The artificial intelligence (AI) race is gaining momentum, with global enterprises projected to invest up to S$26.2 trillion in AI adoption between 2024 and 2030.

According to Mr Ridhav Mahajan, director of Hewlett Packard Enterprise (Asia Pacific), successful AI deployment enables enterprises to stay agile and respond quickly to evolving market demands. “Besides enhancing effciency and reducing costs, AI can help unlock strategic growth across an enterprise over the long term,” he said.

NAVIGATING THE CHALLENGES OF AI DEPLOYMENT

Despite the urgency of scaling enterprise AI, the road to successful deployment is paved with obstacles that must first be addressed.

The complexity of integrating diverse AI tools and the need for effective data management are key barriers, noted Mr Mahajan. “Challenges include data silos, lack of standardisation, skill gaps, scalability issues and security concerns,” he said. “These are further compounded by intricate AI workflows, rapidly evolving technologies, difficulties in integration with legacy systems, and the diversity of data sources.”

He cited the example of a customer who spent over a year procuring and integrating essential AI components, such as infrastructure, storage, Kubernetes (an open-source system that manages containerised applications) and open-source generative AI tools.

Such time-consuming delays can be avoided with turnkey solutions that are pre-integrated and ready for immediate use. These solutions enable enterprises to unlock AI’s transformative potential without being burdened by technical complexities.

AI-READY DATA, WHEREVER IT RESIDES

Simplifying the complexities of deploying and scaling enterprise AI solutions requires accessible, AI-ready data and high-performing data architecture. Poorly structured data pipelines slow development cycles, reduce model accuracy and lower developer productivity due to the need for extensive manual intervention. Fragmented data environments further exacerbate these challenges by creating silos that hinder integration, analytics and insights.

HPE Ezmeral Data Fabric offers a unified data plane that enables seamless integration and access across diverse environments. “The solution’s scalability also ensures that enterprises can handle growing data volumes and complex workflows without compromising performance or agility,” said Mr Mahajan.

Particularly effective in fast-paced, data-intensive environments, HPE Ezmeral Data Fabric can shape high volumes of public and proprietary data across multi-generation IT infrastructures while maintaining data consistency.

A healthcare provider that used HPE Ezmeral Data Fabric to unify disparate patient records cut AI model training time by 40 per cent, in turn enabling faster and more accurate diagnostics.

Another key element of HPE’s AI toolbox is HPE GreenLake for File Storage, which offers scalable, high-performance storage for AI workloads.

“HPE GreenLake for File Storage provides low-latency storage capable of managing the vast datasets required for AI training and inference,” said Mr Mahajan. “Seamlessly integrated with HPE Private Cloud AI, it ensures the efficient training and deployment of models.”

FULL-STACK, TURNKEY PRIVATE CLOUD FOR AI

Developed in partnership with multinational technology company NVIDIA, HPE Private Cloud AI is a comprehensive, one-stop solution designed to simplify AI deployment, mitigate risks and boost time-to-market, all while keeping proprietary data and intellectual property private and secure.

Ready for immediate use, HPE Private Cloud AI can be deployed by HPE in an organisation’s preferred environment in just a few hours. Additionally, this solution enables the rapid deployment of popular generative AI applications such as pre-built digital assistants, in under 30 seconds.

With HPE Private Cloud AI, businesses can deploy virtual assistants capable of delivering accurate, real-time responses without compromising security or scalability. For instance, financial institutions can quickly implement digital assistants to streamline customer support, enhance engagement and reduce operational costs, all while ensuring data compliance.

FUTURE-READY FROM DAY ONE

According to Mr Mahajan, enterprises new to AI should focus on defining clear objectives and selecting manageable, high-impact use cases. HPE Private Cloud AI simplifies this process with its integrated environment and pre-configured components, expediting adoption while minimising risks.

For enterprises looking to scale AI solutions, success depends on optimising infrastructure, ensuring data access and addressing operational challenges such as governance and compliance.

At this stage of the AI journey, pre-integrated tools like HPE AI Essentials and NVIDIA AI Enterprise enable the deployment of advanced use cases, including generative AI and retrieval-augmented generation – a technique that connects generative AI models to external resources, enhancing their reliability and accuracy.

“As an end-to-end solution, HPE Private Cloud AI enables businesses to unify their data workflows, streamline model development and remain agile as their needs evolve. This empowers them to focus on achieving tangible business outcomes, driving efficiency and scaling innovation with confidence,” said Mr Mahajan.