AI in Telecom: What to Fix in Legacy Infrastructure First

The global AI in the telecom market stands at $6.73 billion in 2026. And this number is expected to reach $88.11 billion by 2034. Despite the growing adoption of AI, poor data quality, integration challenges, and limited infrastructure scalability continue to constrain progress.

Many telecom companies still rely on aging systems and adding AI to these environments can expose existing weaknesses rather than resolve them.

So, should you wait for a perfectly modernized core before implementing AI?

Well, that’s not a viable option because the process can often take years. In this blog, we’ll see how to introduce AI in controlled, measurable workflows while optimizing the specific systems, data, and integrations that prevent it from scaling reliably.

Validate end-to-end telecom workflows with AI-powered testing from CoTester. Request a free trial.

TL;DR

• Technical debt, legacy systems, and disconnected data can make AI implementation challenging
• Scaling AI requires stronger data, seamless integrations, scalable infrastructure, and interoperable systems
• Targeted AI deployments can deliver immediate business value and help uncover the operational constraints which require upgrades
• Some of the critical areas where you can see measurable value of AI are order confirmation or orchestration, ticket triage, and network and IT assurance
• As telecom applications and AI-enabled workflows evolve, continuous testing helps teams verify that critical customer and operational journeys continue to work as intended.

Network Reliability Is the Ultimate Test of Customer Trust

AI is becoming a core part of how telecom companies are managing their network operations, customer service, and service assurance today. However, your customers don’t normally evaluate your AI technology investment. What they care about is the outcome.

Customers judge their providers by whether calls connect, data remains available, and services perform consistently. When service is disrupted, trust erodes, churn risk rises, and brand reputation suffers.

That’s why reliability, not the amount of AI you deploy, must remain your priority.

But here’s the challenge:

Many telecom networks are built on legacy infrastructure which has aging hardware, fragmented architectures, and systems approaching end-of-life. This can create obstacles that AI can surface, but not eliminate.

Research shows that while 19% of telcos remain in the pilot phase, 62% report data-quality or integration challenges and 47% cite a lack of scalable infrastructure.

You don’t need perfect infrastructure before you begin. However, you need enough readiness to ensure that your AI system can access reliable information, operate within defined limits, and escalate when it is uncertain.

Also Read: Why Conventional QA Fails in Telecom App Testing

Is Complete Infrastructure Readiness Necessary Before Deploying AI?

When we talk about transforming infrastructure, we broadly focus on two areas:

1. Legacy technology and operational debt

Telecom companies who depend on old platforms, custom integrations, and business processes accumulate significant technical and operational debt. These environments can make AI integration slow, increase maintenance costs, and reduce agility.

2. Fragmented systems and disconnected data

Telecom operations are normally distributed across multiple systems such as OSS, BSS, CRM, billing, and service provisioning. This can lead to data inconsistency and make it hard for AI systems to access data across workflows.

Learn More: Simplify Telecom Testing Across Devices, Networks, and Regions

Where You Can Start Using AI

The best starting points are workflows with clear boundaries, measurable outcomes, and manageable risks.

1. Order confirmation or orchestration

Order confirmation and orchestration basically coordinate the end-to-end fulfillment of telecom services like validating customer requests, provisioning network resources, and activating services across multiple backend systems.

Delays or errors here can impact your customer experience. AI can help you:

• Predict provisioning failures before execution
• Detect conflicting or duplicate service requests
• Automate routine order verification and exception handling
• Recommend the optimal fulfillment path based on network availability

You can begin improving these workflows without immediately replacing the entire OSS or BSS landscape, while using recurring agent friction to identify the systems that require modernization next.

2. Ticket triage

Ticket triage is the process of reviewing, categorizing, and prioritizing customer support requests or network incident tickets before they’re assigned to the appropriate team. Thousands of tickets are generated daily in telecom environments. Efficient triage is important to reduce resolution times and maintain service quality.

How AI can optimize the process:

• Classify tickets based on issue type and severity
• Prioritize tickets according to business impact and SLAs
• Route tickets to the most appropriate support team
• Summarize issue details to reduce manual review time

You can incorporate AI with your existing service desk, OSS, or incident management tools to automate classification, prioritization, and routing.

3. Network and IT assurance

Network and IT assurance is about monitoring telecom networks, apps, and supporting IT systems to maintain service availability and performance. It helps you identify faults, assess their impact, and resolve issues before they affect customers.

With AI, you can:

• Spot anomalies across network and system performance data
• Correlate alerts from multiple monitoring tools to find root causes
• Predict potential failures using historical trends
• Recommend remediation actions for faster resolution

These focused applications can strengthen operational visibility while showing where data, integration, or infrastructure weaknesses continue to limit performance.

Also Read: Telecom Testing Strategy: Steps to Achieve Seamless Connectivity

How to Build AI-Native Telecom Networks

1. Apply AI selectively

You should implement AI where the business impact is measurable and low risk. Instead of giving AI full control over all your critical operations, embed it into specific workflows which can benefit from faster analysis, better decision support, or intelligent automation.

Some of the suitable use cases include service assurance, workforce assistance, and operational planning. Here you need to make sure human oversight is available for exceptions and approvals.

This will enable you to assess performance, measure the ROI, and refine governance practices before you decide to expand AI into more complex responsibilities.

2. Modernize what limits it

The second phase is to identify the areas where modernization will get you the maximum value. You need to first spot the operational gaps which require attention as a priority. See where your AI agents or assistants fail to complete a task or need human intervention repeatedly.

This can include:

• Inconsistent or incomplete data across systems
• Fragile point-to-point integrations
• Conflicting business rules between apps
• Inefficient handoffs across operational teams
• Redundant or unnecessary workflow steps

You can monitor these recurring patterns to understand the areas which need improvement. Here’s what you can do to address these problems:

1. Streamline your product, pricing, and billing operations to reduce process complexity and minimize inconsistencies across commercial systems
2. Adopt modular OSS and BSS platform which are easy to upgrade and integrate with new AI apps
3. Establish enterprise-wide data management so your AI models can work with accurate and relevant information
4. Set strict governance and decision controls which define where AI can act autonomously and where human review or policy enforcement is needed

Learn More: How GenAI Powers Test Assurance in the Autonomous Telco

Continuous Validation Must Accompany Modernization

While infrastructural optimization is critical, as you create new apps, migrate workloads, reconfigure integrations, and redesign operational processes, the behavior of your existing apps and network ecosystem changes too.

Every change increases the need to verify that billing, ordering, customer service, and digital workflows still work as expected. Plus, as AI automates routine actions and assists with recommendations, you need to ensure these decisions are backed and reviewed by human operators.

You need continuous validation because even minor changes can affect your business and customers.

• Modified APIs may interrupt order fulfillment
• Updated integration could generate billing discrepancies
• Workflow enhancements might create delays in service activation

After every app update or process improvement, you must ensure critical business operations like customer onboarding, order processing and billing, network assurance, and self-service experiences stay intact.

You also need to validate the governance mechanisms surrounding AI-enabled workflows. Confirm that business rules are applied correctly, defined decision boundaries are respected, and actions are escalated to human operators whenever approval or judgment is required.

This is where software testing becomes essential. As telecom companies update applications, integrations, business rules, and AI-assisted processes, they need a reliable way to confirm that critical workflows still function from end to end.

CoTester helps QA and engineering teams turn requirements, user stories, and application flows into executable tests. Teams can review and refine each step before execution, run tests across real browsers, and use screenshots, logs, and step-level results to understand exactly where a workflow fails.

When application interfaces change, AgentRx, CoTester’s self-healing execution layer, detects changes to elements, labels, layouts, and structures and repairs affected locator logic during execution. This helps existing regression tests stay stable without requiring teams to rewrite scripts after every interface update.

By making application testing more adaptable, traceable, and easier to maintain, CoTester helps telecom teams validate ongoing technology changes without slowing delivery.

See how CoTester can help you generate, execute, and maintain resilient end-to-end tests. Book a demo today.