Can AI Rescue Telco Revenue Decline?

The global telecommunications industry faces overwhelming change as digital transformation influences consumer behaviour and market dynamics.

Whilst digital transformation across the world brings great positives and potential, industries like telecommunication are forced to grapple with challenges - like traditional revenue streams from voice calls and text messages declining as consumers shift to internet-based communication platforms.

Meanwhile, infrastructure costs continue to rise as networks upgrade to 5G technology.

This shift has forced telecommunications providers to seek new ways to maintain profitability while competing with technology companies that now dominate digital services.

In this context, AI has emerged as a potential solution for telecommunications companies seeking to reverse falling revenues.

McKinsey, the global management consulting firm, reports that Gen AI could increase earnings before interest, taxes, depreciation and amortisation (EBITDA) margins by 10% by 2029.

The path to AI integration

Telecommunications providers aim to become "AI-native" organisations, where AI integrates into core business operations rather than serving as an additional feature.

This approach requires fundamental changes to how companies operate, from customer service to network management.

McKinsey indicates that telecommunications companies can lead in deploying both Gen AI for customer experience improvements and analytical AI for operational optimisation.

However, this transition presents challenges amid increasing regulatory requirements and ethical considerations.

How else is AI transforming telecommunications?

Gen AI is already making its mark in the industry, with several telecom operators reporting significant improvements in key areas, including enhanced marketing campaigns and improved customer service.

Yet at a more granular level, AI is also addressing some of the telecommunications industry's most pressing challenges, including revenue decline and operational efficiency:

Network optimisation
AI algorithms are being used to analyse factors such as user demand, traffic patterns  and geographic conditions to optimise network performance and resource allocation.

Predictive maintenance
AI-powered predictive maintenance helps telecom operators anticipate and prevent network failures, minimising downtime and improving user satisfaction.

Security enhancement
Machine learning algorithms are being employed to monitor network traffic patterns, detect anomalies, and mitigate security threats in real-time.

Responsible AI implementation 

Yet with the power of AI comes ethical responsibilities.

The implementation of responsible AI (RAI) frameworks has become essential for telecommunications companies to maintain consumer trust and data security.

These frameworks establish guidelines for accountability and transparency in AI deployment.

"Forward-thinking telcos recognise that robust RAI governance serves as a set of good brakes that enable them to drive faster to harness the full potential of AI while mitigating risks," McKinsey says.

The lack of industry-standard frameworks presents an obstacle to widespread adoption, forcing many operators to adapt their approach as new regulations emerge.

McKinsey advocates for telecommunications-specific RAI frameworks that address unique industry challenges and include clear guidelines and progress metrics.

"By prioritising RAI, telecom operators can capture the full potential of AI for their businesses and build trust with customers, leading to new innovations and new revenues," McKinsey concludes.

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