How ML And Satellites Are Reshaping Customer Experience

Forbes

a day ago

Glenn Katz, Chief Commercial Officer, Telesat. Senior executive with deep expertise in complex networks and innovative service delivery.
Modern enterprises depend on reliable, fast connectivity to deliver a consistent customer experience. It can make the difference to efficient operations, favorable customer interactions and sustained customer retention and growth. Most enterprises rely on telecommunications providers (telcos) for that connectivity.
Yet according to recent research cited by the World Economic Forum, communication services commoditization has led to stagnation of global telcos' revenues, with only a 0.3% compound annual growth rate (CAGR) being forecasted from 2024 to 2027. With heavy recent investment in 5G and fiber increasing the industry's debt-to-equity ratio, telco market capitalizations have only grown by 7% since 2018, compared to the 230% seen for digital platforms. This leaves telcos needing a better approach to drive growth and improve margins while still meeting their enterprise customers' requirements.
Why AI And ML Are No Longer Optional
For telcos, managing the complexity of modern networks has outpaced human capacity. The explosion of data and increasing customer expectations have made machine learning (ML) essential to delivering satisfactorily against enterprise customer expectations.
In fact, 48% of telcos say that customer experience optimization is their main goal for investing in any artificial intelligence (AI)-based technology. Some carriers have begun incorporating algorithms for managing network orchestration, identifying service issues and proactively addressing potential problems. For instance, using automated systems to flag an issue at a specific address and then dispatching a technician before the customer even realizes there's a problem.
Meeting rising expectations for consistent service has become increasingly important for competitive differentiation. However, while telcos can tightly manage and monitor the networks they own, they often rely on third-party networks for the last-mile connection.
Because they have limited to no visibility into those parties' operational performance or the challenges their networks may be having, they have to rely on whatever data the third party provides—often reactive, incomplete or insufficient. This fragmentation can limit the effectiveness of AI/ML systems in maintaining service levels or meeting customer expectations.
The result is a degraded experience for the telco's customer. Think about bank branch locations, healthcare facilities or even quick service restaurant chains. Poor connectivity that yields a poor customer experience impacts a business's efficiency, bottom line and brand.
This is especially acute in unserved or underserved areas where building out terrestrial fiber and cable is physically and/or cost-prohibitive. Such "digital deserts" lack the robust infrastructure needed for consistent network monitoring and ML-driven operations management. Now, new advances in low Earth orbit (LEO) satellite technology can make it possible to overcome that gap.
Standards-Based Interoperability: A New Role For LEO Satellites
Over the past 10 years, telecos have relied on the MEF (now Mplify) standard to enable compatibility between their Carrier Ethernet networks and those of other land-based last-mile service providers, across both Layer 2 and Layer 3 services.
This MEF standard-based compatibility and interoperability is essential to enabling the seamless extension of their terrestrial networks to wherever they lack coverage, and assuring them visibility for their AI/ML operating systems.
If emerging LEO satellite providers adopt MEF 3.0 standards, telcos could potentially fold these networks into their AI/ML operations with far less integration overhead. This would shift LEO from being a specialized fallback to a more normalized part of the network architecture, especially for underserved regions.
A LEO-supported satellite solution can also extend to unique scenarios like providing surge capacity at large public events, at special locations like airports or even in emergency situations. In a dynamic LEO satellite network, ML algorithms could detect a traffic spike and temporarily add bandwidth to boost capacity in real time. The telco's network is thereby better positioned to handle surges in demand without bespoke engineering.
Beyond network performance, this interoperability can be critical to understanding how telcos and enterprises can harmonize third-party solutions without compromising on visibility or control. By eliminating blind spots, they can extend their network monitoring, ML algorithms and customer-focused AI systems uniformly to all customer locations, whether urban or remote.
The End Goal: A Unified Ecosystem For Reliable, Scalable Service
Ultimately, this is not about ML or LEO in isolation—it's about creating a seamless, standards-based network ecosystem where machine learning, automation and customer experience converge to bring improvements that could benefit both operational teams and end users. Customers relying on telcos will gain consistent performance and visibility across all of their sites.
Telco CTOs, operations teams and network officers may benefit from reduced operational complexity in service delivery. And ultimately, the end customer—whether a multinational enterprise or an individual consumer—will have a reliable, quality and consistent experience, wherever and whenever they need it.
Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?