NTT DATA

nttdata.com

NTT DATA is a technology-services provider with a broader portfolio that also includes business-process services and industry solutions. Its technology practices cover advisory work, system implementation and ongoing operations; these should be scoped separately rather than treated as interchangeable engagements. Commercial AI services include use-case and governance advisory, custom-agent engineering, model implementation and operational support. These delivery offerings are distinct from its AI platforms and published research. ^[1] ^[2] ^[3] ^[4] ^[5] ^[6]

Services and delivery

AI strategy and governance advisory

Advisory. Assesses AI opportunities, prioritizes use cases and develops business cases and roadmaps. Advisory scope includes data readiness, deployment-model and vendor selection, architecture, governance and adoption planning. ^[2]

Custom AI-agent implementation

Implementation. AI Studio designs and builds enterprise-specific agents, integrates them with existing systems and orchestrates workflows. Delivery combines domain and AI expertise with reusable agent patterns. ^[3]

AI-agent operations

Managed services. Provides ongoing monitoring, governance and refinement of custom AI solutions through AI Studio. Buyers should define operational responsibilities and support commitments separately from initial development. ^[3]

AI and machine-learning implementation

Implementation. Designs and implements AI solutions, including model deployment, intelligent automation and intelligent applications. The offering explicitly addresses AI and machine learning rather than data-platform construction alone. ^[4]

Cloud migration and architecture modernization

Implementation. Migrates and modernizes applications and infrastructure, including cloud-native and composable architecture. This is implementation work, distinct from a strategy-only assessment. ^[7]

Managed cloud operations and optimization

Managed services. Operates public and private cloud platforms and provides continuous optimization. Platform coverage and contractual responsibilities require confirmation for the proposed delivery location. ^[8]

Application development, modernization and testing

Implementation. Provides application development, application and mainframe modernization, product-centric development, and quality engineering and testing. These services address software construction and renewal, not just portfolio planning. ^[9]

Application management

Managed services. Offers application management as a separate part of its application-services portfolio. Buyers should distinguish ongoing application responsibilities from development, modernization and testing projects. ^[9]

Security operations modernization

Implementation. Modernizes security operations centers through SIEM integration and security orchestration, automation and response optimization. This implementation scope is separate from ongoing monitoring and response. ^[10]

Managed security monitoring and response

Managed services. Provides continuous monitoring and response, threat management and integrated managed security. The service portfolio also covers incident response and digital forensics. ^[10]

Data-platform engineering and analytics

Implementation. Designs and implements modern data platforms, dashboards and decision-support capabilities. This offering establishes data engineering and analytics delivery; it does not independently establish AI or machine-learning work. ^[11]

AI-native platforms and prebuilt agents

Software product. Offers ready-to-deploy platforms and prebuilt agents. The associated page also describes deployment, tuning and optimization services, but does not resolve how product licensing and delivery responsibilities are commercially packaged. ^[5]

Global AI research and executive guidance

Research. Publishes research-based guidance through the Global AI Report and playbook. This material is distinct from a consulting or implementation engagement. ^[6]

Company details

Track record

For Almirall, NTT DATA reports building a question-answering and semantic-search system using Azure OpenAI Service to extract information from medical papers, answer scientific questions and populate spreadsheets. It reports answer accuracy above 90%. The source is an announcement, not a detailed operating case study, and does not establish evaluation methodology, production scale or support terms. ^[12]

For Damm, NTT DATA describes an AI-agent proof of concept for assessing duplicate hospitality-establishment records and explaining decisions by email. Technologies included Azure AI Foundry, Azure Agent Services and geolocation tools. NTT DATA reports 97% correct decision-making and 92% reasoning accuracy. Database queries were simulated, with integration into Damm’s environment identified as a next step. ^[13]

At MASORANGE, the Microsoft 365 Copilot engagement included integration, application-specific training, a user community, champion support and impact measurement. For an initial cohort of 300 employees, NTT DATA reports 80% regular adoption and average savings of three hours per person monthly. This demonstrates implementation and adoption services, not proprietary model development or an enterprise-wide rollout. ^[14]

Scope and limitations

The supplied evidence does not establish pricing, delivery-team composition, contractual service levels or identical service availability across jurisdictions.
Case-study outcomes are NTT DATA’s reported results, not independently verified benchmarks. Damm documents a proof of concept, while MASORANGE documents an initial deployment cohort.
The MASORANGE source contains differing ROI figures; no single ROI figure is used here.
The older generative-AI infrastructure URL returned 404 and was not used as affirmative evidence.
AI platform licensing, implementation ownership and ongoing operational responsibilities require separate commercial clarification.

Questions to ask

Which legal entity and delivery team will own advisory, implementation, production integration and ongoing operations?
Can you provide references matching our industry, technology environment and intended delivery stage?
What are the acceptance criteria, evaluation datasets and security controls for the proposed AI solution?
How are platform licenses, cloud consumption, implementation fees and ongoing support charged?
Who owns project data, models, custom agents and integration code, and how can they be transferred at exit?
Which platforms, locations and support hours are covered by the proposed service levels?

Overview