How Pacesetter companies match the pace of AI advancement
Dorit Zilbershot, group vice president of product management for the ServiceNow Platform, co-authored this blog post.
Over the past 12 months, we've seen incredible advancements in AI, particularly in areas such as search, agentic AI, and data. Organisations are finding measurable value by embracing emerging AI technologies. In fact, 67% of the 4,500 executives we surveyed worldwide for the ServiceNow Enterprise AI Maturity Index 2025 reported increased gross margins using AI.
We’re witnessing a once-in-a-generation occurrence where technology is evolving much faster than organisations can adopt and integrate it. As a result, AI maturity in organisations around the world actually dipped nine points since our study last year.
This evolution presents a unique opportunity for leaders to embrace change, drive transformation, and guide their organisations. In fact, executives expect to increase their investments in AI in the next fiscal year.
All organisations can take lessons from the group we classified as AI Pacesetters—organisations integrating AI into their operations most effectively. These Pacesetters have a few things in common, one of which is a clear, shared AI vision.
Defining the right AI vision is vital to keeping pace with the speed of AI advancement, helping accelerate the experimentation cycle, and effectively addressing governance. Recognising the risks and understanding how to prove reliability and measure productivity are also imperative before scaling out across the enterprise.
The seismic effects of agentic AI
Agentic AI is one of the most exciting recent developments. It’s all about reasoning and action. Agentic AI systems stitch together multiple tasks, make micro-decisions, and execute entire workflows.
In the past, we looked at machines as tools we could use. With agentic AI, we can oversee the tools and software to do work on our behalf. That frees us to focus on oversight, creativity, and strategy while AI agents handle the day-to-day execution of tasks and reach out to us when they need help.
It’s less about automation in the way we thought about AI in the past and more about orchestration and collaboration. Agentic AI can really transform productivity across the enterprise.
Imagine improving your level of service by orchestrating complex processes more efficiently. That's the promise of agentic AI, and it’s huge. That’s why 43% of respondents to our survey are considering adopting agentic AI in the next 12 months.
A sandbox for experimentation
Overall, we're seeing tremendous enthusiasm for AI across organisations. In fact, AI Pacesetters are consistently experimenting with novel ways to put AI to work. But here's the critical question: experimentation for what purpose?
The most successful AI strategies address this by starting with a clear goal. You need to be able to map your current workflows and identify repeatable goal-driven processes. Are you looking to optimise customer interactions and drive top-line growth? Or are you concentrating on boosting productivity for specific roles?
Once you define that purpose, you can focus your experimentation on targeted use cases that yield the best results, such as triaging IT tickets, streamlining customer support processes, or onboarding new hires, customers, or suppliers.
Next is providing common tool sets and data to feed all these experiments. AI agents thrive on real-time, contextual access to systems and data points.
Responsible governance
As we push the boundaries of AI, we must counter experimentation with responsible governance. That means carefully managing:
- The data we use
- The tools and their security
- The ethical and legal implications of our use cases
Organisations need to balance a culture of experimentation with the responsibility of doing the right things and doing things right. That requires effective boundaries, escalation rules, and trust frameworks.
Nearly two-thirds (63%) of AI Pacesetters have made significant progress drafting AI-focused data governance, privacy, and compliance policies, according to our research. As a result, they can encourage AI experimentation while minimising risks.
Performance management is a crucial part of governance to ensure efforts align with results. We've identified three crucial indicators to measure the impact of AI investments:
- Adoption: Who’s using AI, and how are they integrating it into their roles?
- Relevance and accuracy: Is AI truly adding value, or is it just a distraction?
- Operational results: Are we seeing faster customer response times, improved productivity, or top-line growth?
A platform approach
The key to keeping up with rapidly advancing AI lies in connecting AI technologies with enterprise data to create meaningful user experiences. It's not just about having AI—it's about deploying AI that genuinely adds value.
Taking a platform approach to AI can help organisations realise value faster than they could with point solutions. In fact, 66% of AI Pacesetters in our study employ a platform approach. It offers five major advantages:
- Speed and experimentation
- Model deployment and scaling
- Data integration
- Easy user experience
- Built-in governance and security
Check out our full Enterprise AI Maturity Index report for more valuable insights.