In the fast-paced business world, data and analytics have become the drivers of transformation. However, in order to benefit, it is important for businesses to formulate their AI strategy, says Sunil Senan, senior vice president and global head of data, analytics and AI at Infosys. “Data and AI technologies will help businesses rapidly amplify human potential and uncover business value,” he told 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 cloud infrastructure, is revolutionizing businesses in unprecedented ways. The opportunities of the future will lead businesses to become more connected, intelligent and autonomous. Data and AI technologies will help businesses rapidly amplify human potential and discover business value. This will be achieved by unlocking efficiencies at scale, strengthening the ecosystem and accelerating growth.
It is important for businesses to formulate their AI strategy to maximize the benefits of their data and analytics initiatives. This will not only help them define the right problem to solve, but also define a values framework that will help them monitor and ensure they are realizing the business benefits of these initiatives. Ultimately, companies that pioneer these trends and effectively address the challenges will unleash the transformative power of data analytics and AI, ensuring innovation dominance and competitive advantage. sustainable competitiveness.
How does a data-centric approach reinvent traditional business models?
Big data has driven the AI revolution, transforming data into a strategic asset that can be harnessed to drive growth and value for nations, societies and businesses. It helps improve the lives of citizens and consumers, as governments and businesses seek to “responsibly” harness data and AI to accelerate growth, drive efficiencies at scale and create new new ecosystems. There are three main strategies businesses can adopt:
Become AI native: Build the databases and analytics that will help them on their journey to an AI-driven autonomous organization. The goal here is to prepare business data for AI and use intelligence to augment existing functions and human interactions to improve efficiency and productivity.
Rethinking the Business: Organizations are leveraging their AI initiative to build a business as a platform.
Create an AI ecosystem: Companies are expanding beyond their traditional boundaries and creating ecosystems with their partners, with intelligence being the common currency.
An organization’s ability to leverage data for disruptive and innovative business models is only limited by its imagination and the ethical standards it must adopt. Companies that use data and AI responsibly are far ahead of those that don’t. In the digital age we live in, data and AI are one of the biggest differentiators allowing businesses to outperform their peers and often create new industry value chains and transcend industry boundaries. For this reason, businesses must build a strategy and ecosystem powered by data and AI. Please share some of the best practices that define modern and effective data analysis strategies.
Treating all technologies as tools that can help solve a business problem is a good practice. AI should not be applied for AI’s sake. Depending on the problem at hand, the best solution is the one that uses the simplest and least expensive approach and provides the greatest benefit. Here are some of the most effective data/AI best practices for businesses to drive growth and efficiency:
Refine the use case process to streamline development and ensure more initiatives reach production.
Adopt an intelligent data platform approach that encapsulates key trust and bias ethics considerations to help you scale AI. Adopt an experimentation approach, i.e. rapid innovation and innovation at scale. Failing fast is key to innovation and companies that experiment will transform faster with AI, creating a differential advantage over the competition. Additionally, businesses should focus on preparing “enterprise data for AI.” Additionally, fingerprint your data to include metadata related to privacy and security. Additionally, create data products and AI products that can be leveraged by the rest of the business to scale their intelligence initiatives.
As the development landscape is impacted by AI-enabled products, how should businesses take advantage of them?
Enterprises should invest in GenAI/AI-powered self-service toolsets to analyze, annotate, harmonize, hydrate, and automate end-to-end engineering with AI. This will enable IT and business analysts to align data with the business plan. Second, businesses need a data collaboration infrastructure that not only enables participation in data and AI. economy but it also relies on strong foundations of trust, ethics, confidentiality, compliance and security; This is what we call “responsible by design”. Third, it would also often lead to the renewal and modernization of core systems, as many companies have IT landscapes that were established before the digital age.
