From Pilots to Production: CSPs Embed AI for 60% Fewer Billing Queries and 95% Faster Product Development

How AI Is Transforming Operations for CSPs

AI is moving from pilots to the core of telecom operations. It's embedded, measurable and delivering results. Industry data shows AI-driven automation can cut billing queries by up to 60%, proving the value where it matters: cost, speed and customer experience.

Embedding AI into core telecom systems

The shift is clear: experiments are giving way to production-grade AI inside operational IT. Research from MIT points to organizations integrating AI within daily workflows, not keeping it in the lab. Source

For CSPs, that means building AI into BSS and OSS so it scales, complies and stays reliable. Cerillion's "Bring Your Own AI" model is a practical example, supporting major public LLMs and private AI engines. Teams get automation without deep AI expertise or complex integration work.

Faster product and marketing execution

Product teams can sketch a new offer on a whiteboard, upload a photo to the Enterprise Product Catalogue and receive an auto-generated configuration. Operators report up to 95% faster development, turning weeks into hours.

On the commercial side, AI-driven promotions tailor offers with granular behavioral data. Teams are seeing faster campaign turnaround, better lead quality and higher customer lifetime value-without piling work onto marketing ops.

Delivering outcomes that matter

CSPs embedding AI into core processes get durable gains: campaign ROI uplift, smoother lead conversion, fewer manual errors and leaner operations. Analyst coverage also flags embedded AI as a differentiator at scale, reflecting where the market is heading.

Bottom line: AI works best when it's part of the system, not an add-on.

Beyond billing: network, care and security

AI now supports predictive maintenance, network optimization and fault detection. By analyzing live telemetry, systems can forecast failures, route traffic to avoid congestion and improve uptime.

Virtual assistants handle routine service interactions to cut wait times and free agents for complex cases. Vodafone's TOBi is a clear example, handling a large share of customer queries independently. Learn more

Security teams benefit too. AI-driven threat detection spots anomalies faster and guides response across large, distributed environments.

Preparing for what's next

5G, IoT and edge computing increase operational load. AI systems with "sense, think, act" loops are stepping in-self-healing networks, dynamic resource allocation and smarter routing are becoming standard practice.

Conversational AI at scale is already proving its value in service delivery and support, providing a blueprint for other workflows.

Your practical playbook

• Start where the friction is highest (billing, assurance, care). Baseline KPIs before rollout.
• Integrate AI at the BSS/OSS layer via APIs and event streams. Avoid isolated tools.
• Adopt a BYO-AI approach to switch models as needs change. Enforce prompt, data and access controls.
• Invest in data quality: tagging, lineage and governance. Bad inputs kill ROI.
• Stand up MLOps: CI/CD for models, monitoring, drift alerts and rollback paths.
• Keep humans in the loop for high-impact decisions. Automate, then supervise.
• Bake in compliance, privacy and security from day one.
• Upskill teams so operations, IT and dev share the same playbook. See curated options for telecom and operations roles here.

Final take

Embedding AI inside BSS/OSS and core workflows isn't optional anymore. It's how CSPs reduce cost-to-serve, speed up product cycles and deliver customer experiences that scale.

Vendors like Cerillion show that integrated AI can be deployed without adding technical debt. Move past pilots, measure aggressively and treat AI as operational infrastructure. That's how you win on efficiency and growth-now, not later.