AI leaders surge ahead in growth and profitability
New global research from NTT DATA draws a hard line between companies that treat AI as core strategy and those still experimenting. The top 15% of organisations - the "AI leaders" - are 2.5x more likely to post revenue growth above 10%. They're also more than 3x more likely to hit profit margins of at least 15% from AI initiatives.
The study covers 2,567 senior executives across 35 countries and 15 industries, from technology and manufacturing to banking, healthcare and consumer sectors. To qualify as an AI leader, firms needed a clear AI strategy, a mature operating model and strong execution.
"AI accountability now belongs in the boardroom and demands an enterprise-wide agenda," said Yutaka Sasaki, President and CEO, NTT DATA Group. "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."
What AI leaders do differently
- Strategy first: AI is embedded into core business plans, not treated as side experiments.
- Focus beats spread: They pick one or two high-value domains, redesign them end to end and move fast from concept to deployment.
- Clear line of sight to outcomes: Every AI investment maps to a strategic priority with defined business metrics.
- Flywheel effect: Early wins fund broader initiatives, compounding gains across functions.
- Rebuilt foundations: AI is integrated into core applications and systems-not just surface-level add-ons.
Execution that scales
- Infrastructure built for growth: Leaders invest in secure, scalable platforms and address data, compute and network capacity early. Some localise or relocate infrastructure to meet private or sovereign AI requirements and tighter data controls.
- People first: AI is deployed to amplify experienced, high-skill employees. Adoption is run as a company-wide change program with training, new workflows and incentives.
- Central governance: Policies for risk, ethics, compliance and performance are consistent across the enterprise. Many appoint a Chief AI Officer (CAIO) to own risk, standards and results.
- Partners tied to outcomes: Strategic collaborators are engaged across the AI value chain, often with gain-sharing tied to measurable results.
Advice from the front lines
"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," said Abhijit Dubey, CEO and CAIO, NTT DATA, Inc. "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 profits and pulling ahead of the market."
Your 90-day plan
- Choose 1-2 business domains with outsized economic upside (e.g., claims, underwriting, demand planning, collections, R&D).
- Define the value case: target revenue, margin or cost outcomes; time-to-value; risk thresholds; success metrics.
- Stand up central governance: appoint a CAIO (or equivalent), set enterprise policies and create a single AI intake and review process.
- Map processes end to end in your chosen domains; identify decisions, data and handoffs where AI can improve throughput and quality.
- Prioritise foundations: data quality, model lifecycle, observability, security and access controls.
- Right-size infrastructure: plan capacity for data pipelines, compute and networking; decide where private/sovereign requirements apply.
- Partner with intent: structure gain-sharing where providers share upside on agreed business outcomes.
- Pilot, then scale: limit scope, instrument everything, and move from proof to production in weeks-not quarters.
- Run change like a program: training, playbooks, role redesign and communication baked into the rollout.
- Operationalise measurement: weekly dashboards, owner accountability, and a review cadence with the exec team.
Metrics that matter
- Revenue lift per use case and time-to-impact.
- Margin improvement (gross and operating) tied to AI.
- Cycle time, accuracy and throughput for redesigned workflows.
- Adoption rates by role; expert productivity uplift.
- Risk incidents, model drift and compliance exceptions.
- Unit economics: cost per inference, data pipeline cost, utilization.
The CAIO mandate
- Own the portfolio: prioritize use cases against strategic goals; cut efforts that don't move the needle.
- Set the rules: enterprise policies for data, risk, ethics and model performance.
- Coordinate execution: architecture, infrastructure, vendor strategy and security aligned across teams.
- Prove outcomes: auditable impact tracking and transparent reporting to the board.
Governance resources
Benchmark and next steps
NTT DATA's 2026 Global AI Report doubles as a benchmark. Use it to compare your maturity across strategy, operating model and execution. Then pick your two biggest levers and commit resources until results show up in revenue and margin.
If you're building leadership capability across functions, explore role-based learning paths and certifications to speed up adoption: Courses by job.
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