Gaining a competitive edge in the ‘AI at work’ era with AI-ready infrastructure

 

Less than a year since it captured the attention of individuals and organizations across the world, generative AI has transitioned to being an imperative for organizations. Recent surveys and reports underscore this shift, revealing that a majority of organizations across industries are implementing and planning to deploy AI technologies.
  Recent research commissioned by Microsoft1, shows most companies are actively ramping up their AI capabilities, with 95% of businesses surveyed planning to increase their AI usage over the next two years. Across industries, AI adoption is believed to be critical for success. In India, particularly, confidence in AI is strong. The 2024 Microsoft and LinkedIn Work Trend Index reveals over 90% of Indian leaders believe their company needs to adopt AI to stay competitive.  In this race to unlock the promise and transformative benefits of AI, the AI-readiness of the organization will be the big differentiator. 

 
There are two aspects to AI readiness: the readiness of the infrastructure and the readiness of the data – is the organization's data residing in silos and is there a single source of truth for the data.  AI-ready infrastructure is inherently scalable and flexible, allowing businesses to adjust resources in real time to meet the changing demands of AI applications. This adaptability ensures that organizations can maintain efficiency and cost-effectiveness across their AI lifecycle. 
 
A good example is Air India, which became one of the first major global airlines to migrate to a cloud-only IT infrastructure. It wanted a flexible and reliable computational and networking infrastructure to support its goal of accelerated innovation, not just cost savings and better operational efficiencies from moving to the cloud. 
 
Becoming AI-ready is not one-size-fits-all 

The journey to becoming AI-ready is not one-size-fits-all and can vary significantly depending on industry characteristics, regulatory landscapes, and business goals. The choice depends on the specific needs, priorities, and strategic goals of the organization, including considerations of cost, control, security, and compliance. Partnering with firms that have deep AI expertise can make this journey easier and accelerate ROI. For example, running AI on-prem could accelerate compliance and security check-ups but organizations will have to bear in mind that building and scaling AI projects require significant investments in computing power. On-premises deployments also have only a limited number of pre-trained models and ready-made services that enterprises can take advantage of.  They can explore alternatives such as model-as-a-service (MaaS) approach, where businesses can access sophisticated pre-trained models like Phi 3 over the cloud, providing a scalable and flexible solution without the need for substantial on-premises investments. 
 
However, it often requires a hybrid approach and careful planning to address the challenges associated with data transfer, security, and compliance. A secure adaptive hybrid cloud, combining the best of both worlds, can offer a compelling alternative in such cases. A secure adaptive hybrid cloud blends public cloud flexibility, private cloud security, and on-premises computing. Adaptive cloud is an approach to unify siloed teams, distributed sites, and sprawling systems into a single operations, security, application, and data model.  
 
With AI-enhanced central management and security, the adaptive cloud elevates IT capabilities, enabling teams to focus on strategic work. Its cloud-native tools offer freedom from traditional system confines, enabling rapid application development to scale across boundaries. A unified data foundation supercharges physical operations, driving efficient workflows, predictive insights, and resource optimization. 
 
This mix is particularly beneficial for organizations requiring dynamic scaling capabilities without compromising data privacy and compliance. A hybrid cloud infrastructure ensures that sensitive data can be processed locally, while less sensitive tasks can leverage the cloud's computational power, optimizing costs and efficiency.   
 
In industries, such as retail and manufacturing, that typically manage high volumes and complex AI workloads, performance and scalability of the infrastructure are key considerations along with security, while in highly regulated ones like healthcare and finance, security and privacy are the overriding priorities influencing the choice of infrastructure.  In the latter case, the AI-ready infrastructure should incorporate compliance by design, with mechanisms for data encryption, audit trails, and access controls that meet industry-specific regulations.  
 
Confidential computing offers a way to secure the data, not only where it is stored, but also while it is being processed. Data is encrypted during processing, not just in transit and at rest, enabling more secure data analysis and AI model training. By leveraging confidential computing, businesses can further protect against unauthorized access and data breaches, fostering trust and compliance in AI deployments. 
 
The future is AI 
AI offers organizations an unprecedented opportunity to reimagine their business by unlocking efficiencies, boosting employee productivity and opening a world of possibilities that did not exist earlier – to improve processes, crunch time and boost innovation as copilots transform work by assisting teams to achieve more.  

While cost is an important consideration in AI infrastructure, organizations should also consider the opportunity cost and what will maximize the impact of their AI implementations and unlock value for stakeholders. 
 
As AI becomes pervasive across organizations, the future will belong to those who are better prepared to leverage AI effectively and responsibly, and investing in the right infrastructure will be the foundation to securing that future.  Ultimately, AI-readiness can make the difference between being the disruptor and getting disrupted, making this a critical element in the AI strategies of organizations and in driving bottom line impact. 

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