The conversation about Artificial Intelligence (AI) has changed tone. It’s no longer just about flashy proof-of-concepts or the latest generative AI demo, but rather a much more uncomfortable question: who is turning AI into money — and who is stuck in unreturnable pilots?
The NTT DATA Global AI Report 2026 captures this inflection point with a focus closer to a “benchmark” than a slogan. Its core thesis is clear: the boundaries between technology strategy and business strategy are blurring until they converge into a single integrated vision, where AI ceases to be just an enabler and begins to behave like a “business operating system.”
Only 15% stand out from the rest — and the numbers reveal it
The study is based on a global survey of 2,567 senior executives across 35 countries and 15 industries, conducted between September and October 2025. Of this pool, NTT DATA classifies the 15% (397 organizations) as “AI leaders,” meeting three conditions: a defined or actively implementing strategy, “mature” or “evolved” maturity, and significantly higher benefits attributable to AI.
The comparison with other companies is particularly interesting to tech media because it quantifies a phenomenon many had suspected:
- Growth: 62.8% of leaders report revenue growth over 10%, compared to only 25.3% of others.
- Profitability: 33.8% of leaders report profit margins of 15% or higher, compared to 9.4% of the rest; this translates to 3.6 times higher likelihood of operating within that margin range.
This isn’t a marginal difference — it’s a structural gap. And according to the report, it’s not just about “having AI,” but how it is governed, where it is applied, and with what architecture it is scaled.
The recipe isn’t “more AI”: it’s AI aligned with business and rapid execution
One of the most repeated ideas over the past two years — “align AI with business” — often remains a mantra. Here, it’s backed with metrics:
- Among organizations with fully aligned strategies, 83.6% report profit increases of 5% or more linked to AI in the last fiscal year.
- In those with partial alignment, the figure drops to 77%; and in non-aligned companies, it falls further to 58%.
The second key factor is attitude toward risk: leaders tend to experiment early and avoid “waiting and seeing.” An 46.1% explicitly state they seek to “move fast and lead the market,” compared to only 25.4% of laggards.
Practically, the report suggests that competitive advantage is gained when AI stops being an added layer and is integrated into high-impact decisions (pricing, service, execution capability), which are now no longer just decided in IT but in executive committees.
From GenAI to Agentic AI: the leap from creation to execution
The report introduces a distinction gaining prominence in product teams by 2026: GenAI as a “creator” (content, ideas, dialogue) and Agentic AI as an “executor” (autonomous systems focused on results that act, iterate, and optimize in closed loops).
The implications for companies and administrations are significant: if GenAI has improved productivity “on the screen,” agentic AI aims to improve it in the flow, automating repeatable decisions and connecting tasks across systems. However, this raises the bar for architecture: observability, control, recovery, and governance are no longer “nice-to-haves.”
“AI-native”: when architecture is designed to adapt (not just to scale)
The report proposes the concept of an AI-native organization, differentiating it from cloud-native. While cloud-native emphasizes elasticity and scalability, AI-native focuses on adaptability and autonomy, incorporating reasoning, feedback, and self-correction in layered structures.
Put simply: connecting a model to a process isn’t enough to truly scale. To achieve true scale, processes must be redesigned “from end to end” and foundations reinforced (data, identity, networks, pipelines, security). The report provides a numerical example of this philosophy:
- Leaders use AI to support front-office interactions (marketing, sales, customer service) in 73.3% of cases, and back/mid-office in 85.6%.
- Laggards, meanwhile, are at 44% (front) and 71.1% (back/mid).
This pattern suggests that leaders view AI not as a departmental tool, but as a transversal capability.
The real weak point: infrastructure, technical debt, and data sovereignty
As geopolitics and regulation influence where data and compute can reside, the report highlights the shift toward private and sovereign AI as a strategic response. It defines sovereign AI as one constrained by jurisdictional borders and regulatory frameworks; and private AI as an organizational decision driven by data sensitivity, intellectual property, or the economy of “owning” versus “renting” infrastructure.
Concerns about privacy and sovereignty across multiple geographies emerge as the top governance issue (59.4%) among leaders, surpassing others (54.5%) and laggards (49.6%).
The other silent obstacle is technical debt: for leaders, the biggest blocker for infrastructure is “high maintenance needs” (29.5%), far above other groups. In other words: many strategies are halted not by a lack of ideas, but by fragile platforms, disconnected tools, and inconsistent controls, which drain critical resources and slow down progress from pilot to production.
Governance and leadership: the rise of the CAIO and AI as a corporate discipline
As systems become more autonomous, the report describes an organizational convergence: committees, roles, and functions to standardize decisions and “artifacts” (policies, evaluations, audits). In leaders:
- 55.9% follow a centralized governance model (compared to 37.6% of the rest).
- 56.2% have an AI steering committee with executive sponsorship and involvement from legal, security, and compliance areas.
- 77.8% have a dedicated Chief AI Officer (CAIO).
The report also suggests that the CAIO acts as an orchestrator figure: aligning investments with results and risk tolerance, integrating AI with observability and cost discipline, and translating economic and cultural implications.
Practical conclusion: platforms, not pilots
The report emphasizes a key point that in 2026 has become a criterion of maturity: an organization cannot scale what it cannot govern. In tech terms, this means building unified platforms with routing of models, safeguards, logs, retention, access controls, and observability of metrics such as latency, inference cost, or model/agent deviation.
In an environment where generative AI has become mainstream and agentic AI is beginning to automate decisions, the difference between leading or falling behind isn’t just about “trying more,” but about focusing on one or two high-value domains and redesigning them end-to-end, supported by robust governance, modern infrastructure, and results-oriented partnerships.
Frequently Asked Questions
What sets apart a “leader” in Artificial Intelligence from a company that only runs pilots?
maturity is reflected in three signals: a clear strategy, scalable operation (governance and platform), and measurable benefits. In the study, leaders show significantly higher growth and margin rates and tend to centralize governance and professionalize roles like the CAIO.
What is Agentic AI and why does it concern technology and compliance leaders?
These are systems that not only generate content but perform tasks and optimize results in closed loops. As autonomy grows, so does the need for supervision, traceability, continuous evaluation, incident management, and cost control.
Why is there so much talk about sovereign and private AI in 2026?
Because data residency, jurisdiction, and infrastructure control have become strategic variables. The report describes sovereign AI as a regulatory/geopolitical response, and private AI as an organizational choice driven by data sensitivity, intellectual property, and economic efficiency.
What is the most common mistake when trying to scale AI in an organization?
Undervaluing technical debt and infrastructure: disconnected tools, weak pipelines, fragile networks and identities, and high maintenance costs drain critical resources and prevent moving from pilot to production.
via: NTT Data Peru