point scale, qualifying them as “AI Pacesetters.”
These data points suggest something initially difficult to grasp: AI is spreading faster than it’s maturing. In many cases, adoption is outpacing readiness. Organizations are piloting tools without clear strategies, scaling systems without sufficient governance and investing heavily without fully understanding how to measure return.
Meanwhile, the bar for what qualifies as “mature” AI use has risen. A year ago, using chatbots or basic automation might have indicated forward-thinking adoption. Today, the expectation includes autonomous decision-making systems, cross-platform data integration and enterprise-wide governance frameworks. Many companies that were previously considered ahead of the curve now find themselves behind it.
It’s a paradox of progress. As AI capabilities grow more powerful, the challenge of adopting them responsibly and at scale becomes more complex. As a result, many organizations are falling behind — not because they’re doing less, but because the standard is growing higher, faster.
But speed of innovation isn’t the only thing holding organizations back.
What Else is Holding Orgs Back Beyond AI Acceleration?
There is a long list of potential reasons why maturity is falling, but a couple stand out as particularly interesting and, importantly, within organizations’ control:
Lack of a Clear Strategy
The sheer amount of available AI tools is enough to make an omnipresent cyborg’s head spin. It can be easy to fall into a trap of adopt, adopt, adopt, trying to keep pace with acceleration. But what often happens is that you end up with too many tools and not a well-rounded understanding of any. Processes become fragmented under the illusion of AI efficiency — they each streamline one or two tasks individually, but don’t integrate.
The AI Maturity Index looks to its Pacesetters and their preference for platforms as an example of one way to solve this problem. Instead of adopting a new standalone tool every time one hits the market — reinventing the wheel each time — choose one platform that offers built-in AI capabilities across various workflows and scale with that. This can be a tough choice in and of itself, but here are ways to narrow down your options:
- Start with a use case: What problems are you trying to solve? Match the platform’s strengths (e.g., NLP, automation, etc.) to your highest-priority use cases.
- Check integration compatibility: Can this platform work with your current data systems and tech stack? Prioritize platforms that offer pre-built connectors or open APIs for your CRM, ERP, data warehouse, etc.
- Verify scalability: Will this platform scale as our AI workloads grow? Choose cloud-native or hybrid platforms that can scale compute and storage dynamically.
- Run a pilot first: Does it actually work for us in practice? Pilot with a real business case before full deployment to test fit, performance and user adoption.
Shortage of Skilled Talent
According to a recent Bain & Company report, a sizable number of executives (44%) say that a lack of in-house AI expertise is slowing adoption. But it’s not just about hiring more data scientists — it’s about attracting professionals across roles who understand how to collaborate with and make decisions alongside AI.
Pacesetter-categorized organizations in the AI Maturity Index are most likely to agree that they have the right people in place to execute their AI strategy. On a mission to achieve higher maturity, organizations need to invest in people.
To start, define what being “AI-ready” means for your organization and develop role-specific competencies that reflect how AI intersects with each function (e.g., marketing, finance, product). For example, in marketing, AI-readiness might mean comfort with tools like predictive analytics or generative content platforms.
Resistance to Change
The opinions people have about AI span a spectrum — from disdain to delight. In fact, employees attach “a considerable degree of stigma” to their peers who choose to use AI at work, according to a recent study conducted by researchers at Duke University’s Fuqua School of Business, Management & Organizations.
Oppositely, a hallmark trait of 71% of Pacesetters from the AI Maturity Index is that they empower their employees to make their own decisions about how AI can improve their work. With that in mind, AI adoption and maturity is as much of a workplace culture game as it is a technological one, if not more, which takes intentional and sustained effort.
Here are some tips to help begin positive conversations about using AI at work:
- Create cross-functional AI champions: Identify early adopters across departments and empower them to share wins and learnings.
- Celebrate small wins: Recognize teams that successfully use AI to improve processes, even modestly.
- Address replacement fears head-on: Be transparent that AI is here to augment, not eliminate, roles.
Final Thoughts
The organizations with the highest degree of AI maturity understand that intentionality beats intensity when it comes to building sustainable AI capabilities — and that AI adoption is more of a mindset than a list with boxes to check. Building the right culture and infrastructure is essential to the successful integration of AI processes. Without these elements, even the best tools will underperform.
If organizational AI maturity is important to you, be like the Pacesetters and foster a culture of AI acceptance and excitement by training, upskilling and reskilling employees, having transparent conversations, getting governance right and, most importantly, keeping humans at the center of it all.
For more in-depth insight about AI maturity in 2025, read ServiceNow’s full report.
