BSNL Powers Up with AI, Developing Indigenous Small Language Models

BSNL’s Chairman and Managing Director, Robert J. Ravi, has confirmed that the state-run telecom operator is developing in-house small language models (SLMs) to strengthen its AI capabilities. The move is part of BSNL’s broader strategy to leverage artificial intelligence for network management, customer experience, and churn reduction.

Key Highlights

• AI-driven transformation: BSNL is banking on AI to optimize telecom solutions, improve service delivery, and enhance customer satisfaction.
• In-house SLMs: Instead of relying solely on external large language models, BSNL is building its own smaller, domain-specific models tailored for telecom operations.
• Strategic focus areas:
  • Network management – predictive maintenance, traffic optimization.
  • Customer experience – personalized support, faster resolution.
  • Churn reduction – analyzing usage patterns and proactively addressing customer concerns.
• Leadership vision: Ravi emphasized that AI workloads will dominate the telecom sector in the 5G era, making indigenous AI development critical for BSNL’s competitiveness.

This signals BSNL’s intent to position itself not just as a telecom provider but as a tech-driven enterprise, aligning with India’s push for indigenous AI stacks and digital sovereignty.

Here’s a detailed breakdown of how BSNL’s Small Language Models (SLMs) differ from traditional Large Language Models (LLMs), and why this distinction matters in telecom:

Key Differences Between SLMs and LLMs

Aspect	Large Language Models (LLMs)	Small Language Models (SLMs)	Why BSNL Prefers SLMs
Scale	Billions of parameters, massive datasets	Fewer parameters, domain-specific training	Easier to train and deploy for telecom-specific tasks
Resource Needs	Require huge compute power, GPUs, cloud infrastructure	Lightweight, can run on local servers or edge devices	Cost-effective for a state-run telco with limited budgets
Speed & Efficiency	Slower inference, high latency	Faster response times, optimized for real-time tasks	Critical for customer support and network management
Generalization	Broad knowledge across domains	Narrow focus on telecom operations	Better accuracy for BSNL’s internal use cases
Deployment	Cloud-heavy, centralized	Edge-friendly, can be embedded in telecom infrastructure	Supports 5G rollout and localized AI workloads
Cost	Expensive to train and maintain	Lower training and operational costs	Aligns with BSNL’s need for affordable innovation

Why This Matters for BSNL

• Telecom-specific optimization: SLMs can be fine-tuned for tasks like predictive maintenance, call routing, and churn analysis.
• Digital sovereignty: Developing in-house models reduces dependence on foreign AI providers.
• Scalability in 5G era: Lightweight models can be deployed across BSNL’s vast network infrastructure without overwhelming resources.
• Customer experience: Faster, domain-specific AI responses improve support and reduce churn.

In short, BSNL’s SLMs are leaner, cheaper, and more focused, making them a strategic fit for telecom operations, whereas LLMs are powerful but resource-heavy and overly broad for BSNL’s needs.