Four Ways Operators Are Using AI to Increase Competitiveness

This article was first published in Telecoms.com.

Telecom operators are facing a pivotal time. Pressures that have been building for the past decade are coming to a head. Connectivity revenues are flat, and according to PwC, the market is expected to grow by a CAGR of just 2.9% through to 2028, far below the projected rate of inflation. Meanwhile, costs continue to rise, and the two major waves of transformation operators invested in – cloud and 5G – have yet to deliver the returns many expected.

The temptation for operators might be to double down on operational efficiency and protect profit margins, but focusing only on that would be a mistake. AI is reshaping the entire technology sector at a pace that would have been unthinkable only a few years ago, and it offers operators a rare chance to break out of the reactive, incremental mindset that has held them back. The industry has a history of watching seismic opportunities pass it by; missing the internet era is still the cautionary tale no one wants to repeat, and AI cannot become the next one.

To seize this moment, operators will need to rethink their approach to technology. After years of absorbing one wave after another, from virtualisation to the cloud to 5G, there is very little appetite for another investment that lacks a clear business case. And AI, to be fair, introduces real uncertainty. Cost models based on token consumption, questions around sustainability, and the operational risk of handing mission-critical decisions to systems that are still maturing all give pause. 

This is where the next phase of AI matters. The answer is not unchecked autonomy, but agentic AI: systems that can reason, plan, and act toward defined goals, while operating within clearly enforced boundaries and under human oversight. Increased competition, rising customer expectations, and other industries aggressively encroaching on relationships operators once owned mean operators are starting to embrace agentic AI. To become more competitive, operators are approaching AI as a top-down reinvention that delivers immediate value, reinforces trust, and positions them to compete in entirely new ways.

Here are four ways that operators are doing this:

AI Shifts from Automation to Revenue Creation

Within the next 12 months, operators will begin moving past the instinct to use AI only for cost reduction and basic automation. Reduced cost and increased efficiency will continue to be the main drivers for AI, but now operators are starting to use AI to increase revenue growth. The first phase of AI adoption has largely focused on “helping do what we already do,” but the next use cases will focus on using AI to reshape how operators engage customers and bring services to market.

Agentic AI accelerates this shift by moving beyond insight generation into coordinated action. Instead of simply identifying opportunities, AI agents can orchestrate next-best actions across marketing, sales, and service—while still operating within predefined commercial and regulatory guardrails. Hyper-personalised offerings, proactive recommendations, and truly holistic customer understanding will become essential as operators fight to retain relevance in a world where other industries increasingly compete for ownership of the customer relationship.

To better compete, operators are now rolling out a range of beyond connectivity services. These include digital services ranging from education to entertainment to ride sharing for consumers and technology solutions for enterprise customers. In B2B, operators are partnering with tech providers to sell complex technology solutions and here we are already seeing agentic AI support and enhance B2B sales management. This shift is driving new revenue streams beyond connectivity while also delivering more relevant, timely solutions to enterprise customers. AI—especially agentic AI—is the engine that turns customer data into commercial execution at scale.

Use of MCP (Model Context Protocol) to Integrate Systems and AI Agents

As agentic AI expands across operators, MCP is set to become a key enabler. Introduced by Anthropic in 2024 and now supported by major players like OpenAI and Google, MCP is an open, neutral standard for integrating AI with external tools, data, and systems. Donated to the Linux Foundation’s Agentic AI Foundation, MCP is emerging as the industry standard for AI integration.

For CSPs operating in complex environments with many IT systems, MCP acts as a universal “plug-and-play” layer between AI models and business systems such as BSS, CRM, billing, charging and network tools, complementing standards like TM Forum Open APIs. By enabling secure, bidirectional access to data and workflows, MCP allows users to interact with BSS and monetization systems through natural language. This unlocks AI-driven efficiency across sales, customer care, marketing, and personalisation, fundamentally changing how CSP operations are executed.

Operators Get Serious About Data Cleanup

The promise of AI will collide with a familiar obstacle. Operators cannot extract meaningful value from advanced models if their underlying data remains fragmented, inconsistent, or unreliable. Telcos have spent years trying to unify customer, network, and usage data for analytics and business intelligence, but agentic AI raises the stakes even higher.

Because AI agents act on data rather than simply analyse it, poor data quality becomes an operational risk, not just an analytical limitation. If the inputs are flawed, the actions will be flawed. The operators that make the most progress in the coming year will be those that treat data quality as a strategic prerequisite rather than an IT housekeeping exercise. Trustworthy, governed, real-time data is what allows agentic systems to operate safely, predictably, and at scale—whether that is in customer engagement, service assurance, or network planning.

“Sovereign AI” and Agentic Services Create New Revenue Potential

Enterprises are eager to tap into AI, but many are becoming equally concerned about where their data resides, how it is processed, and whether AI systems operate within regional privacy and regulatory boundaries. This concern opens a new opportunity for operators: delivering sovereign, agentic AI services that run inside protected, country-specific environments.

Operators already have hard-earned reputations for trust, security, and compliance—qualities that become even more valuable as AI systems move from advisory roles to active execution. By offering secure, locally anchored AI platforms that support agentic workflows, operators can extend their role beyond connectivity and position themselves as trusted AI execution partners for enterprises. This goes beyond hosting models; it enables enterprises to deploy AI agents that can operate on sensitive data and comply with local guidelines and regulations.

AI is everywhere, but it demands a different kind of ambition from operators. It must be treated as more than just another incremental technology wave. Agentic AI, in particular, challenges operators to rethink how decisions are made, how actions are executed, and how value is created across the business. Success will depend on disciplined data foundations, human-centred governance, and the confidence to pursue new revenue models with clarity and intent. Operators cannot afford to let another opportunity pass by.

Matthew Halligan, CTO
Qvantel