The telecom industry is rapidly integrating AI, with 97% of operators adopting or assessing AI and 49% already implementing it in their operations to automate customer support, streamline operations and improve how they interact with customers. This industry-wide shift is set to grow, as 65% of operators plan to increase AI infrastructure investment in 2025, reinforcing AIโs role in transforming customer experience and operational efficiency.
While many mobile operators recognize AIโs potential and are eager to implement it across operations, its success depends on a strong digital foundation โ one that breaks down silos of data between channels and allows end-to-end, seamless journey completion. Without this, AIโs ability to personalize experiences, automate processes, and improve efficiency is limited.
AI canโt deliver without digital maturity
To fully unlock AIโs potential, mobile operators must elevate their digital maturity to ensure AI has the real-time device intelligence and seamless data flow needed to drive customer engagement and efficiency. At Level 1, operators may have digital tools in place, but without proper integration, processes remain siloed, and service delivery is inconsistent. At Level 3, some data connectivity exists, but AIโs impact is still limited because the necessary device intelligence is fragmented and inaccessible, preventing AI from making informed, real-time decisions. True AI-driven transformation happens at Level 5, where AI is no longer constrained by data gaps or channel fragmentation, with continuous access to device intelligence enabling real-time personalization, predictive automation and frictionless omnichannel engagement.
Many mobile operators are making progress toward AI integration but need to elevate beyond Level 3 to fully realize its value. While investments are in place, AIโs effectiveness remains limited due to a lack of remote intelligence, fragmented experiences between channels, and restricted access to customer device history. These gaps prevent AI from functioning optimally across touchpoints.ย
Without remote intelligence, AI lacks real-time device insights, making it difficult for customers and reps to make informed decisions remotely โ resulting in unnecessary store visits or service center calls and limited capacity to leverage AIโs ability to address a customer remotely. Limited access to customer device history adds to the frustration, with 56% of customers forced to repeat information when switching channels. Meanwhile, inconsistent service across digital and physical touchpoints leads to confusion, as promotions and service options vary depending on where customers engage, ultimately eroding trust.
This fragmentation directly impacts customer satisfaction, as seen in declining Net Promoter Scores. Delays in onboarding and activation due to disconnected systems lower NPS by 16 points. AI may successfully recommend an upgrade or resolve a device care issue, but if the customer goes in-store and the rep has no visibility into the interaction, they are forced to start over or repeat steps, negating AIโs initial gains. These breakdowns in continuity create frustration, reducing NPS by 19 points. A failure to offer timely trade-in or upgrade opportunities due to missing device history results in an 18-point drop.
Without device history awareness, AI struggles to personalize interactions, connect customer journeys, and identify monetization opportunities. Operators who fail to address these gaps will see AIโs potential wasted, as any initial improvements are wiped out by disjointed customer journeys.
dDLM: The digital foundation for AI-driven success
For AI to reach its full potential, mobile operators need a digitally mature framework that ensures seamless data flow, omnichannel connectivity, and real-time intelligence. Digital Device Lifecycle Management (dDLM) provides this foundation, enabling AI to function effectively across all customer touchpoints by addressing three critical capabilities:
1. Remote intelligence
On-device digital self-help tools allow customers and operator employees to engage with digital services remotely, executing self-service functions without relying on retail visits or service center calls. This capability enables delivery of real-time, context-aware engagement and proactive support, improving both customer experience and operational efficiency.
2. Omnichannel continuity
Customers expect a seamless experience across all channels, whether interacting with an app, a retail representative, or a call center agent. dDLM ensures service quality, promotions, and support remain consistent across all engagement points, eliminating discrepancies and friction when transitioning between touchpoints.
3. Device history awareness
dDLM ensures AI has full visibility into a customerโs past interactions, device condition, and service history, allowing customers and reps to pick up where they left off without starting over or repeating steps. By tracking customer experiences over time, operators can better engage customers with personalized, relevant interactions.
By implementing dDLM, mobile operators can maximize AIโs value across customer engagement, monetization, and operational efficiency. AI-powered interactions become smarter and more predictive, leading to higher engagement and conversion rates. Customers experience seamless, personalized service, reducing churn and increasing satisfaction โ 76% of U.S. customers say they would stay loyal to an operator that applies dDLM principles, while 73% would switch to a competitor offering a more seamless experience. According to internal MCE data, dDLM-driven customer care has also been shown to improve NPS by 20 points.
Beyond CX improvements, dDLM drives revenue and efficiency. Operational efficiency increases as dDLM-powered automation streamlines processes and reduces costs. With channel continuity and better device history awareness, operators have cut in-store trade-in processing times by 78% and reduced no-fault warranty claims by 50%, minimizing operational expenses. With dDLM, mobile operators can also identify upsell and monetization opportunities in real time, turning every customer interaction into a potential revenue driver. dDLM-driven engagement has been shown to increase trade-in volumes by 5x, improve rate plan upsell conversions by 20 percentage points and boosting ARPU by up to $22.
Through its digital device lifecycle management (dDLM) platform, MCE helps mobile operators bridge the gap between AI and digital maturity, ensuring seamless data flow, real-time decision-making, and frictionless customer experiences. By connecting touchpoints and optimizing AI-driven interactions, mobile operators can streamline operations, enhance customer experiences, and drive greater revenue opportunities.
