Some organizations are racing ahead in their use of artificial intelligence, fueled by a clear purpose and solid business models. While companies worldwide grapple with integrating AI responsibly, a recent study spotlights how a small segment—deemed AI leaders—turn these efforts into tangible gains. Executives facing evolving market demands may find valuable insights in the distinct behaviors and management styles these AI leaders demonstrate.
Earlier reports focused mainly on forecasts or experimental AI pilots, often highlighting caution and uncertainty about return on investment. More recent studies began tracking companies integrating AI in isolated projects, but few documented large-scale structural and financial outcomes. The new NTT DATA survey draws sharper lines between AI leaders and other firms, detailing how comprehensive, coordinated strategies now correlate with growth and operational performance benchmarks.
What Drives AI Leaders to Excel?
According to NTT DATA’s research, only 15% of global companies surveyed have achieved the distinction of AI leader. These organizations share several traits: a clear vision for AI’s business role, robust operating frameworks, and disciplined execution. Their focus lies in treating AI as a main driver of progress, rather than an add-on to existing processes. This clarity enables them to prioritize high-value business areas, resulting in stronger financial indicators. As a result, they report both higher revenue and profitability compared to peers. One senior executive observed,
“AI accountability now belongs in the boardroom and demands an enterprise-wide agenda.”
How Do They Deploy Their AI Strategy?
Execution supports the differences between leaders and other groups. These companies invest in scalable and secure systems that can handle expanding AI needs and sometimes tailor infrastructure to local requirements or sovereignty demands. Removing technical bottlenecks allows teams to focus on adoption. Instead of using AI to cut staff, leaders empower skilled employees to take on more complex work, with AI handling repetitive or challenging tasks. Long-term adoption depends on communication and consistent change management across the organization, addressing potential resistance at all levels.
Governance and Partnerships: What Roles Do They Play?
Leading companies centralize AI oversight, assigning responsibility to roles like Chief AI Officer and building advisory structures to balance innovation and risk. Robust governance frameworks equip them to expand their AI initiatives strategically. Partnerships also matter; collaborating with external experts and establishing shared outcome agreements help these companies move quickly. This combination of internal focus and external collaboration strengthens both innovation and risk management. As Abhijit Dubey from NTT DATA put it,
“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.”
Companies closer to the mainstream often experiment with single projects, while the AI leaders approach implementation holistically, embedding it deeply into their business models. NTT DATA’s research highlights how the most effective organizations use AI to redesign workflows entirely rather than layering it on existing structures. Notably, these leaders tend to see sustained benefits, such as repeated financial gains and a positive cycle of reinvestment as early wins feed into further development. Such disciplined, closely managed change not only maximizes value but also mitigates potential risks, providing a template for others looking to scale their AI efforts. Organizations considering their own AI integration can benefit from setting clear priorities, ensuring governance, and aligning resources—critical factors shown to correlate with stronger outcomes and resilience as technology matures.
