Leading Indian IT firms are deepening their AI capabilities by integrating large language models (LLMs), focusing on domain-specific applications, and upskilling employees to meet the growing demand for AI-driven solutions.
Nikhil Malhotra, Chief Innovation Officer & Global Head of AI and Emerging Technologies at Tech Mahindra, said the company pioneered building LLMs in India with Project Indus, a foundational model it built from the ground up.
An independent study conducted by The Assam Kaziranga University in Jorhat compared various models and demonstrated that Indus outperforms many counterparts in tokenization efficiency, he shared.
“The speed of tokenization is influenced by the number of parameters and the model’s size. It is safe to say Indus has shown the possibility of outperforming even larger models by using optimized techniques. We are also leveraging LLMs to enhance our services for customers and improve delivery. We are utilizing OpenAI models to fine-tune our LLMs. The usage varies depending on the specific use case, which is determined through our research to compare the models and select the appropriate one.”
Deepak Bagchi, VP of AI CoE at Coforge, said Quasar, its AI platform, has integrated 23 different LLMs, combining open-source and licensed models. This allows the platform to handle a range of tasks, from natural language processing to complex data analysis.
“We plan to fine-tune models tailored to client requirements. They will be optimized for tasks and industries, ensuring higher accuracy and efficiency. We are also focused on working with SLMs custom-built for specific use cases, which will be designed to address unique business challenges and provide targeted solutions.”
- Also read: Beyond LLMs: A multi-model approach to AI
Alongside, Sridhar Mantha, CEO of Generative AI Business Services (GBS) at Happiest Minds Technologies, explained that rather than competing in the generic LLM space, the company focuses on utilising existing LLMs to create domain-specific applications leveraging its domain expertise to deliver business impact through Industry specific solutioning and use cases.
Addressing the challenges companies face in building LLMs, he explained, “The challenge isn’t reluctance but strategic decision-making. Building proprietary LLMs requires massive investments in infrastructure, talent, and R&D, making it less viable for most IT services firms. Considering it is a fast-evolving space, the investments need to be near-perpetual to keep ahead of the technology curve. Instead, they focus on fine-tuning existing models for enterprise-specific needs, balancing customization with cost efficiency.”
The industry sees greater value in AI-enabled solutions, consulting, and managed services rather than training foundational models from scratch, he added. AI is a fundamental driver of its long-term growth strategy, with the company investing heavily to stay ahead of the curve.
With a dedicated business unit for GenAI, Happiest Minds is aligned with its 2031 vision of becoming a $1 billion company, he said. The company’s AI, GenAI, and Analytics business already contribute significantly to its overall revenue, accounting for 11 per cent.
“By partnering with leading AI providers and specializing in verticalized AI applications, security, and responsible AI, IT firms can deliver high-impact solutions without the overhead of LLM development. While full-scale LLM development remains uncommon, specialized AI innovations tailored for business transformation are gaining momentum.”
While Indian IT companies are making significant strides in AI, widespread concern remains about AI potentially replacing various job roles. Acknowledging this, Coforge’s Bagchi noted AI has the potential to create new job opportunities, particularly in roles like machine learning engineers, data scientists, and AI researchers. This has led to a focus on reskilling and upskilling existing employees to meet the demands of said AI-driven roles.
“We have set up a dedicated CoE to formalize the process across all the labs. The centralized AI CoE is led by the leadership team with over 250 AI practitioners of which over 100 are certified professionals in technologies including MS Azure, GCP, and AWS. The skills span areas like AI/ML, Data Science, Data Science Platform Engineers, Machine Learning Operations, and Integration Engineers. We have trained all employees directly working on AI/ML projects and have certified over 25,000 employees on relevant AI content as part of the ‘AI Spark’ module with advanced courses for top performers,” he said.
Mantha noted that job creation happens in the new demands being created in AI. For example, GenAI-driven hiring is directly proportional to business adoption and demand. While there has been immense interest in GenAI, its adoption follows a hockey-stick trajectory—initial exploration and gradual implementation, followed by a sharp acceleration once businesses integrate this into their core operations.
“Currently, companies are prioritizing upskilling and reskilling their existing workforce to align with Generative AI-driven workflows rather than engaging in large-scale hiring. However, as GenAI adoption matures and more enterprises move from experimentation to full-scale deployment, we can expect a corresponding surge in hiring—particularly in specialized areas like AI model engineering, data science, and AI operations. This shift will mirror the upswing of a hockey stick, where increased adoption drives greater workforce expansion.”
He explained that Happiest Minds has established a dedicated business unit for GenAI and an AI CoE to drive AI literacy across the
organization. Its investments include Gen AI training programs at various levels, from foundational AI awareness to deep technical expertise. The company also has internal platforms for hands-on experimentation with GenAI models and has created solutions for its internal team.
Jaspreet Bindra, co-founder of AI & Beyond, shared that upskilling in AI and making targeted investments in AI-driven innovations are no longer just strategic options for Indian IT companies. Instead, they have become operational imperatives. With shrinking margins and intense competition, companies failing to adapt risk falling behind. Reskilling the workforce in AI tools like generative models, automation frameworks, and data-driven decision-making processes is essential for maintaining relevance, he added.
“While these investments may incur short-term costs, they act as a hedge against long-term margin erosion. By embedding AI into core operations, companies can streamline processes, reduce overheads, and improve service delivery. This enhances productivity and operational efficiency and allows for innovative, AI-powered solutions that differentiate them in the global market. In a world where AI proficiency will define the next era of technology services, Indian IT firms must seize this opportunity to future-proof their business and secure sustained growth.”