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AI enables organizations to improve operational efficiencies and enhance customer experiences through rich data driven insights and process automation. Leveraging AI technologies such as machine learning, natural language processing, and predictive analytics empowers businesses to adapt, innovate, and stay competitive in the rapidly evolving digital landscape.

GS Lab | GAVS hosted a fireside chat on AI as a Key Enabler in Digital Transformation. The panel included Dr. Ganesh Natarajan, Chairman of 5F World, Dr. Pushpak Bhattacharyya, Professor of Computer Science and Engineering IIT Bombay, Mr. Ravindra Pandey, CIO at SBI, Mr. Khushru Mistry, CIO and Senior VP at Eureka Forbes Ltd, Mr. VS Srirangarajan CIO at Grasim Industries Aditya Birla, and Mr. Girish Koppar, GM-IT at Wockhardt Hospitals, and Mr. Chandra, Senior VP Customer Success at GS Lab | GAVS.

It was a thought-provoking discussion where pioneer CXOs from different industries came together to discuss the role of AI in re-inventing their enterprises – reaffirming how AI and various products built using AI technologies enhance the quality of our lives and of the people we care about. There was also focus on the challenges with AI and how addressing them is crucial for responsible and beneficial AI deployment. Some of the challenges discussed include ambiguity, data security, correctness of data, and the need for the right skillsets to effectively leverage these new technologies.

AI for Digital Transformation

Embracing digital transformation allows organizations to stay competitive in the digital age, adapt to changing market dynamics, and unlock new growth opportunities. To that end, AI and ML play a pivotal role in an organization’s digital transformation journey. Discussions also revolved around the topics below (not in any particular order). The recording is available here.

Education and Training

Routine training ensures a skilled workforce and enables easier adoption of AI and ML technologies. However, it is hard for organizations to keep up with the current rate of growth in AI. Partnerships with educational institutions can help meet this demand, so that the organization stays ahead of emerging technologies.


Data is an integral part of AI. Data is primarily used to train deep neural network models and translational machine learning models. It helps improve the probability and maximize the likelihood estimate, and thereby the prediction process. Data helps resolve ambiguity issues. It is possible to create machine learning models locally and make them interact with each other through meta-learning and federated learning.

Predictive Analytics

Predictive analytics is replacing the need for traditional statistics because of the noise and ambiguity. It is gaining popularity among several industries, including aviation, banking, and railways. There is a demand to integrate AI for prediction, such as identifying areas that could malfunction and areas that could be more efficient. While traditional statistical models can derive answers, a large number of parameters must be considered – which can become cumbersome in real-life operations.

Federated Learning

Organizations are not comfortable parting with their data. However, they can train machine learning models in their setup with the help of researchers and academicians. These machine-learning models can be put together to train other models – called meta-learning.

AI for Infrastructure Management

ZIFTM is a true AIOps platform that is crafted to deliver business outcomes by leveraging pure-play artificial intelligence. ZIFTM ensures that business service reliability is delivered across the entire chain that supports delivery of the service, including physical and virtualized servers, databases, middleware, storage and networks. For information on how ZIFTM can help you harness the power of AI to deliver business value in your IT operations, please visit

AI for Healthcare

GS Lab | GAVS is focused on addressing concerns and bringing transformation in the healthcare space using AI. AI, AI-enabled, and AI-curated applications will lead to almost 51.3 billion worth of revenue by 2027 through virtual nursing, cybersecurity, preliminary diagnostics, and more. AI as a Service (AIaaS) holds significant promise in healthcare. AIaaS enables efficient analysis of data, facilitating accurate diagnoses, personalized treatments and improved patient outcomes, streamlined administrative tasks, optimized resource allocation, and so much more. Through cloud-based AI platforms, healthcare providers can access advanced analytics, predictive modeling, and machine learning capabilities without the need for extensive infrastructure investment. To learn how we can help you leverage Advanced Analytics and AI for smart healthcare operations, please visit