Lumen Technologies, a U.S. telecommunications and IT services company serving enterprise and government customers, deployed a generative AI network diagnostics platform built on Databricks with Meta Llama 3.3 as the foundation model, designed to help contact-center engineers resolve more than 3 million annual service incidents faster and cut the time spent walking through diagnostic runbooks.
"We need to make our network engineers and contact center agents smarter and faster — and we're doing that by applying generative AI at the heart of Lumen's operations," said Lumen CIO and Chief AI Officer Rabbit Nahas. "Our network diagnostics are enhanced with generative AI to allow engineers to move at a pace never before seen in the industry."
The platform was built because traditional diagnostic runbooks required Lumen's engineers to navigate through dozens of screens to identify root cause for connectivity, latency, and service degradation incidents across the company's fiber, SD-WAN, and edge network footprint. Contact-center agents handling inbound trouble tickets similarly needed 8-15 minutes of scripted diagnostic questioning per call before being able to dispatch a field technician. Lumen chose Databricks as the data and AI platform because the company already used Databricks Mosaic AI to evaluate models, and the platform's data governance layer allowed the diagnostics team to fine-tune Llama 3.3 on historical incident notes, runbook completions, and resolution patterns without copying sensitive customer telemetry outside the company's existing data perimeter.
The technical architecture combines retrieval-augmented generation over the company's historical incident database with structured tool-use calls into Lumen's existing diagnostic APIs. When an engineer asks a natural-language question about an active incident, the system retrieves the most similar prior incidents, summarises the diagnostic path that resolved them, and proposes the next runbook step for the current case. Contact-center agents access the same underlying model through a simplified chat interface that maps conversational queries into the same tool calls. Lumen implemented prompt-evaluation pipelines and PII-detection guardrails before deploying the system to general availability.
The operational impact since rollout has been a lift in Tier-1 deflection — the percentage of contacts resolved at first contact without escalation to a senior engineer or field technician — from a baseline of 30% toward a target of 50%. Lumen is targeting annual savings of 3,675 engineering hours previously spent walking through diagnostics for high-volume service incidents, plus a reduction in average handle time on contact-center calls that previously required multi-step scripted questioning. The platform is now being extended to additional Lumen business workflows, with plans to incorporate agentic capabilities that can autonomously trigger diagnostic commands and recommend field-dispatch decisions.