The pilot-to-production gap
Most telecom CTOs have seen impressive AI pilots. Predictive fault detection. Capacity forecasting. Anomaly detection. Optimisation recommendations.
Yet far fewer have seen these systems operate reliably in live networks at scale.
The gap between pilot and production is where most AI initiatives fail — not because the idea is wrong, but because production imposes a fundamentally different set of requirements.
Understanding what changes at this transition point is critical.
In production, accuracy is not enough
In pilots, model accuracy is often the primary success metric. In production, accuracy is only one dimension.
Live networks demand:
- Predictable behaviour under edge cases
- Clear confidence thresholds
- Known failure modes
- Graceful degradation when data is missing or delayed
A highly accurate model that occasionally behaves unpredictably is unacceptable in a network context. CTOs must demand systems that are robust, not just precise.
Data behaves differently in the real world
Pilot datasets are typically cleaned, aligned, and historically complete. Production data is not.
In live networks:
- Telemetry arrives late or out of order
- Data sources drift as vendors update software
- Instrumentation gaps appear during incidents
- New network elements behave differently from legacy ones
Production AI systems must be designed to handle imperfect data without cascading failures. This requires defensive data engineering, validation layers, and explicit assumptions about data quality.
For CTOs, data engineering often matters more than model sophistication.
Integration becomes non-negotiable
In production, AI systems must integrate with:
- Fault and performance management systems
- Change and incident workflows
- Capacity planning and investment processes
- Field operations and workforce tools
This integration introduces latency, dependency management, and security concerns that pilots rarely encounter.
CTOs should expect production AI systems to:
- Operate within existing enterprise integration patterns
- Respect change management processes
- Support rollback and override mechanisms
- Log and audit all actions
If an AI system cannot integrate cleanly, it cannot be trusted.
Human trust becomes the bottleneck
In pilots, teams are enthusiastic. In production, operators are cautious — and rightly so.
Network engineers must trust AI outputs before acting on them. This trust is earned through:
- Explainable recommendations
- Consistent performance
- Clear escalation paths
- Gradual automation maturity
CTOs should plan for progressive adoption, where AI starts as advisory, then moves toward automation as confidence grows.
Forcing automation too early erodes trust and sets programmes back months or years.
Operational responsibility must be explicit
In production, questions arise immediately:
- Who is on call for the AI system?
- How are incidents triaged when AI is involved?
- What happens when AI and humans disagree?
- How is performance reviewed over time?
If these questions do not have clear answers, AI systems will be bypassed or switched off.
CTOs who succeed assign operational responsibility as explicitly as they would for any network platform.
Production is where AI either becomes strategic — or irrelevant
The transition from pilot to production is not a technical milestone. It is an organisational and operational one.
CTOs who approach this transition deliberately — with governance, integration, and operational discipline — turn AI into a durable capability.
Those who do not accumulate impressive demonstrations and limited value.
In telecom networks, production is not a destination. It is the environment AI must survive in every day.