At DTW Ignite, Cyient stressed the continuum between network engineering, data foundation and cognitive operations that will drive autonomy, efficiency and growth
Across the telecoms industry, the language of AI-augmented networks and increasingly autonomous operations has become familiar. The harder work is turning those ambitions into infrastructure and operational realities. Operators are still managing fragmented OSS/BSS environments, multi-vendor architectures, manual operational processes and mounting pressure to improve cost efficiency while finding new paths to monetize 5G and future network investments.
For Cyient, that gap between aspiration and execution is the starting point for intelligent network modernization. As Arunav Roy, senior vice president and business head of connectivity at Cyient, explained at DTW Ignite in Copenhagen, the company’s strategy has evolved quickly as operator priorities have sharpened.
“The pace of change is much more significant than ever before,” Roy said. “Today we are talking about changes in weeks.”
Cyient’s intelligent network modernization approach is built around three mutually reinforcing layers: network engineering, data foundation and cognitive operations. The first layer focuses on planning, building and running the network itself. That includes using AI-assisted engineering, simulation and domain expertise to improve how operators think about coverage, capacity, resilience, energy efficiency and service performance across radio, transport and core network domains.
But Roy emphasized that the industry’s ability to use AI effectively increasingly depends on the data foundation. Operators have spent years talking about data quality, data readiness and OSS modernization. What has changed is the scale, speed and operational importance of the data now being generated across networks and IT systems.
“Even for AI models to be more effective, it needs clean data,” Roy said. “How do you create context around what the AI models are generating? It requires a clear foundation, a structure, and governance.”
That foundation is what enables cognitive operations. As operators pursue higher levels of automation in the longer-term pursuit of autonomy, the challenge is creating conditions for automation to be trusted, explainable and operationally meaningful. Roy noted that most operators remain at relatively early stages of automation, with many still operating at Level 1 or Level 2 on the TM Forum’s six-level Autonomous Networks maturity index. Moving toward Level 3, Level 4 and beyond requires clean data, clear use cases, standardized interfaces and a practical roadmap for how automation will be introduced.
Cyient’s approach is designed to be modular rather than monolithic. Roy said the company’s intelligent network modernization stack is powered by VISMON AI, an IP-based platform approach that is typically bundled into Cyient’s services as an enabling layer. Operators can engage Cyient around specific modules, such as network engineering, data foundation or cognitive NOC capabilities, or pursue a comprehensive modernization program.
“Our difference really comes into our ability to do multi-domain, multi-vendor [work], bring in our 30 years-plus knowledge of having solved meaningful problems for the telcos,” Roy said.
That experience also informs Cyient’s work around rApps and SMO-enabled automation. Roy said the company has built a catalog of roughly 40 rApps targeting use cases such as dynamic configuration management, conflict management and energy efficiency. The goal is to package long standing operator pain points into modular, deployable applications that can run through emerging automation architectures.
Ultimately, Cyient’s position is that intelligent network modernization is not about AI as a slogan. It is about combining telecom domain knowledge, engineering discipline, clean data and cognitive operations to help operators modernize networks in ways that are reliable, auditable and outcome-driven.
As Roy put it, Cyient is “taking a bold position” by aligning its investments with the savings, efficiency and operational outcomes operators need most. For telcos facing cost pressure, complexity and rising expectations, that may be the difference between AI experimentation and AI-augmented transformation.
Explore more from Cyient on Intelligent Network Modernization: https://www.cyient.com/connectivity
