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Q&A with Rahul Bagchi, Senior Data Solution Strategist

Updated: Jul 24, 2023

Exclusive Trusted Magazine Q&A with Rahul Bagchi, Senior Data Solution Strategist


What are the latest technologies with impact in the AI landscape?


AI is our generations greatest opportunity and challenge which will completely transform our ways of living and working. There are so many AI technologies that are impacting our everyday’s life. But before we speak about the latest AI technologies we need to understand what AI really means. AI is not a magic instead it’s a combination of software and data engineering techniques to interpret meaning and take decisions from vast amount of data. Artificial intelligence is about building a programme which will perform tasks that typically require human intelligence including visual recognition, speech recognition, language processing and decision making. AI is primarily focused on achieving two things:


1. Automation and optimisation of repeatable human jobs


2. Better decision making with improved predictions


There are lot of AI technologies which are helping in automation, optimisation and predictions. Few of the examples are:


1. Conversational Marketing or Customer Service Bots


2. Robots performing manufacturing jobs in a factory assembly line


3. Robotic vacuum cleaner


4. Self-driven car


5. Natural language processing engines to understand user sentiments from social media


6. Personalised health guidance used by healthcare providers/hospitals



How AI is changing some organisations and processes in a disruptive way ?


AI is going to change how organisations do their business today. The impacts will be far fetching. We are experiencing the possibilities in pockets across various industries like Healthcare, Manufacturing, Energy & Utilities and Retail. Few of those examples are:


1. Personalised medicine and personalised care in Healthcare sectors


2. Healthcare industry is using AI for efficient diagnosis and reduce error


3. Hospitals are trying to predict return of customers to hospitals based on patients clinical records


4. Use of robots in assembly line across manufacturing industries


5. Transforming historical information from documents to digital records using an array of AI technologies and then using them for better decision making


6. Customer service across industries is transforming themselves by using Conversational AI


7. Retail sector is using AI in understanding, identifying consumer needs across channels and then propose best fit products



What are you recommendations to succeed in AI projects ?


As I mentioned earlier that AI is the biggest opportunity of our generation but its not a magic. In today’s world most of the organizations are struggling to implement and scale AI. There are several reasons why organizations are not succeeding with AI projects, but the main 2 reasons are:


1. Data Modernisation - Absence of modern data and information architecture


2. Ways of working - Absence of the correct ways of working to support AI projects


Data is fundamental and fuel for any successful AI projects, so it is of utmost importance that the organization has got the right information architecture available to run AI applications. Organization needs to have a flexible, modular, and agile information architecture to successfully implement AI projects. The 3 cornerstones to build a flexible, modular and agile information architecture are:


1. A cloud based responsive architecture which can be scaled horizontally and vertically


2. A robust and intelligent data management strategy ensuring data quality and data governance


3. Scalable delivery by embedding processes like DevOps, DataOps and MLOps


Once organizations have the above building blocks, then the prescriptive steps to do a successful AI will be:


1. Collect the data required and make it simple and available across enterprise


2. Organize the collected data with embedded data management and governance principles


3. Build your AI model on top of the organized data and make sure the models are scalable, bias free, can be explained and trusted


4. And last but not the least embed AI across business operations



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