Creating a unified data, AI and infrastructure strategy to scale innovation ambitions
By Srinivas Kompella, SVP, CTO & Ecosystem, Data & AI, HCLTech
In today’s rapidly evolving technological landscape, organisations are increasingly turning to artificial intelligence (AI) to enhance their operations and drive innovation. However, scaling AI capabilities present a unique set of challenges that many enterprises struggle to overcome. The key to success is having the right data foundation in place. Put simply, there is no AI without data. But data alone isn’t enough; organisations need to bridge the silos between data, AI and infrastructure to get the most value out of “traditional AI”. Most organisations, especially non-digital natives, are missing the connection between these three efforts, which is hindering scaled innovation ambitions.
Bridging the gap
In today’s digital landscape, organisations are increasingly recognising the value of data as a strategic asset. To unlock its potential, it is essential to align data and AI strategies with business objectives, fostering collaboration across teams to drive meaningful outcomes.
The basic value chain of data and AI is simple: Data is converted into insights, which are converted into actions and decisions. What’s often missing from this unified data and AI strategy is infrastructure. Both data and AI are infrastructure hungry, and while storage costs have become lower over time, if organisations don’t consider the infrastructure strategy, then the ROI becomes highly questionable.
Very few organisations have an enterprise-wide unified data strategy that connects both AI and infrastructure, as well as structured and unstructured data. To assist organisations in overcoming this challenge, a comprehensive solution that helps facilitate the execution of AI initiatives with a clear understanding of costs and requirements can prove to be game changing. Organisational models also need to pivot to break these silos.
Connecting the dots
To effectively leverage data and AI, organisations must first shift their mindset from merely collecting data to actively connecting the dots. This involves identifying the core problem that needs to be addressed and focusing on use cases that will yield maximum business impact, rather than isolating data collection and AI model development. For example, a bank wanted to run AI models to enhance trade surveillance. This required the organisation to look at the problem statement holistically, starting from the desired output and working back from there. Next, it’s essential to determine the best approach for running AI models — whether that means sending data to the model, like with OpenAI’s ChatGPT, or bringing the AI model to the data, which should remain in a private, secure environment. Finally, strategic alignment among AI, data and infrastructure teams is crucial; they must collaborate to tackle the problem instead of working in silos. Failing to do so could lead to inconsistent execution and multiple technology solutions for the same project
Scaling AI and innovation
To enhance AI implementation, organisations should shift from a use-case-driven approach to a capability-driven strategy, focusing on building reusable AI capabilities such as conversational AI and voice analytics for both internal and external service desks. A company exploring numerous use cases can then group them into distinct capabilities for greater efficiency. Establishing a centralised team dedicated to data, AI and infrastructure is essential to create a robust foundation and platform while allowing business units to develop their own AI-powered applications on top, ensuring consistency across the organisation. It’s crucial that the technology approach aligns with the organisational model and that AI is democratised, enabling non-technical employees to easily leverage its capabilities and create value. Initially, the focus should be on targeting low-hanging fruit to drive efficiencies, which can then pave the way for transforming employee and customer experiences and developing new products and services.
The shift has begun
To succeed in scaling innovation and AI, organisations must move from merely collecting data to actively connecting data, AI and infrastructure. Today’s advancements in cloud and data management technologies enable this integration, fostering collaboration and driving innovation at scale. By adopting a unified strategy, businesses can overcome existing challenges and unlock the full potential of their capabilities. This holistic approach not only enhances efficiency and customer experiences but also positions organisations for sustainable growth in a competitive market, ensuring they remain at the forefront of technological advancement.
