“People run organisations, technology doesn’t. So it is important to explain the WHY behind the recommendations of an AI model to business leaders and users. The whole field of Explainable AI is becoming increasingly important; it started with SHAP and LIME which help go deeper behind the features in AI models. Currently “counterfactual explanations “ is another emerging area in the explainability space.” - Excerpt from the Expert Talks interview with Anirban Nandi.

Today is Episode 10 of the Interview series on Expert-Talks, with Leaders in the Analytics, AI and Transformation space. For this episode, our CEO Mahadevann Iyerr (Mahaa) is in conversation with Anirban Nandi, Vice President – AI Products & Business Analytics at Rakuten. Prior to Rakuten, Anirban has had analytics programs at Landmark group and Target. Anirban is a frequent speaker and Analytics and AI industry forums and is also an extensive blogger on AI topics.We are sure listeners will enjoy listening to Anirban’s perspectives over the next 40 minutes. We are listing below, a few key points from the interview :

1. Anirban emphasizes the key building blocks required for a successful AI and Analytics program in any organisation. Firstly, there has to be organisational and business alignment and belief system in the AI and Analytics program, secondly, there has to be a vision and data strategy with well-articulated KPIs and goals, thirdly the company needs to invest in collecting and storing quality data with the appropriate data latency and fourth it needs to build the right team, platform and tech capabilities to convert the data into actionable insights and models. Most importantly the AI and Analytics teams need to invest in developing a business context which is relevant to its specific industry and market situation.

2. A combination of quick turnaround analytics and deep breakthrough AI models may be required to create both immediate as well as long-term value for organisations. For companies which are at initial levels of AI and Analytics adoption/maturity, the proportion may be skewed more towards quick turnaround analytics & insights. However as an organisation matures, it can automate more and more insights and focus on building transformative AI models and integrate them into its delivery/platform features.

3. It is always a good idea to get outside in perspective and data and augment it with internal data, during an organisation’s digital transformation journey. While the products that customers buy are different in different industry segments and have their own nuances, some of the basics like why and when customers buy can be applied from first principles across industries. Businesses can also leverage external anonymised data from external providers, data from GEO SDKs and in some cases avail the services of consulting firms to build more outside-in perspectives.

4. Generative AI is an augmentation or extension to the existing field of AI, and will soon large disruptive adoption by businesses, in many areas like EdTech, Contact support etc. As it begins to get wider traction, Prompt Engineering will become an increasingly important skill. Business Transformation Professionals who have a good understanding of business constraints/ outcomes, and the possibilities of Tech / AI, are most likely best positioned to leverage their experience for better prompt effectiveness.

5. Future of AI and Future of Work will definitely influence each other. AI will not take away your job, but a person who uses AI effectively could take away your job. With increasing adoption of generative AI, the nature and expectations from each job/role will change and professionals will need to stay abreast, upskill themselves and adapt to changing environments.