There are tons of wonderful AI and Data Science projects that are continuously getting developed in recent years. As we continue to advance and progress in these fields, there will be tons more awesome projects that will make their way through the development phase and reach the general audience to explore and be fascinated about. For someone who is getting started with these subjects, it is crucial to understand that Data Science and AI projects can sometimes be more complicated than we imagine. As you climb up the skill ceiling, there are tons of noteworthy points to keep in mind as you work on new ventures. While you might be successful in your initial endeavors on beginner-level projects, the increasing complexity of more advanced-level projects could cause several hindrances when you work on them.

This article is based on an in-depth study of the data science efforts in three large, private-sector Indian banks with collective assets exceeding $200 million. The study included onsite observations; semistructured interviews with 57 executives, managers, and data scientists; and the examination of archival records. The five obstacles and the solutions for overcoming them emerged from an inductive analytical process based on the qualitative data. More and more companies are embracing data science as a function and a capability. But many of them have not been able to consistently derive business value from their investments in big data, artificial intelligence, and machine learning.1

This article is based on an in-depth study of the data science efforts in three large, private-sector Indian banks with collective assets exceeding $200 million. The study included onsite observations; semistructured interviews with 57 executives, managers, and data scientists; and the examination of archival records. The five obstacles and the solutions for overcoming them emerged from an inductive analytical process based on the qualitative data. More and more companies are embracing data science as a function and a capability. But many of them have not been able to consistently derive business value from their investments in big data, artificial intelligence, and machine learning.1 Moreover, evidence suggests that the gap is widening between organizations successfully gaining value from data science and those struggling to do so.2

Digital transformation is the flavor of the season. Every company has accelerated its efforts to digitize operations, gather intelligence, and rapidly respond to a changing market. McKinsey senior partner Kate Smaje says that organizations are now accomplishing in 10 days what used to take them 10 months. With data powering better and faster decisions, she says, the road to recovery is paved with data. As a result, most organizations are trying to adopt data-driven decision-making.

Data is quickly becoming the most valuable asset in the insurance sector, given its tremendous volume in our digital era. Simultaneously, Artificial Intelligence (AI), harnessing big data and complex structures with Machine Learning (ML) and other methods, is becoming more powerful. Insurers expect more efficient processes, new product categories, more personalized pricing, and increasingly real-time service delivery and risk management from this development. Given the many leverage points in insurance, it surprises that AI-driven digitization is not evolving more rapidly. When according to a recent Gartner[2] study, 85 % of data science projects fail, how can insurance companies make sure that their projects are among the successful ones[3]?