AI is pushing telcos beyond network autonomy and into a new era of business intelligence. Working with operators worldwide, Wipro explains how the industry’s next transformation will create intelligent, adaptive organizations designed to learn, decide, and grow in real time.
For much of the past decade, the telecom industry’s internal change-agenda has focused on a single objective: to build autonomous networks, and that way drive untold efficiency gains, performance improvements, and maybe some new revenue streams on top. Driven by advances in machine learning, and the grand promise of artificial intelligence (AI), operators have sought to reduce manual intervention at every turn, even as their networks have become more complex. The vision was clear: networks capable of self-monitoring, self-healing, and self-optimizing, and operators in dynamic charge of rising traffic and complexity. But something has changed.
Total industrial transformation has come into view in the new AI era – which has made all the fanciful talk about telco-to-techco reinvention both credible and achievable. In fact, clever autonomy is no longer the end goal. Instead, the mechanics have created a foundation for something more ambitious – the intelligent telco, like a strategic framework for the old tech-co rhetoric. There is an argument to say this industry, like any industry, has seen the light, that the value of AI will quickly extend beyond familiar operational workloads. Where automation improves efficiency and resilience, deep industrial intelligence stands to reshape entire business models and unlock new revenues.
The future is within reach, suddenly – or visible on the horizon, at least. Embedded network intelligence will not just drive operational autonomy in telecom companies, but change how they identify and create value. The industry’s internal focus has gone from self-optimizing networks to self-evolving operators, in charge of the same – who can learn and adapt, and make decisions and capture opportunities in something closer to ‘real-time’. Global technology services and consulting firm Wipro is at the coal-face, designing and directing this vision with telco partners. Lalit Kashyap, Vice President & Sector Head – Comms, Media & Networks, Americas and Global Head of Consulting – Telecom & Media, Wipro says they have a head of steam, thinking bigger than ever.
Leap forward
But let’s take stock, because this big leap is based on small steps. The road to autonomy has already delivered benefits. Operators have deployed analytics and automation platforms and closed-loop frameworks to improve system downtime, fault resolution, and service quality. Routine tasks are increasingly handled automatically, freeing engineering teams to focus on higher-value activities, including lighter-touch orchestration of increasingly distributed environments – covering network topologies, cloud architectures, edge computing, and interleaved software stacks. They are at DTW Ignite in Copenhagen next week (June 23-25) to celebrate their wins, and plot their next moves.
But ask TM Forum, hosting them in Denmark, about their broad progress, and it is candid: patchy, it answers. On a sliding scale, manual-to-automatic, based on a six-step taxonomy for autonomous networks (AN; officially Levels 0 to 5, realistically Levels up to 4), the industry is somewhere between Levels 1.5 and 2.1, with lots of work to do. Despite years of investment, the industry remains at relatively early stages of maturity. Many operators operate with partial automation, where AI assists human decision-making rather than independently executing actions. Still, progress is real, and brought into relief real digital transformation as intelligent telcos. The two workstreams go hand in hand.
More than this, the community’s work to leverage AI in their business decisions and models will also drive their operational strategies. Indeed, the stop-start of their autonomy gains, even as they gather pace over the next few years, highlights an important truth for telecom leaders, suggests Lalit Kashyap at Wipro – that autonomy is necessary, but not enough.
Intelligent telco
Self-managing networks address operational challenges, but do not automatically create competitive differentiation or growth. For this, operators must multiply and monetize intelligence beyond just their networks, into their wider businesses – where AI is not simply deployed as an operational tool on top of existing systems, but embedded across core processes to drive continuous optimization, predictive decision-making, and automated execution at scale. Certain characteristics define this emerging model, as tracked already in autonomous network projects, and expanded and commercialized in new intelligent telco platforms.
Lalit Kashyap, explains: “First, intelligence becomes native to operations. Rather than relying on isolated AI applications, operators build AI-driven capabilities directly into network management, service assurance, customer engagement and business planning functions. Second, decision-making becomes increasingly predictive and proactive. Instead of responding to faults, congestion or customer issues after they occur, AI systems can anticipate potential problems and take preventative action before service quality is affected. Third, automation expands beyond the network domain. End-to-end processes become increasingly automated and interconnected.”
For end-to-end, read: customer experience, service delivery, enterprise offerings, commercial operations – the whole nine yards, effectively; total digital transformation. The result is an organization capable of acting with greater speed, agility, and precision across every layer of the business.
Business intelligence
Maybe the most significant opportunity lies beyond operational efficiency – and beyond straight connectivity, for that matter. While network performance remains critical, AI is enabling operators to extract greater value from their infrastructure. Networks generate vast amounts of real-time data about service performance, user behaviour, application requirements, service conditions. Plugged into the right platforms, and exposed to the right AI models, this intelligence becomes a strategic asset in its own right, which can be made actionable and productive.
Operators are already looking to expose network capabilities through APIs to enable developers and customers to fast-track comms on network slices, guarantee their delivery terms with quality-of-service controls, and load them up with location intelligence. They are also developing their own AI-enabled services for enterprise customers, ranging from bespoke connectivity solutions to specialist industrial change bundles. Which signs the way to this broader transition of telco operators, running as dynamic connected service platforms for the whole digital economy – to connect and control and secure their AI workloads, appropriately.
This is where the original tech-co ideals start to take concrete form. The intelligent telco is not simply a more efficient network operator. It is an organization capable of monetizing intelligence itself.
Strategic catalyst
The pace of AI innovation is pushing this transformation faster than many expected. Generative AI, agentic AI, and increasingly sophisticated machine learning models are creating new opportunities to automate decision-making and orchestrate complex processes across large-scale environments. Operators are responding by embedding AI across their planning, operations, service, and product functions. The industry’s focus is on AI-native architectures designed from the outset to support intelligent decision-making rather than retrofitting AI into legacy environments – so they can direct full-stack autonomy across their network, cloud, and edge systems.
Lalit Kashyap explains: “At the network level, this means moving toward AI-native networks capable of continuously learning and optimizing themselves. At the operational level, agentic AI systems are beginning to emerge, capable of coordinating tasks, making recommendations and executing actions with minimal human intervention. At the infrastructure level, intelligence is becoming more distributed, operating across cloud, edge and network environments in real time. As a result, the role of telecom infrastructure itself is evolving.”
The whole discipline has changed: from transporting data efficiently and reliably between nodes in monolith network infrastructure to moving intelligence across digital infrastructure, between the cloud and edge, responding to micro-managed performance requirements, regulatory constraints, and security concerns in live time, with total trust and transparency.
The road ahead
While the vision is compelling, and better defined than ever, it requires significant organizational and technological change. Success will depend on operators’ ability to industrialize AI at scale, bottom-up, rather than trying to string together isolated pilot projects. It will require greater integration of data across IT and OT domains, and creation of a unified intelligence layer to support real-time decision-making. Perhaps most importantly, it will demand a rethinking of operating models themselves. Automating existing processes is not good enough. Operators must redesign workflows, organizational structures ,and business strategies around AI-driven capabilities.
Lalit Kashyap says: “Those that successfully make this transition stand to gain significant advantages in operational efficiency, customer experience and revenue generation. Those that do not risk being left with highly automated networks but limited ability to compete in an increasingly intelligence-driven market. Autonomous networks remain one of the telecom industry’s most important achievements, but they should be viewed as the beginning of the journey rather than its destination. The next era of telecom will be defined by organizations that can continuously learn, adapt and create value through intelligence embedded across every aspect of their operations.”
For operators, the challenge is no longer simply building networks that can run themselves. It is building businesses that can think for themselves. And in the emerging AI-driven telecom landscape, that distinction may ultimately determine which operators lead the industry into its next phase of growth.
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