CONTRIBUTOR
General Manager and Editorial Director,
Techstrong Group

Heading into 2023, a survey of 2,500 senior technology leaders and executives across thirteen industries finds that despite recent advances, most enterprise organizations are a long way from mastering artificial intelligence (AI).

Conducted by the Infosys Knowledge Institute, the survey finds that 63% of AI models function only at basic sense (36%) or understand capability (27%), are not autonomous and often fall short on data verification, data practices and data strategies.

Most organizations, however, are still new to AI with 81% of respondents deploying their first true AI system in only the past four years, with 50% deploying in the last two years. Only a quarter of respondents (26%) said they are highly satisfied with their data and AI tools, with the highest rates in the financial services sector outpacing all other industries by a wide margin, the survey finds.

Nevertheless, the survey also suggests organizations can generate more than $460 billion in incremental profit if they improve data practices, trust in advanced AI and integrate AI with business operations. The top two challenges cited with employing AI at scale are cost (21%) and data (18%), the survey finds.

The core issue is the quality of the data most organizations work with is not all that high, says Sunil Senan, senior vice president and business head for Infosys. As a result, there’s a lot of effort involving data operations (DataOps) that can be inconsistent, he adds.

In general, there is not much agreement in terms of how data should be managed and shared, adds Senan. Some organizations prefer a centralized approach while others are employing a federated model. Infosys contends there is a greater opportunity to increase the return on AI investments using a hub-and-spoke model that makes it simpler to share data, notes Senan. “Data is the lifeblood for AI,” he says.

Regardless of approach to data management, the survey suggests more than a third of respondents (34%) are incurring business risks because of AI biases that can be traced back to the data used to create a model.

At this juncture, it’s not a question of whether AI will play a critical role in driving digital business transformation as much as it is to what extent. Two thirds of respondents, for example, are already using deep learning algorithms in more than 30% of their AI systems, the survey finds. Whether the AI models being employed are valid given the state of the data relied on to create them is, unfortunately, not easy to determine. Many organizations are undoubtedly moving forward cautiously, simply because implementing AI models at scale based on flawed data can have a profound impact on the business.

The challenge, of course, is rivals are making similar AI investments. Arguably, the one thing business leaders are more afraid of than a potentially flawed AI model is waking up one morning to discover that a competitor has successfully implemented one that provides them with a major strategic advantage. As such, there is no choice but to move forward in the expectation that underlying data being relied on to create an AI model is indeed as accurate as everyone hopes.