A growing number of companies are experimenting with AI, but only a small group is managing to turn those efforts into clear business gains. New research from NTT DATA highlights what separates these early winners from the rest and offers a practical look at how they use AI to drive growth, improve operations, and build long-term advantage.
The findings come from a survey of 2,567 senior executives across 35 countries and 15 industries. Only 15% of respondents qualified as “AI leaders,” a group defined by clear strategic direction, disciplined execution, and strong alignment between AI projects and business goals. These organisations also reported faster revenue growth and better profit margins than their peers.
Yutaka Sasaki, President and CEO of NTT DATA Group, said the shift is no longer optional.
“AI accountability now belongs in the boardroom and demands an enterprise-wide agenda. Our research shows that a small group of AI leaders already are using AI to differentiate, grow and reinvent how humans and machines create value together.”
A strategy that treats AI as core to the business
One of the biggest differences between AI leaders and everyone else is the way they approach strategy. Instead of treating AI as an add-on or a series of experiments, these companies view it as a central engine for growth. They build clear plans that tie AI directly to business priorities, which helps them focus on the opportunities that matter most.
Leaders also avoid spreading their resources too thin. Instead of launching dozens of small pilots, they choose one or two high-value areas and redesign those processes end to end with AI. This approach creates faster wins, which leads to more investment and fuels what the report describes as a flywheel effect.
Another key move: leaders rebuild core systems with AI built in from the start instead of layering new tools on top of outdated technology. This gives them deeper gains and prepares them for long-term scaling.
How AI leaders bring their strategy to life
Strong ideas only work with strong execution, and this is where top performers stand out the most.
AI leaders invest early in the infrastructure needed to support large-scale AI. In some cases, that includes shifting parts of their technology stack to meet private or sovereign AI requirements. They also work to reduce bottlenecks so teams can experiment and deploy updates more easily.
These companies take an “expert-first” approach to the workforce. Rather than using AI as a replacement, they use it to help experienced employees work faster and tackle more complex tasks. This keeps teams engaged and helps ensure AI is used responsibly.
Adoption is treated as a long-term commitment. Leaders support change with clear communication, ongoing training, and structured change-management plans. This reduces hesitation and encourages AI use throughout the organisation.
Governance is another area where leaders excel. Many centralise oversight under roles such as Chief AI Officers and build processes that balance innovation with risk management. The result is a framework that makes scaling AI safer and more predictable.
Partnerships also play a key role. Top organisations often bring in experts and pursue arrangements tied to shared outcomes, which helps them move faster without losing sight of their goals.
Abhijit Dubey, CEO and CAIO of NTT DATA, Inc., summed up the winning approach: “Once AI and business strategies are aligned, the single most effective move is to pick one or two domains that deliver disproportionate value and redesign them end-to-end with AI. Supporting this focused, end-to-end approach with strong governance, modern infrastructure and trusted partners is how today’s AI leaders are turning pilots into profit and pulling ahead of the market.”
The report makes one point clear: AI success isn’t about having the most tools. It’s about having a clear plan, disciplined execution, and a willingness to rethink how work gets done. As more companies seek to move past experimentation and into measurable impact, the practices of today’s AI leaders offer a roadmap for what sustainable AI adoption can look like.
