From cloud-ready to AI-ready: The new modernization mandate

Modernization has been a mantra for organizations for the past decade. From migrating workloads to the cloud to embracing digital-first operations, they have invested heavily in building scalable, agile foundations.

But the game has changed. As noted in a new NTT DATA guide, Modernization 2.0: How agentic AI is changing the game, cloud-native modernization — reconfiguring legacy systems into microservices — is no longer the end goal. Rather, it’s a step toward building intelligent platforms that enable agentic workflows.

Therefore, if your strategy stops at cloud migration, you’re only halfway there. The next leap forward is about creating adaptive, intelligent ecosystems that transform how you operate and compete.

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From cloud-first to AI-first

The first wave of modernization delivered cloud-native infrastructure, unified data platforms and real-time analytics. These moves cut costs, improved scalability, boosted efficiency and productivity, and enabled a faster pace of innovation. Organizations became more agile as teams could deploy updates faster and make data-driven decisions in real time, laying the groundwork for the next phase of intelligent transformation.

Today, however, your organization needs more than automation. You also need to add intelligence, in the form of agentic AI — which takes modernization beyond efficiency with systems that learn, adapt and make decisions in context.

Here’s how it plays out across five key sectors:

• Banking, financial services and insurance: Compliance, fraud detection and customer trust are at the heart of this highly regulated industry. Data and application modernization enable real-time risk monitoring, AI-driven underwriting and personalized financial services. Agentic AI elevates this by automating routine operations and predicting potential failures before they occur.
• Manufacturing: Factories are becoming intelligent ecosystems. Modernized infrastructure and IoT data platforms enable predictive maintenance and dynamic production scheduling. Agentic AI takes it one step further by optimizing energy use, balancing supply chains, automating quality control and more — all in real time.
• Healthcare: In this industry, a big challenge is data interoperability — making patient data secure, accessible and useful. Modernized data foundations support AI-powered diagnostics and personalized care journeys. AI agents can even autonomously coordinate scheduling, monitor patient progress and alert clinicians to anomalies.
• Retail and consumer: This is where customer-experience transformation shines. From omnichannel engagement to personalized recommendations, retailers are building dashboards that unify every interaction. Then, agentic AI turns these insights into action by triggering inventory updates, tailoring offers and predicting churn before it happens.
• Automotive: The industry is racing toward connected, software-defined vehicles. Modernization enables digital sales journeys, mobility as a service, and self-updating vehicle ecosystems. Agentic AI adds predictive maintenance and autonomous decisioning within the car itself, turning every vehicle into an intelligent, adaptive system.

How to lower the barriers to modernization and agentic AI adoption

Despite the promise, scaling agentic AI isn’t simple. Without a unified data foundation and a clear roadmap that aligns your technology and business goals, even the most advanced AI agents will struggle to deliver valuable outcomes.

Our modernization guide sets out three main challenges to overcome before you can start deploying agentic AI — or any AI — at scale:

1. The foundational challenge: Managing a large legacy footprint

You can’t access agentic AI’s full potential when you’re still weighed down by outdated infrastructure and fragmented systems. Many organizations still operate with a heavy legacy footprint that limits scalability, agility and integration with AI capabilities.

The solution begins with modernizing workloads with cloud-native environments that support microservices, automation and seamless data accessibility.

2. The philosophical challenge: Reimagining the role of AI

Agentic AI isn’t just an automation tool — it’s a transformative force that upends entire business processes, decision-making frameworks and value chains.

Using cloud-native architectures, serverless technologies and edge intelligence, bundled with prebuilt industry Service as a Software solutions to speed up deployment and innovation, turning AI into a strategic partner rather than a supporting function.

3. The operational challenge: Demonstrating real value

The biggest hurdle in scaling agentic AI lies in defining and proving measurable impact. Many organizations stall at the pilot phase due to unclear metrics or fragmented data.

Success demands KPIs and SLAs for AI agents that directly tie to business outcomes, supported by unified, high-quality data foundations.

A fast journey to the next frontier

Here’s the good news: You don’t need to overhaul everything at once. Start with these steps:

1. Assess readiness: Evaluate your infrastructure, data quality and integration points.
2. Target quick wins: Identify high-impact use cases, such as customer service or supply chain optimization.
3. Leverage accelerators: Industry platforms and prebuilt AI agents can fast-track deployment. Partnering with experts like NTT DATA and Google can help you move from proof of concept to measurable value in months, not years.

Agentic AI is the next frontier of modernization. Organizations that embrace it now will gain a decisive advantage in efficiency, innovation and customer experience.

Ready to explore what agentic AI can do for your business? Let us help you make it a reality.

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