Digital to AI—The Continuum

Digital to AI—The Continuum

AI has truly evolved through many decades of technology pushes and demand pulls in the corporate world. The biggest development that served as a precursor to the current surge of AI was the onslaught of digital technology. The constant quest for productivity improvement, automation of manufacturing through robots, re-engineering of business process through RPA and extensive deployment of digital touch points in every business has resulted in a comprehensive redesign of both internal processes and external networks. The improvement of experience, which is a second-order need after productivity gains, has been served through the integration of physical and digital experiences, data warehouses and data marts. AI and machine learning have been enabling all consumers of information to get actionable insights better, faster and cheaper. Today, it is anything as a service that attracts new customers and conceives new systems of engagement and sometimes completely redefines the scope of the business itself.

Digital business models that have emerged over the years with acceleration in the last decade have seen different enterprises emerge, embracing either the cost model, the experience model or the platform model. In the cost model, including world-beating companies like Google, Facebook and Amazon, the focus has been on free or freemium services, providing extreme price transparency, in some cases with reverse auctions and usage-based pricing to aggregate buyers and provide limitless choice to consumers. The experience model, driven by the likes of Netflix, Apple and Disney, has enabled reduced friction and personalisation, leading to higher customer discretion and gratification with automation driving empowerment. Finally, the platform model emerged to bring multiple customers together for multiple vendors and service providers with new entities like Flickr and Uber and incumbents like Siemens and General Electric offering platform solutions. These platforms have created new ecosystems for commerce with self-forming communities and data orchestration to ensure clean information comes to a digital marketplace frequented by all participants. Imagine an Indian village haat, and you will get the picture of how platforms work on the internet.

To use an oft-repeated cliché, data has emerged as the new oil for commerce with data playing multiple roles, primarily as a creator of new assets, including digital content, and providing capabilities to enhance human intelligence and creativity. In other roles, data becomes an enabler of business goal setting and attainment, a utility to be tapped for diverse requirements and a driver of new business ideas and accelerated achievement of goals. This has led to a continuum from data to information to knowledge to wisdom in several stages of progression over the years. The first use of data was the ability to analyse transactions and provide reports and presentations in a descriptive and diagnostic format. Remember the monthly analysis of slow and non-moving items in inventory that material managers would get as a fat heap of computer stationery in the early days of electronic data processing? Being able to ask questions ranging from ‘what happened’ to ‘why did it happen’ and ‘what can happen’ moved both thinking and capabilities from descriptive to suggestive, and as data moved from the enabler to driver role, and machine learning and AI started emerging, prediction truly became power. The final frontier has been the change from ‘what could happen’ to ‘what should happen’, and data has become a creator of suggestions, unleashing the power of prescriptive analytics.

This is the stage where leaders must play a role in defining boundaries for the role of AI and setting up robust governance mechanisms as we will see as we progress through this chapter. As AI itself has progressed in the last couple of years through newer versions of ChatGPT and LLMs to distillation models like DeepSeek and small and narrow language models and, finally, to semi-autonomous and near-autonomous agentic AI, the capabilities of AI and the propensity to take over more and more of the responsibilities and actions of humans has become a threat to the future of human workers. When should the leader step in?