DXC Technology

dxc.com

DXC is primarily an enterprise technology services provider rather than a research firm or software-only vendor. Buyers can engage it for technology planning, discrete implementation projects or ongoing application and infrastructure management. Its Enterprise Intelligence offering explicitly includes building, deploying and operating generative AI, while its application services are available as separate service lines. Procurement should distinguish advisory deliverables, implementation responsibility, operational support and product licensing rather than assume that every engagement includes all four. ^[1] ^[2] ^[3] ^[4] ^[5]

Services and delivery

Technology strategy and digital operating-model advisory

Advisory. Advises on aligning technology investment with business priorities and designing scalable digital operating models. This is planning and organizational design, not an automatic commitment to implement systems. ^[1]

Enterprise generative AI implementation

Implementation. Builds and deploys retrieval-augmented generation, inference, vector databases, model fine-tuning, chatbots, conversational AI, APIs and AI applications. The offering also includes model training and AI governance. ^[2]

Managed AI operations

Managed services. Provides ITIL-aligned, round-the-clock management across AI software layers, including MLOps, LLMOps and ongoing use-case development. ^[2]

Data engineering

Implementation. Delivers data lakes, warehouses, pipelines, metadata cataloguing and data products. This data-platform work is distinct from the separately documented AI implementation service. ^[2]

Private AI deployment

Implementation. Executes private AI deployments, from data environments through workload deployment, using on-premises infrastructure. Buyers should establish its contractual relationship to Enterprise Intelligence. ^[6]

Agentic security operations

Managed services. Operates a 7AI-powered managed security service covering alert categorization and enrichment, threat investigation, incident lifecycle management and policy-governed response. Customer-defined authority boundaries and human escalation constrain automated decisions. ^[7]

Software development

Implementation. Builds cloud-native, API-first microservices applications using DevSecOps. References to AI-assisted engineering do not establish that every application engagement delivers an AI system. ^[3]

Application modernization

Implementation. Modernizes existing applications through rehosting, replatforming and rearchitecting, with middleware and database modernization included in the documented scope. ^[3]

Application quality assurance

Implementation. Provides functional, performance and security testing, including Testing-as-a-Service, as a separately available application service. ^[3]

Managed application services

Managed services. Provides ongoing application support and operational improvement rather than only project-based development or modernization. ^[3]

Cloud strategy and architecture advisory

Advisory. Provides cloud strategy and architecture planning as the advisory component of a broader cloud lifecycle offering. ^[4]

Cloud migration

Implementation. Implements migrations involving AWS, Azure, Google Cloud and Oracle Cloud. Migration delivery is separately documented from cloud planning and ongoing operations. ^[4]

Cloud and infrastructure operations

Managed services. Manages cloud environments and hybrid infrastructure, including hosting, networking, mainframe operations, Kubernetes operations and managed SAP platforms. ^[4]

SAP modernization

Implementation. Provides Fast RISE migration services to move legacy SAP environments to RISE with SAP S/4HANA. ^[8]

SAP application management

Managed services. Provides ongoing Enterprise Applications Management for SAP, distinct from migration implementation and software resale. ^[8]

Assure Answers insurance software

Software product. An AI product for insurance-document summaries, conversational queries and knowledge access. Product functionality alone does not establish an included implementation or managed-service engagement. ^[5]

Company details

Technology and platforms

Enterprise Intelligence identifies NVIDIA NIM as its primary inference engine. Its AI delivery scope also includes vector databases, APIs and model operations. The cloud practice documents AWS, Azure, Google Cloud and Oracle Cloud, while the SAP practice specifically addresses RISE with SAP S/4HANA. ^[2] ^[4] ^[8]

Track record

For Syngenta, DXC reports developing an AWS-native invoice and tax-processing solution combining OCR with generative AI. It extracts tax data, matches information across systems and presents dashboards. DXC reports more than 90% accuracy in tax bookings. Additional SAP tax-entry and regulatory-summary applications were described as in development, not completed. ^[9]

For an unnamed North American insurer, DXC reports migrating mainframe applications to containerized Micro Focus Enterprise Server workloads on AWS. Work included RESTful APIs, IBM Db2 migration, migration rehearsals, disaster-recovery exercises and subsequent management. DXC reports an eight-month migration and a 40% operating-cost reduction; these are case-specific vendor-reported outcomes. ^[10]

Scope and limitations

The supplied sources establish commercial technology and AI delivery, but do not establish pricing, contractual service levels, staffing commitments or acceptance criteria.
Case-study outcomes are DXC-reported rather than independently validated. The unnamed insurer limits direct reference checking.
Private AI has less technical detail in the supplied notes than Enterprise Intelligence; their scope and contractual overlap require clarification.
AI surveys and product functionality are not treated as evidence of implementation services. The Sardinia case is not used as proof of completed automation where the notes describe prospective work.

Questions to ask

Which advisory, implementation and operational responsibilities will DXC own, and which remain with our team or third parties?
Can DXC provide named references matching our proposed AI use case, deployment model and regulatory requirements?
Which models, infrastructure and third-party licenses are required, and who pays for and manages them?
What data residency, retention, training-use and intellectual-property terms apply?
Who owns integration, model evaluation, production monitoring, incident response and ongoing model changes?
What acceptance criteria, support service levels, transition assistance and exit provisions will be contractual?

Overview