(US and Canada) Ranjan Sinha, IBM Fellow, Vice President and Chief Technology Officer, IBM Global Chief Data Office, speaks with Virendra Dafane, Technology Leader, about solutions being developed at IBM, reasons for digital transformation failure, and AI regulations.

Sinha has been involved in developing the data and AI backbone that is used across IBM with thousands of users and hundreds of projects for several types of personnel. It has been developed over six years with a focus on making it easy to ingest or access data, generate insights, and eventually infuse AI into business processes. The data and AI backbone has resulted in more than 50% improvement, touching critical business processes like supply chain, finance, sales, marketing contracts, procurement, and operations risk.

When talking about the reasons for failure in digital transformation, Sinha says that the steady shift to digital has been underway for over a decade, but with the pandemic, nearly 10 years of digitization are happening in a single year. At the same time, many businesses still rely on manual antiquated practices, and fragmented data and services. As a result, employers often struggle to find the information they need and cannot react quickly to disruptions or competitive pressures. In addition to the usual challenges, he says there is also hesitation to adopt new technologies.

Further, he points toward today’s state of data, which is diverse and more difficult to manage than ever before. Sinha indicates that enterprises have to juggle complex multi-vendor data environments, siloed data sets, and long data preparation cycles while maintaining a secure, governed, and compliant strategy. Sinha mentions that IBM has been making it easier for its clients to learn from IBM’s experience to accelerate their digital transformation.

He continues by talking about important regulations in the AI era. Sinha says that AI is a family of techniques that allows machines to learn from data and act on what they have learned. According to him, the growth of AI has been accelerated by three major factors. The first is the availability of data due to digitization and smart devices. The second is the availability of easy-to-use analytics and ML tools. And the third is a dramatic increase in computing power that helps to build complex models.

Sinha indicates that the widespread adoption of AI would need monitoring and governance, especially when it comes to critical decision-making. There is always a risk of bio-scripting into the data and its analysis which may disadvantage certain groups. He adds that there are also concerns about potential misuse or unintended consequences of AI, prompting efforts to develop standards to create reliable AI systems. Already, at least 60 countries have adopted some form of AI policy around AI discrimination, fraud, data privacy, and data misuse.