AI is increasingly dominating global telecom innovation, with communication service providers (CSPs) racing to integrate it across their networks, services and operations. Reports estimate that the telecom AI market will reach USD 46.2 billion by 2033[1], growing at a CAGR of 32.5%. This, however, has not necessarily translated into real value. While nearly all telcos across the world have piloted AI, only 12% have made impressive commercial impact to date[2].
The Fast Mode recently sat down with experts from Etiya, a leading AI-driven CX-focused digital transformation solution provider, to learn how CSPs can better navigate and design their AI deployments to realize true operational efficiencies and business outcomes.
CSPs are sitting on a treasure trove of data which can be translated into deep insights and drive better business outcomes – but only if used and processed effectively. How is AI a game-changer in this scenario?
Etiya: You’re absolutely correct – CSPs generate enormous volumes of data daily, from their network to customer care channels, billing and payment records, CRM systems, marketing and promotional systems, retail operations and more. But the question remains: How can they turn all of this data into real business value?
This is where AI comes into play. With its advanced predictive and generative capabilities, AI boasts the powerful ability to rapidly understand and uncover patterns in large volumes in data, enabling CSPs to unlock unlimited insights into their subscribers, networks, plans and programs.
In our experience here at Etiya, we notice that many AI initiatives fail because they rely on data that is fragmented, incomplete or inconsistent. So, while AI is a game-changer in extracting insights from CSP data, it is only as effective as the quality of the data and context it is provided with.
In our opinion, the conversation around AI should shift from ‘How effective is AI?’ to ‘How trusted and actionable is our data?’.
What does an effective AI value chain look like for CSPs?
Etiya: We see the AI value chain in telecoms as five core capabilities – Trusted Data, Business Insights, Decision Intelligence, Agentic Execution and finally, Autonomous Operations, each stage building on the previous one, contributing to better CX, efficiency, and driving business value.
We start with Trusted Data, which comprises a unified and centralized source of high-quality data, with robust data privacy and governance measures in place. This data is then used to derive Business Insights. For example, our Digital Twin solution processes and analyses CSP data to understand subscriber behavior and then proactively detect potential risks and opportunities.
Building on such insights, Digital Twins can simulate the outcomes of various scenarios to find the most optimal action recommendations, in line with both customer needs and business objectives. Twin-driven Decision Intelligence is then feeding into Agentic Execution, where teams of agents execute the recommended actions. Our Agentic AI solution, for instance, leverages specialized agents for each telco function that jointly coordinate, manage and perform a certain action. Finally, the last step in the AI value chain is Autonomous Operations, where multi-agent systems continuously optimize their actions to achieve the desired outcome, with human-in-the-loop governance where needed.
Can these capabilities be integrated into one platform?
Etiya: Yes – to operationalize the AI value chain, all five AI capabilities have to be integrated into the core of CSPs’ architecture. At Etiya, our Autonomous BSS platform comes with these AI capabilities natively embedded into the Intelligence and Impact layers of telco business support systems (BSS).
At the Intelligence layer, AI capabilities are powered by our Digital Twin of Customer. Digital Twins ingest all available information about an entity, such as a subscriber, and create a dynamic replica that enables CSPs to simulate different scenarios, test actions and forecast responses.
Our BSS solution aggregates data from various streams across a CSP’s architecture and then creates a 360-degree unified view of the customer. This data is then funneled into an AI engine that understands why, how, when and where a customer behaves in a certain way. With deep insights into customer behavior, CSPs can predict reactions to, for example, new plans or customized bundles. This in turn enables them to offer plans and bundles that have the highest potential take-up rate, thus maximizing revenue and customer satisfaction. In our experience, we found that twin-led predictions and hyper-personalization bring improved retention, up to a 15% increase in customer lifetime value (CLV) and a 10–18-point increase in NPS.
At the Impact Layer, our Agentic AI solution translates insight into action. Our solution handles agents end-to-end – issuing tasks to specialized agents, monitoring their performance, coordinating their actions with other agents, mapping out workflows and managing the load of each agent. Specialized agents include those designed specifically to carry out tasks in various domains such as customer care, sales, service assurance and revenue management.
A core feature is closed-loop automation, enabling agents to measure the outcomes of their actions, learn from experience and continuously refine their approach, resulting in improved decisions over time. Our solution comes with ready-made workflows for common telecom scenarios, supports build-your-own-agent for specific needs and provides APIs for seamless integration. Based on our observations, autonomous workflows have led to up to a 40% increase in first contact resolution (FCR) and up to a 60% reduction in average handle time (AHT).
These Intelligence and Impact layers build on top of the Assured Operations layer, which contains traditional BSS functions such as CRM, product catalog, CPQ, order management, customer service management and revenue management.
With AI built into the core architecture of the BSS platform, CSPs can now deploy AI features anywhere and everywhere across all business functions, enabling them to realize the true value of AI.
Why is a strategic AI approach important?
Etiya: Many operators have deployed disconnected AI projects across various departments, often delivering localized but limited efficiency or efficacy gains.
When AI is deployed in isolation, data sources remain siloed, which means AI systems do not have complete context when making a decision, leading to sub-optimal decisions, and thus sub-optimal outcomes. AI actions performed in one domain are not communicated with other domains, resulting in duplicate, conflicting or non-coordinated actions.
In scaling AI enterprise-wide, a common foundation is therefore critical. By aligning data, governance, guardrails, architecture, agents, and workflows across AI capabilities, CSPs can ensure that their AI systems work together rather than in isolation, towards the same intent and business objective.
What will differentiate CSPs who will lead the next stage of AI transformation?
Etiya: As mentioned, we predict that those who will lead in the era of AI will be those who see it as a shift in the operating model itself, rather than an add-on feature. In our view, the success of AI lies in connecting its entire value chain – from Trusted Data to Autonomous Operations – enabling CSPs to move seamlessly from data to insight, from insight to decision, from decision to execution, and from execution to autonomous operations.
As a proven technology partner, we can help CSPs roll out, scale and manage their AI capabilities with ease and reliability. Our Autonomous BSS provides the platform, intelligence, orchestration and governance needed to support CSPs’ evolution to an AI-native organization.
Learn more about Etiya Autonomous BSS >
Sources:
[1] https://www.grandviewresearch.com/industry-analysis/artificial-intelligence-telecommunication-market
[2] https://www.mckinsey.com/industries/technology-media-and-telecommunications/our-insights/telcos-ai-inflection-point-what-leaders-do-to-capture-value#/
