Reimagining AI: Building applications and data on AWS

Have you launched AI pilots in your organization, only to see very few of them translate into enterprise-wide value?

You’re not alone. In Cloud-led innovation in the era of AI: The new rules for driving value with cloud, a survey of more than 2,300 senior decision-makers in 33 countries, we found that just 14% of organizations have reached the highest level of cloud maturity after nearly two decades of cloud adoption. Meanwhile, 88% say current cloud investment levels are putting AI, cloud-native development and modernization initiatives at risk.

And, when it comes to scaling AI to production, moving from a successful pilot to enterprise-wide deployment depends on the cloud infrastructure, applications and data around it.

Decades of technical debt, aging applications and siloed data are slowing modernization and starving AI of the trusted information it depends on. Gartner^® predicts that “through 2026, organizations will abandon 60% of AI projects unsupported by AI-ready data.”*

As the widening gap between AI ambition and operational reality becomes a competitive risk, the answer is a connected foundation that modernizes the digital estate, industrializes trusted data and AI, and scales agentic operations with confidence.

We build that foundation with Amazon Web Services (AWS).

• ALSO READ → The cloud conversation has changed. Has your AI strategy?

A partnership for the agentic era

As an AWS Premier Tier Services Partner and a launch partner for the AWS Agentic AI Competency, NTT DATA sits at the intersection of modernization, data and AI. Our recent Strategic Collaboration Agreement is aimed at helping organizations across industries modernize legacy systems and adopt agentic AI responsibly.

Agentic AI is where the work is heading. Amazon Bedrock AgentCore is a platform for building and operating production AI agents across frameworks and models and securing them at scale.

You can build a useful agent or prototype relatively fast. What slows you down is connecting it to enterprise systems, giving it controlled access to tools and data, evaluating behavior, operating it reliably and improving it over time. AgentCore handles the heavy lifting that slows you down. Our role is to help you use this capability at scale with the governed operating model, engineering discipline and delivery capacity needed for complex enterprises.

Modern applications: From technical debt to an AI-ready estate

A practical foundation for AI starts with the systems and data you already have. We help you modernize applications, reduce technical debt, connect fragmented data and improve software development with AI.

In our experience, AI-augmented development drives up to 50% productivity gains in application modernization, and our industrialized modernization factory approach delivers up to 40% faster time to market.

The architectural shift is also important. Moving from monolithic systems to API-first, cloud-native applications accelerates release cycles by 25% to 50% and improves application responsiveness by up to 40%.

These gains are the prerequisites for AI that can reason within live, connected systems.

Trusted data is the bedrock of AI

AI systems need reliable, governed data to produce reliable results. We help you put governed, AI-ready data in place to put the right information in the right hands.

Our Industry Composable Service Engine uses more than 500 reusable accelerators that shorten delivery timelines at a lower cost than building them from scratch. Our data engineering and democratization work help make governed data accessible to business users, rather than sitting in silos.

AI Studio: From ideation to production

In addition, our AI Studio operating and delivery model makes AI development repeatable. It moves AI opportunities through a governed route from initial concept to production, followed by continuous management to realize and maintain value.

The model is simple:

• Assess: Decide where AI can make the biggest difference. We help connect business priorities to practical opportunities, so you can turn ambiguous ideas into delivery-ready work.
• Build: We engineer secure, integrated and supportable AI solutions using common architecture, responsible AI, testing and evaluation standards.
• Manage: Keep AI delivering value over time. We monitor service performance, model and agent quality, incidents, adoption, consumption and cost.

The agentic enterprise in action

The impact is easier to see in practice. In the following examples, we deployed AI to resolve operational complaints by connecting modern applications, trusted data and AWS-native agentic capabilities:

• At International Airlines Group (IAG), the parent company of British Airways, executives spent hours synthesizing fragmented news across more than 88 aviation sources while fuel-price volatility and geopolitical shifts demanded immediate responses. We built a Market News Agent on Amazon Bedrock and Amazon Bedrock AgentCore that provides analyst-grade reports to 280 senior leaders, resulting in reduced research dependency by 40% to 60%, while the underlying Agent Factory became a reusable foundation for future AI use cases.
• SERGAS, the public health service of Galicia, Spain, was experiencing challenges in advancing clinical research due to fragmented patient data, disconnected systems and manual processes. We partnered with AWS to develop Innovatrial — a secure, AWS-based clinical research platform that consolidates data and automates research workflows. AI-powered patient cohort identification reduces patient recruitment from weeks to minutes. The solution supports faster, more efficient clinical trials and strengthens collaboration among researchers, healthcare providers and regulators.

Close the gap for enterprise-wide value

Scaling AI takes more than another pilot. It requires the cloud maturity to support AI across your organization, with modern applications, connected and trusted data, and a consistent way to govern and scale AI in production.

Only by strengthening that cloud foundation can you move beyond isolated experiments and turn AI into sustained enterprise-wide value.

• ALSO READ → In 2026, AI strategies will fail if cloud strategies lag behind

* Gartner Press Release. Lack of AI-Ready Data Puts AI Projects at Risk. 26 February 2025.       

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