Corporates have taken to AI in a big way, and have invested heavily in AI systems. Some of the corporates are able to convert data science insights into business benefits but many are not able to do so. The corporates with old legacy systems, though they adopt AI, they do not get much benefits out of such adoption.

AI is a niche technology. To many corporates, it is a new area. Many corporates have AI practitioners who do not have top-tier AI capabilities. In such a situation, AI does not come close to prediction of future. Corporates must employ top-tier advanced AI to get the results. Most finance companies use AI profitably.

It is also true that nimble IT-savvy companies get better results from AI than big corporates, e.g. food delivery companies use AI to predict a lot of challenges in real time — especially the last-mile hiccups. Edtech companies use AI profitably. Hospitals make use of AI for health risk assessment as a part of preventive healthcare.

Legacy systems have flawed datasets. It generates a bias. AI should be deployed only after data verification. Top and senior executives must be involved in AI implementation. The data input for AI must be validated. The data should be trustworthy. There should be hub-and-spoke data management. There is centralisation of the platform, but the teams have flexibility to operate.

Thus in essence, successful AI implementation requires improved data practices, trust in advanced AI and integration of AI with business operations.