Image
When Birlasoft first engaged with the client’s operations and engineering leadership, the discussion did not start with AI strategy or transformation roadmaps. It started with scale.
The organization had already committed to enterprise copilots. Adoption was no longer the question. The challenge was what came next—how to run, govern, and extract value from more than 5,000 copilot instances spread across network operations, customer service, billing, and engineering teams.
They were seeing pockets of success. Some teams moved faster, while others struggled. As usage increased, the operational load grew with it. Configuration drift, license sprawl, uneven performance, and compliance oversight were becoming daily concerns.
They were not looking for another platform. They were looking for an operating model.
Impact Realized by the Client
Within the first quarter of steady-state operations, the client reported measurable improvements:
- 50% improvement in network incident response times
- 35% increase in customer service efficiency
- 45% reduction in manual billing reconciliation effort
- 5 Faster SDLC cycles due to improved test readiness and release validation
- 5 Improved operational visibility through unified dashboards
Where the Strain Was Emerging
The pressure points were not dramatic failures. They were smaller, persistent frictions
For instance, in network operations, incidents still require multiple handoffs. Monitoring data existed, but correlation and context took time. Engineers often had the information they needed—just not in one place.
Customer service teams were using copilots, but efficiency varied widely. Some agents resolved issues faster; others spent time validating responses or searching for supporting information.
Billing teams faced a familiar problem. Reconciliation was accurate, but manual. Exceptions were reviewed line by line. AI assistance helped, but without orchestration, the workload simply shifted rather than reduced.
Within engineering copilots were present across the SDLC but lacked uniformity. Test generation worked well in some pipelines and barely at all in others. Release readiness still depends on manual checks. The potential for agentic AI for SDLC acceleration was clear—but not consistently realized.
A common theme resonated across all domains:
Copilots were deployed on isolated instances; however, they were not being operated as a system.
Birlasoft’s Approach
We began by observing how copilots were used in daily operations. Not how they were designed to be used—but how teams relied on them during real work.
Network teams walked through incident bridges. Customer service leaders shared quality review notes. Billing analysts showed reconciliation spreadsheets. Engineering managers demonstrated release checkpoints that still depended on tribal knowledge.
Instead of a typical large-scale AI transformation, Birlasoft focused on managed control and targeted autonomy built on strong guiding principles:
- Treat copilots as enterprise tools
- Introduce agentic behavior only where it reduced measurable effort
- Build governance and operations first, optimization follows
- Improve SDLC velocity without destabilizing production systems
Where We Introduced Change
Copilot environments were standardized and centrally monitored. Copilot capabilities were applied end‑to‑end across operations, customer-facing functions, finance, and engineering moving from standardized foundations to targeted, role specific and agentic workflows where automation delivered the most impact. Deployment patterns were unified so that new copilots followed consistent configuration, security, and performance baselines.
License management was automated. Idle and under-utilized licenses were reclaimed. Instead of being anecdotal, usage trends became clear. Once that framework was in place, agentic AI capabilities were added one at a time. In network operations, agentic workflows helped with incident triage by linking warnings, recent changes, and past patterns before they got worse.
In customer service, copilots were grounded with approved knowledge sources and guided resolution steps, reducing validation overhead for agents.
AI agents in billing highlighted unusual transactions and made summaries for reconciliation, which let analysts focus on real exceptions.
Agentic AI was used directly in the SDLC by engineering teams:
- Automatically created baseline test cases for new code pathways
- Defect signals were grouped together in different pipelines and contexts.
- Before approvals, release readiness tests made sure that dependencies, configurations, and change risks were all correct.
Solution Architecture
The solution used a controlled, composable design. A supervised copilot layer took care of lifecycle tasks including enabling, configuring, customizing, monitoring, and optimizing. An agentic AI layer took care of specific duties including sorting incidents, making tests, checking deployments, and analysing reconciliations.
An operations and governance layer enforced identity controls, usage policies, audit logging, and compliance monitoring across all copilot instances.
Why It Worked
The engagement worked because it was more about usability than novelty. Copilots were regarded institutionalized as corporate services, with SLAs, controls, and responsibility. Agentic AI was only used when it made things easier or safer in ways that could be measured. Governance was built in from the beginning and not an afterthought. The critical factor was that the model fit with team’s existing ways of working. It strengthened existing workflows rather than inventing new ones.
What This Means for Telecommunications Leaders
Value does not come from deploying more copilots. It comes from running them well.
A managed Copilot-as-a-Service model, combined with agentic AI for SDLC acceleration, allows enterprises to move faster without losing control, turning experimentation into repeatable performance.