New Delhi, Feb. 3 -- India's enterprise AI market is moving past its early fascination with large, general-purpose language models. As companies begin embedding AI into regulated, high-volume business processes, attention is shifting toward domain-specific language models, or DSLMs, that are trained to operate within defined industry, data, and language boundaries. The change reflects a broader recalibration of enterprise priorities-from experimentation and scale to reliability, cost control, and regulatory alignment.
Executives and engineers say the limits of general models have become clearer as deployments move into production. In sectors such as banking, healthcare, telecom, and manufacturing, enterprises are finding that accuracy, predictability, and governance matter more than broad conversational ability.
Why Domain-Specific Models Are Gaining Ground
Domain-specific language models are designed to perform a narrower set of tasks using industry-relevant data rather than general internet-scale corpora. This allows them to understand sector-specific terminology, workflows, and compliance requirements more consistently. According to Vishal Chahal, Vice President at IBM India Software Labs, enterprises are increasingly focused on efficiency, trust, and adaptability rather than model size.
"For enterprises, meaningful ROI from AI depends on efficient performance at the right cost, trustworthy and transparent data, and flexibility to adapt to business needs," Chahal said. He added that smaller, domain-focused models can often run on standard enterprise infrastructure, reducing dependence on expensive GPU resources while offering greater control over data and customization.
From Pilots to Production Workflows
Indian enterprises are now deploying DSLMs in operational environments rather than isolated pilots. In financial services, these models are being used for compliance analysis, document processing, and internal knowledge systems where regulatory nuance is critical. Ramprakash Ramamoorthy, Director of AI Research at ManageEngine, Zoho Corp, said domain-trained models outperform general LLMs in operational IT and compliance-driven workflows because they behave more deterministically.
"These environments demand precision and predictability, not open-ended reasoning," he said. "Control matters more than raw generative capability in regulated Indian enterprises."
In customer engagement and voice-based systems, latency has emerged as another decisive factor. Bharath Shankar, Co-Founder of Gnani.ai, noted that even small delays can disrupt real-time interactions in BFSI and telecom use cases. "Purpose-built models deliver consistent low latency and lower hallucination rates, which directly impact business outcomes like recovery rates and average handling time," he said.
Measuring ROI Beyond Benchmarks
Enterprises are also changing how they measure returns on AI investments. Rather than relying on model benchmarks, companies are tracking operational metrics such as reduced resolution times, lower error rates, and decreased manual intervention. According to Ramamoorthy, ROI is increasingly tied to outcomes like faster audits, improved compliance readiness, and predictable infrastructure costs.
Bindu Sunil, Chief AI Officer at Mindsprint, said the economics become clear at scale. "Once enterprises reach millions of inferences, self-hosted smaller models dramatically reduce costs," she said, adding that some organizations have cut inference expenses by as much as 80 percent after shifting away from cloud-based general models.
India's Language and Regulatory Reality
India's regulatory environment and linguistic diversity are further accelerating the move toward DSLMs. The Digital Personal Data Protection Act and sector-specific rules from regulators such as the RBI and IRDAI place strict requirements on data residency, auditability, and explainability. Smaller, self-hosted models make it easier to meet these obligations.
Language remains another critical factor. Indian enterprise communication is heavily code-mixed, blending English with regional languages and domain jargon. "Without domain understanding combined with Indian languages, AI systems remain superficial," said Dr. Samiksha Mishra, Director of AI at R Systems. Models trained on code-mixed, domain-specific data are better suited to how work actually happens across Indian enterprises.
What Will Separate Success From Failure
As AI adoption accelerates, industry leaders say successful DSLM deployments will be defined by operational discipline rather than model sophistication. According to Gautam Goenka, Senior Vice President at R Systems, enterprises that embed DSLMs directly into workflows such as CRM, DevOps, or compliance systems will see sustained value. At the same time, standalone chatbot deployments are likely to stall.
Looking ahead, the trajectory is clear. Indian enterprises are no longer chasing models that can do everything. They are investing in systems that reliably perform specific tasks at scale and within regulatory boundaries-marking a more mature and pragmatic phase for enterprise AI in the country.
Published by HT Digital Content Services with permission from TechCircle.