In the life sciences space, there is significant adoption of Generative AI as it can help reduce paperwork and time-to-market for drugs, especially in clinical trials, said Nitesh Mirchandani, chief business officer, MINDSPRINT, a digital technology and business solutions firm. “Covid-19 accelerated this trend. While Gen AI wasn’t available in 2020, AI was used to crunch data and expedite vaccine development. This demonstrates the potential of AI in reducing timelines for clinical trials and drug rollouts,” he told this newspaper.
Most customers are adopting AI, and it is part of the company's data analytics practice. MINDSPRINT has three service lines - technology services, business process services, and cybersecurity services.
"AI is a prominent part of our tech services group. Within AI, particularly Gen AI, there's significant interest as everyone wants to be more efficient, fast, and scalable. Gen AI use cases are helping with scalability, efficiency, productivity, with the end goal of improving customer or employee experience and helping with improved company evaluation," Mirchandani added.
For commercial teams, the company has built platforms to help sales teams sell better and faster. "Imagine if they're going to meet a customer - previously, people spent hours reading up on customers. Now, industry reports, annual reports, quarterly earnings can be fed into a system that generates crisp, consumable inputs about that customer," he said
The company has customers in four primary industries -- food and agriculture, retail CPG (consumer packaged goods), life sciences, and manufacturing. Talking about cyber security, he said it is an emerging space and a top priority for chief information officers and IT leaders. "We have a cybersecurity practice offering managed security assessments and services. In the manufacturing space, we do factory security assessments, and we also do a lot on the consulting side, helping our customers with cybersecurity,” he said.
The use of AI cuts across all its verticals, with different use cases for each. “In the manufacturing space, we’re working on inventory optimisation. Gen AI can help with better forecasting and optimising inventory levels. Sometimes customers come to us with a three-year roadmap and ask if we can help achieve certain aspects. More often, we take a consultative approach, proactively telling our customers what we can do to solve their problems,” Mirchandani added.
Over the past 12-18 months, organisations like MINDSPRINT have moved beyond proof of concepts (POCs) to full production, showcasing the advantage of being a fast mover. “This allows us to confidently discuss our progress, as we adopted a rapid approach in Gen AI, while others are still catching up. However, we also recognise the limitations and potential game-changers in this field. We are focusing on key challenges, such as using large public LLMs (large language models) for enterprise data,” said the company’s chief technology officer Sagar P V.
“Securing data and training LLMs to handle a company’s large datasets is a significant challenge. Relying solely on public platforms is not ideal when we can train our own LLMs to manage proprietary data, though this process is not as straightforward as it might seem,” he added.
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