From Pilots to Production: AI in Telecoms Delivers 60% Fewer Billing Queries and 95% Faster Product Setup

AI in Telecom Operations: From Pilots to Production Results

After years of trials and pilot projects, AI in telecoms is delivering measurable gains. Industry data shows billing queries can be cut by up to 60% through better automation and error reduction, freeing operations teams to focus on higher-value work.

Research from MIT points to a shift across the sector: moving from lab experiments to embedding AI directly into operational IT systems. The message is clear-value comes from practical deployment, not isolated experiments.

Within this context, Cerillion is embedding AI into the core of its BSS/OSS product suite. CSPs can use a "Bring Your Own" AI model supporting major public LLMs or private models, enabling immediate benefits without specialist AI skills or complex integration.

Cerillion has also been positioned in Gartner's Magic Quadrant for AI in CSP Customer and Business Operations, a signal that AI capabilities are becoming integral to core telecom operations. For many CSPs that consult Gartner research when shaping strategy, this recognition supports the case for embedding AI within BSS/OSS to deliver clear business outcomes. Learn about Magic Quadrants

What this means for operations leaders

• Improve campaign efficiency and ROI
• Optimise lead generation and conversion
• Grow customer lifetime value
• Increase operational efficiency across IT and business processes

Faster product setup with GenAI image recognition

Cerillion's GenAI-powered image recognition supports rapid product configuration. Teams can sketch a new product on a whiteboard, upload a picture into the Enterprise Product Catalogue, and the configuration is generated automatically-cutting product setup time by up to 95%.

Smarter promotions that move the needle

An AI-driven promotions engine enables quicker, more precise marketing execution. Offers can be aligned to customer behaviour and preferences, improving response rates without adding operational overhead.

From pilots to production: how to deploy with confidence

The priority now is to integrate AI within core BSS/OSS workflows so it scales, complies with policy, and runs smoothly alongside existing infrastructure. Treat AI as an operational capability-embedded in processes, monitored with clear KPIs, and governed like any other critical system.

Practical next steps for CSP operations teams

• Prioritise 2-3 high-impact use cases: billing assurance, product setup, and campaign optimisation are proven starting points.
• Audit data readiness: data quality, lineage, and access controls determine AI effectiveness and compliance.
• Adopt a BYO-AI architecture: support major public LLMs and private models to meet security and cost needs.
• Embed AI into change and release cycles: version prompts/configs, test in pre-prod, and track drift.
• Define outcome metrics early: target reductions in tickets, cycle time, and cost-to-serve; track uplift in ARPU and conversion.
• Plan for human-in-the-loop: approvals for high-risk actions and clear escalation paths.

Why this matters now

Third-party benchmarks and academic studies point to a sector-wide shift from experimentation to measurable outcomes. With proven gains-like a 60% reduction in billing queries and major time savings in product setup-CSPs that operationalise AI inside BSS/OSS will see faster, more durable results.

If your team is building AI capability in operations, structured training can shorten the learning curve. Explore role-based programs and certifications: AI courses by job and AI Automation Certification.