Anuradha Sharma

While investments in AI continue to rise worldwide, companies still struggle with the ground level gains. Misalignments between business and technology and a lack of planning ahead are key issues to not realising the potential of AI adoption. As businesses think about adopting AI, they need to look at it in terms of what current problems can be solved with the AI&ML approach-not all can! Looking at gains and pains for clearly articulated AI&ML business use cases helps develop a short and long term strategy for AI adoption. Concentrating on high gain and high pain problems early on in the adoption journey can completely derail the entire initiative! How execution leaders think about people, data, performance and productionization of initiatives seals the case for how successfully AI will generate revenue for the company. The AI adoption process needs to be thought of more like a new type of process where change management, implementation and last mile execution are critical to success. Top level executives have to drive change downwards and hiring a data science head and team does not ensure that happens. Planning before jumping into AI changes the 85% failure rate to success. There are practitioner frameworks and solutions available that can help companies not just adopt AI but drive revenue from it.

Datasciences profession has been tagged with some rather cliched terms and is often in limelight for the right reasons of its contribution in powering product and experience differentiation. While many industry segments and enterprises of varied scale have setup data sciences teams, the focus of development has primarily been on technical skills. Join this presentation by industry and academia veterans to decipher how to craft a holistic success profile of a rockstar datascientist and leveraging it to power employee development.