Laying the digital foundation for scalable, secure and sustainable GenAI adoption across enterprises.
A recent survey revealed that GenAI is witnessing rapid adoption across almost all business functions. Especially marketing, sales, customer service, cybersecurity, ITOps and product development teams are leading in GenAI integration, with usage ranging from 60% to 70%. Across industries, enterprises are investing in off-the-shelf GenAI applications, with more than 46% also building on top of pre-existing models or developing their own LLMs from scratch. In terms of ROI, GenAI is projected to contribute $3-4 trillion to the global economy annually.
Unsurprisingly, 87% of organizations plan to increase their investment in this technology by 2025. However, there is more to the story.
While various reports highlight the rapid rise in enterprise adoption of Generative AI, they often overlook a critical element—the cognitive infrastructure. This foundational framework provides a structured roadmap for organizations to effectively integrate GenAI into their unique business contexts, ensuring they unlock its full potential and drive meaningful outcomes.
To address the organizational requirement of GenAI Infrastructure, we have been working with our ecosystem partners to create a blueprint for scalable and secure GenAI Infrastructure.
This is why we have charted the blueprint for cognitive infrastructure for enterprise-wide GenAI integration in collaboration with IBM®.
This article highlights how the HCLTech-IBM® synergy overcomes traditional infrastructure limitations and lays a resilient foundation for various enterprise GenAI initiatives.
Cognitive Infrastructure to optimize GenAI
Simply scaling up or upgrading the significant constituents of your digital infrastructure – storage, processor, OS and networking – is insufficient for GenAI integration. The infrastructure must be purpose-built to support cognitive processes, their massive computational requirements and an astronomical amount of data supply.
The key differentiators of a cognitive infrastructure include:
Specialized processors
GenAI performance depends on advanced CPUs, GPUs, TPUs and neuromorphic chips, which are essential for computing and inferencing unstructured data. Traditional infrastructure is not built to handle such extensive parallel processing demands.
Scalable storage
GenAI deployment necessitates optimized data pipelines and distributed storage. Efficient storage, accessibility and management of petabytes of data are not possible without customized storage solutions.
Dynamic resource provisioning
Unlike traditional infrastructure, where resources are allocated according to a pre-defined rule, GenAI requires intelligent and dynamic resource allocation based on workload fluctuations.
Resilient networking
Extremely high bandwidth and ultra-low latency are critical for GenAI to infer from unstructured data. Besides, such network redundancy is also crucial for LLM training, without which a model may generate untrue and unexplainable responses.
Foolproof security
Data privacy and security have always been organizations' top priorities. Since GenAI learns and infers from both structured and unstructured data, the need for watertight protection has escalated critically. Cognitive infrastructure delivers custom-built, uncompromising security tailored to the demands of GenAI deployments.
Stringent monitoring
AI models are highly susceptible to drifts and hallucinations, even more than we can anticipate. Letting the guard down even momentarily is not an option when it comes to training and using GenAI. Cognitive infrastructure is architected to facilitate the constant and rigorous monitoring needed to ensure that GenAI outcomes are factual and explicable.
These resources and capabilities are prerequisites for harnessing the true potential of GenAI. However, the specifics of cognitive infrastructure for your GenAI ambitions vary with your immediate deployment objectives, long-term business goals and pre-existing technology ecosystem.
Getting Cognitive Infrastructure right
The need for cognitive infrastructure is evident, but getting it right is no mean feat. Two-thirds of organizations, 67% to be precise, cannot turn even 50% of their GenAI pilots into live projects. One of the reasons for such a poor pilot-to-rollout ratio is inadequate expertise in all the relevant technology domains. To overcome this and propel their GenAI aspirations to fruition, organizations are seeking synergies with domain specialists, such as HCLTech. In fact, according to McKinsey, outsourced AI services are expected to increase in double digits over the next three to five years.
Advisory services
A third party comes with a clean slate and gets a bird’s-eye view of your selected use case and how your existing infrastructure stacks up against it. A crucial aspect of such consulting is addressing the buy vs. build dilemma—whether to go for on-premises or cloud deployment. Depending on your business objectives, existing infrastructure, budget and deployment deadline, the best approach could be either of the two or a mix of both, i.e., hybrid.
Infrastructure build
A system integrator that truly specializes in AI will be able to build the cognitive infrastructure from the ground up for your GenAI use case as well as scale it as and when required. The same goes for developing and deploying your preferred GenAI platform.
Managed services
Ongoing support, especially for operating and maintaining complex technologies, is crucial for sustaining maximum benefits while minimizing expenditure. For GenAI projects, managed services include onsite/offsite support for cognitive infrastructure lifecycle management, platform management, availability and performance monitoring, AI/MLOps observability and more.
Besides these must-haves, the partner ecosystem of the technology service provider being considered is also crucial. Your system integrator’s synergy with industry leaders such as AMD, AWS, Cisco, Google, IBM®, Intel, Microsoft, NVIDIA, Red Hat and VMware, etc., will also reflect in terms of product quality and cost benefits.
The HCLTech distinction: Cognitive Infrastructure Services powered by IBM watsonx®
HCLTech has a comprehensive approach to integrating GenAI. Owing to our decades of experience orchestrating emerging and prevalent technologies, we know that a robust foundation is imperative to optimally leverage any technology.
Built with IBM watsonx®, HCLTech Cognitive Infrastructure Services provides a versatile and scalable MLOps/LLMOps platform for developing, implementing and managing your GenAI systems and applications. Harnessing the full stack of the IBM® solution – watsonx.ai®, watsonx.data® and watsonx.governance®– our cognitive infrastructure service offers:
- Pre-trained, purpose-built, production-grade AI models
- Data lake house architecture leveraging IBM and open-source resources
- Ethical, legal and regulatory governance for the entire AI lifecycle
In addition to the end-to-end GenAI services – from discovery to post-deployment – HCLTech differentiators include faster AI app production, out-of-the-box offerings for organizations of all sizes, edge and data center readiness and ultra-scalable architecture. This confluence of deep domain expertise and comprehensive cognitive services makes HCLTech the partner of choice for many industry leaders.
IBM® Fusion HCI
HCLTech has validated and assisted organizations in adopting IBM® Fusion HCI for cognitive infrastructure by leveraging its expertise in digital transformation and IT services. HCLTech provides comprehensive support, including strategic planning, implementation and integration of IBM® Fusion HCI into existing systems. Our expert pool of resources and robust processes ensure seamless deployment, optimizing performance and scalability to meet diverse business needs. Additionally, HCLTech offers training and ongoing support to ensure that organizations can fully utilize the capabilities of IBM Fusion HCI, enhancing their cognitive infrastructure and driving innovation.
A promising future
The phenomenal growth of GenAI indicates huge opportunities for all GenAI developers, infrastructure providers and enterprise customers. However, enterprise GenAI success depends on building a solid cognitive infrastructure that meets today’s requirements and is flexible to adapt to future technological advancements. Here, the challenges with developing and scaling cognitive infrastructure cannot be glossed over. The technology is complex, the talent pool is limited and competition is stiff. It calls for collaboration with systems integrators, AI chipmakers and cloud enablers. Since there are many GenAI partners with their own portfolios, organizations must take a pragmatic approach and opt for a service provider that has been trusted by many leading names.
By addressing all critical factors and partnering with the right technology enabler, organizations can deploy GenAI on a purpose-built cognitive infrastructure designed for long-term, sustainable success.
