In the fast-paced world of business, data and analytics have become the driving forces behind transformation. However, in order to get the benefits, it is important for companies to formulate their AI strategy, says Sunil Senan, senior vice-president & global head of data, analytics & AI, at Infosys. “Data and AI technologies will help companies rapidly amplify human potential and uncover business value,” he tells Sudhir Chowdhary in a recent interview. Excerpts:
What are some of the key trends and challenges anticipated in the data analytics landscape in 2024?Data-driven transformation powered by generative AI, advanced analytics, and a cloud backbone is revolutionising businesses in unprecedented ways. Opportunities of the future will lead businesses to become more connected, intelligent, and autonomous. Data and AI technologies will help companies rapidly amplify human potential and uncover business value. This will be achieved by unlocking efficiencies at scale, empowering the ecosystem, and accelerating growth.It is important for businesses to formulate their AI strategy in order to maximise benefits from their data and analytics initiatives . This will not only help them frame the right problem to solve but also define a value framework that will help them monitor and ensure that they are getting the business benefits from these initiatives. Ultimately, enterprises that spearhead these trends and effectively address the challenges will unleash the transformative power of data analytics and AI, securing innovation dominance and a lasting competitive edge.In what way is a data-centric approach reinventing traditional business models?The vast amount of data has engendered the AI revolution, turning data into a strategic asset that can be leveraged to drive growth and value for nations, societies, and enterprises. It is helping improve lives of citizens and consumers as governments and enterprises look to “responsibly” leverage data and AI for accelerated growth, unlock efficiencies at scale and build new ecosystems.There are three main strategies that businesses can adopt:Become AI native: Put the foundations of data and analytics in place that help them on their journey towards becoming an AI-driven autonomous organisation. The focus here is to get the enterprise data ready for AI and use the intelligence to augment existing functions and human interactions to improve efficiency and productivity.Rethink business: Organisations leverage their AI initiative to create business as a platform.Create an AI ecosystem: Businesses expand beyond their traditional boundaries and create ecosystems with their partners with intelligence being the shared currency.
Treating all technologies as tools that can help solve a business problem is a good practice. One should not apply AI for AI’s sake. Based on the problem at hand the best solution is the one that uses the simplest and least cost approach and provides the best benefit. Here are some of the most effective data/AI best practices for enterprises to drive growth and efficiency:Refine the use case process to streamline development and ensure more initiatives reach production.Adopt a smart data platform approach that encapsulates the major trust ethics and bias considerations and that will allow you to scale AI.Adopt an approach of experimentation, i.e., innovation at speed and innovation at scale. Failing fast is key to innovation and businesses that will experiment will transform themselves faster with AI, creating differential advantage vis-à-vis competition. Additionally, enterprises should focus on getting ‘enterprise data ready for AI’. Also, fingerprint your data to include the privacy and security related metadata. Plus, create data products and AI products that can be leveraged by the rest of the enterprise to scale their intelligence initiatives.
Given that the development landscape is being impacted by AI-powered products, how should businesses capitalise on this?Businesses need to invest in GenAI/AI assisted self-serve tool-sets for analysing, annotating, harmonising, hydrating and automating the end-to-end engineering with AI. This will empower and enable the IT and business analysts to get the data in line with the enterprise blueprint.Second, enterprises need data collaboration infrastructure that not only enables participation in the data and AI economy but also does this with strong underpinnings of trust, ethics, privacy, compliance, and security; this we call ‘Responsible by Design’.Third, this would also often lead to renewing and modernising the core systems, as many companies carry IT landscapes that were established prior to the digital era.
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