Artificial Intelligence is no longer limited to experimentation. Enterprises around the world are now looking for reliable, scalable, and production-ready AI solutions that deliver measurable business value. To meet this growing demand, NTT DATA and NVIDIA have introduced a new enterprise AI factory model designed to help organisations move from AI pilots to full-scale production environments.
This initiative combines NVIDIA’s GPU-accelerated computing, high-performance networking, and AI software ecosystem with NTT DATA’s enterprise integration expertise. The result is a complete AI platform that allows companies to build, train, deploy, and manage AI applications across cloud, on-premise, and edge environments.
The new AI factory approach aims to solve one of the biggest challenges in enterprise AI — the gap between proof-of-concept projects and real-world deployment.
The Need for Production-Ready Enterprise AI Platforms
Many organisations have invested heavily in artificial intelligence over the past few years. However, a large percentage of AI projects never move beyond the pilot stage. Companies often struggle with infrastructure limitations, lack of integration, high costs, and governance issues when trying to scale AI solutions.
This is where the enterprise AI factory concept comes into play.
NTT DATA’s new initiative focuses on delivering a repeatable and standardised architecture that allows organisations to build AI systems faster while maintaining security, compliance, and performance.
According to Abhijit Dubey, CEO of NTT DATA, businesses are changing the way they approach AI deployment. Instead of running isolated experiments, enterprises now want structured platforms that can deliver real returns from day one.
By integrating NVIDIA technologies into enterprise AI factories, organisations can get a secure and powerful environment where AI applications can be developed, tested, and deployed with predictable outcomes.
What Is an Enterprise AI Factory?
An enterprise AI factory is a complete technology stack designed to support the entire AI lifecycle. It includes hardware, software, networking, data pipelines, and governance tools required to develop and run AI at scale.
The AI factory model introduced by NTT DATA and NVIDIA provides:
- GPU-accelerated computing for high-performance workloads
- AI software frameworks for model development
- Pre-configured microservices for deployment
- Secure infrastructure for enterprise use
- Standardised workflows for repeatable results
This approach allows organisations to avoid building AI infrastructure from scratch every time they start a new project.
Instead, they can use a proven architecture that reduces development time, lowers costs, and ensures consistent performance.
NVIDIA Technology at the Core of the AI Factory
The platform is powered by NVIDIA’s advanced computing and AI software ecosystem. NVIDIA GPUs are widely used for machine learning, deep learning, and high-performance computing because of their ability to process massive amounts of data quickly.
The enterprise AI factory integrates several NVIDIA technologies, including:
- GPU-accelerated servers for AI training
- High-speed networking for data transfer
- AI Enterprise software suite
- NeMo framework for agentic AI
- NIM microservices for deployment
These components work together to create a full-stack AI platform capable of handling complex enterprise workloads.
Full AI Lifecycle Support
One of the main advantages of the new AI factory model is that it supports the entire AI lifecycle inside a governed framework.
This includes:
- Data preparation
- Model training
- Model testing
- Application development
- Deployment
- Monitoring and optimisation
By covering every stage, the platform ensures that AI projects can move smoothly from idea to production without delays or compatibility issues.
Many companies fail to scale AI because their tools are not designed to work together. The enterprise AI factory solves this problem by providing a unified architecture.
Solving the Gap Between Pilot and Production
A common issue in enterprise AI is the difficulty of converting a successful prototype into a production-ready system. This gap often leads to wasted investments and slow adoption.
NTT DATA says its AI factory model is specifically designed to eliminate this problem.
The platform standardises infrastructure and workflows, making it easier to move from proof-of-concept to real-world deployment. This reduces both the time and cost required to launch AI applications.
With a repeatable architecture, organisations can deploy new AI solutions much faster than before.
Real-World Examples of Enterprise AI Factories
Several early adopters are already using the enterprise AI factory approach in real production environments. These examples show how the model works across different industries.
Healthcare and Cancer Research
A leading cancer research hospital has deployed NVIDIA HGX platforms with support from NTT DATA and Dell Technologies.
The system is used for:
- Advanced radiology analysis
- Faster model testing
- AI-driven clinical research
- High-performance medical imaging
By using GPU-accelerated computing, the hospital can evaluate complex medical data much faster than traditional systems.
This helps researchers develop new treatments and improve patient care.
Automotive Manufacturing
In the automotive sector, a global parts supplier has implemented the AI factory architecture to improve production planning.
The company first validated its workloads on bare-metal infrastructure. After testing, the system was scaled using NVIDIA-powered AI factory architecture.
This allowed the manufacturer to:
- Reduce production setup time
- Improve quality control
- Optimise manufacturing processes
- Lower operational costs
The ability to simulate and test workloads before deployment helped the company avoid expensive mistakes.
Technology Manufacturing and Battery Production
A US-based technology manufacturer is using NVIDIA-accelerated simulation and 3D visualisation to design a next-generation battery production line.
Instead of building the factory first, the company created a virtual simulation environment using the AI factory platform.
This allowed engineers to:
- Test production workflows
- Identify design issues
- Optimise performance
- Reduce risk before physical deployment
Such simulations save both time and money while improving efficiency.
Domain-Specific AI Factory Solutions
NTT DATA is positioning enterprise AI factories as industry-specific solutions rather than generic platforms.
Different industries have different requirements, such as:
- Healthcare compliance rules
- Manufacturing automation needs
- Financial data security
- Retail analytics systems
The AI factory model uses NVIDIA infrastructure as the base layer while allowing customisation for each sector.
This makes it easier for organisations to adopt AI without redesigning their systems.
Role of NVIDIA NeMo in Agentic AI
One of the key components of the platform is NVIDIA NeMo.
NeMo is a software framework designed to build agentic AI systems. Agentic AI refers to AI models that can act independently, make decisions, and perform tasks automatically.
With NeMo, developers can create AI agents that can:
- Understand language
- Analyse data
- Automate workflows
- Interact with applications
Because NeMo runs on GPU-accelerated infrastructure, it can handle large-scale enterprise workloads.
NVIDIA NIM Microservices for Faster Deployment
Another important part of the AI factory stack is NVIDIA NIM Microservices.
These are pre-built containers that include optimised AI models and APIs.
Using NIM microservices, organisations can deploy AI applications quickly without building everything from scratch.
Benefits include:
- Faster development time
- Reduced complexity
- Better performance
- Easy integration with enterprise software
Together, NeMo and NIM form a production-ready AI platform that can be used across industries.
Pre-Built Generative AI Prototypes
To make adoption even easier, NTT DATA is offering pre-qualified generative AI prototypes built on the enterprise AI factory stack.
These prototypes help organisations start quickly without spending months on setup.
Companies can customise these prototypes for their specific needs, such as:
- Customer support automation
- Predictive analytics
- Document processing
- Supply chain optimisation
This approach reduces the time needed to see real business value from AI.
NVIDIA’s View on Enterprise AI Scaling
According to John Fanelli, Vice President of Enterprise Software at NVIDIA, businesses are now looking for platforms that can take AI from experiments to full-scale production.
He explained that organisations need reliable, scalable, and secure systems to make AI investments successful.
NTT DATA’s AI factory model provides domain-specific solutions built on NVIDIA’s AI infrastructure, making it easier for enterprises to deploy production-grade AI.
NTT DATA’s Unique Position in the NVIDIA Ecosystem
NTT DATA says it is the only global IT services provider that participates in all three major NVIDIA partner programmes.
These include:
- Solution Provider
- Cloud Partner
- Global System Integrator Partner Network
This allows NTT DATA to deliver complete AI solutions, from infrastructure to software to integration.
Because of this partnership, organisations can get end-to-end support when building AI systems.
Why Enterprises Are Under Pressure to Show AI ROI
Companies have invested billions of dollars in artificial intelligence, but many executives are now asking for measurable results.
Businesses want AI projects that deliver:
- Higher efficiency
- Lower costs
- Better decision-making
- Faster innovation
The enterprise AI factory model helps organisations achieve these goals by providing a structured approach to AI deployment.
Instead of running isolated experiments, companies can build scalable systems that generate real business value.
Importance of Governance and Security in AI
As AI adoption grows, governance and security are becoming critical.
Enterprises must ensure that AI systems:
- Follow regulatory rules
- Protect sensitive data
- Provide accurate results
- Avoid bias and errors
The AI factory platform includes governance tools that help organisations manage these risks.
This is especially important in industries like healthcare, finance, and manufacturing.
Future of Enterprise AI Factories
The launch of enterprise AI factories marks a shift in how companies will adopt artificial intelligence in the future.
Instead of building custom infrastructure for every project, organisations will use standardised AI platforms that can be reused across departments.
This will lead to:
- Faster innovation
- Lower costs
- Better performance
- Wider AI adoption
As AI becomes essential for business operations, production-ready platforms like the one from NTT DATA and NVIDIA will play a major role.
Conclusion
NTT DATA and NVIDIA’s enterprise AI factory initiative represents a major step toward making artificial intelligence practical for real-world business use.
By combining GPU-accelerated computing, advanced AI software, and enterprise integration, the new platform provides a repeatable and scalable way to deploy AI.
From healthcare and manufacturing to technology and research, organisations can use the AI factory model to move from pilot projects to full production systems with confidence.
As companies continue to demand measurable returns from AI investments, production-ready platforms like enterprise AI factories are likely to become the standard for the next generation of digital transformation.