AI is entering a new phase. Over the past years, organizations have focused mainly on experimenting, piloting, and building early use cases. But 2026 will be different. We are now seeing AI systems evolve from tools that simply advise to systems that can act autonomously and execute decisions within clear boundaries.
In our NTT DATA Global AI Report 2026, the organizations that embrace this evolution stand out. They are 2.5 times more likely to achieve higher revenue growth. What sets them apart is not luck — it’s the strategic choices they make.
Below, I share the six AI choices I see as defining the next stage of enterprise transformation.
1. Agentic AI: moving from prediction to execution
Until recently, AI was used mainly to detect patterns or predict outcomes. But agentic AI goes a step further: it can act within predefined guardrails.
For years, companies have used AI to detect fraud. But in 2026, AI agents will be able to pause suspicious transactions, adjust risk rules, and trigger follow-up steps —automatically.
This shift moves AI from decision support to decision execution, without removing the human from the loop. Autonomy still relies on governance, oversight, and clear objectives.
The impact is immediate: faster throughput, higher quality, and fewer manual interventions.
2. Look at the entire end-to-end process
Many organizations still automate isolated tasks because they seem easy or convenient. But real value emerges when you look at the full decision chain.
Start by identifying:
- recurring low-risk decisions
- high-volume transactions
- processes that rely on large information flows
These are ideal candidates for AI-driven scale and speed.
Our research shows that 85% of leading AI organizations already apply AI in back- and mid-office operations — exactly where scale advantages are greatest.
3. Human expertise remains central
There is still a misconception that AI will replace people. What we see in practice is the opposite: the organizations that scale AI successfully place senior experts even more at the center.
Human judgment is irreplaceable.
AI brings speed, scale, and the ability to process complexity.
People bring context, accountability, and ethical decision-making.
The strongest solutions emerge when humans and AI work together, lifting decision-making to a new level.
4. Technology is easy. Transformation is human.
The biggest challenge is not implementing technology — it’s changing how people work.
You can deploy AI within weeks. But adoption, trust, and behaviour change take time. Organizations that generate lasting value invest as much in change management and leadership as they do in technology.
If employees don’t change how they decide, collaborate, or trust AI, the transformation will not embed. This is why AI should never be treated merely as an innovation project — it must be part of a company-wide transformation program.
5. Data quality is the real bottleneck
We often assume AI problems come from model limitations, but in reality, data is the biggest blocker. Fragmented datasets, unclear ownership, and weak governance prevent organizations from scaling.
Data responsibility is shifting from IT teams to domain experts, because only they can assess accuracy within the right business context.
Strong data ownership is the foundation to maintain trust, consistency, and quality — without it, AI cannot scale.
6. Embed Data & AI governance into your operating model
Governance is often perceived as a brake on innovation. But the most advanced organizations do the opposite: they embed governance directly into their operating model.
When governance lives inside processes, platforms, and decision flows, organizations can deploy new AI use cases safely and repeatedly without requesting approvals each time.
Clear roles, automated controls, and continuous monitoring create the conditions to scale faster — not slower.
Governance becomes the accelerator for AI growth at scale.
Final thought
In 2026, success will not depend on having the best models. It will depend on:
- people and leadership
- data quality
- embedded governance
- and the willingness to adopt AI at scale
Organizations that make these strategic choices today will be the ones shaping tomorrow.
“In 2026, success will not be defined by models, but by people, data quality, governance, and the willingness to embed AI at scale.” — Bart Moens, Head of Data & Analytics Benelux & France, NTT DATA