NTT DATA, Inc.

Today’s #OperationalAI in October use case is focused on the level of autonomy in #AgenticAI deployments and a market check on enterprise progress. Agentic AI gets a lot of hype. But what happens after the pilot? Moving into production means agents need to work reliably across real processes, in real systems, and meet real governance requirements. HFS reports that 70% of autonomy is already visible in mature use cases, which gives us a sense of how quickly this space is developing. In the HFS Horizons: Agentic Services, 2026 report, NTT DATA is recognized as a Horizon 3 Market Leader. With a full-stack approach to agentic AI and expertise in 15 industries, we know how to apply orchestration and the right controls to these environments. Explore the report to see what HFS is finding as agentic AI moves toward enterprise scale. https://lnkd.in/eq6SjEva

There’s an important question that organizations with big AI ambitions need to answer: Can your applications keep up? Aging applications, technical debt and siloed data can make it harder to move from successful pilots to AI at scale. Modernization can make a measurable difference. In our experience, AI-augmented app development can deliver productivity gains of up to 50%. Moving to API-first, cloud-native applications can also improve responsiveness by up to 40%. If you’re serious about implementing AI at scale, application and data modernization needs to be part of your strategy. In this blog, I explore how you can modernize your application estate to support AI innovation with AWS. https://lnkd.in/dTxcUhGr Shashi Gupta Ryan R. #NTTDATA #modernization #AWS #CloudModernization

Fast-changing economic conditions put a premium on timely insight. International Airlines Group (IAG) worked with NTT DATA to develop an AI-powered news agent that helps leadership monitor macroeconomic developments, identify emerging trends and respond more effectively to changing conditions. IAG describes the shift as moving from a reactive approach toward more proactive decision-making. That’s a practical use of AI that I think has real relevance for leaders navigating volatility. Play the video to see how IAG is turning market intelligence into action. https://lnkd.in/gKqdR3xP

For years, the insurance industry has focused on becoming more data-driven. But while data provides intelligence, it doesn’t guarantee execution. Agentic AI—specifically the neuro-symbolic layer—changes this. It bridges the gap by converting intelligence into real-time action, continuously improving to drive exponential business outcomes. What does this look like in practice? For Underwriters: It means dynamically refining underwriting guidelines in response to real-time loss experience, exposure concentrations, or shifts in UW strategy. For Claims Adjusters: It means safely scaling autonomy while maintaining strict governance through payment authority thresholds, automated audits, and robust evidence capture. For industry leaders, this represents a massive paradigm shift. The next competitive advantage wont come from who generates the most insights, but from who can turn that intelligence into action at scale. How is your organization approaching this shift? https://lnkd.in/guTfaFqs #Insurance#Insurance #AgenticAI #InsuranceInnovation

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