What actually works when you bring AI into operational workflows — from ticket triage to scoring models to the GenAI tools landing in every ops team’s inbox.
In February 2022 I led one of the earlier enterprise AI deployments in a CX operation — a Forethought triage model that replaced the manual classification work of 6 FTEs at 89% accuracy, saving $200K annually. The model worked. But it worked because the data infrastructure was right first.
Most AI implementations in ops fail for the same reason most BI implementations fail: the data going in is wrong, the metric definitions are inconsistent, and nobody has done the governance work that makes outputs trustworthy.
This pillar covers AI from the practitioner seat — what to evaluate, what to build before the model, and how to measure whether it’s actually working.
“The AI is only as good as the data it’s trained on. Fix the data problem first, then the model problem.”
What an enterprise AI deployment actually looks like from the inside — vendor evaluation, data preparation, rollout, and the metrics that tell you if it’s working.
Building automation into operational workflows — ticket routing, alert thresholds, report generation, and the governance layer that keeps automated outputs trustworthy.
What GenAI tools actually change for ops teams — documentation, knowledge base management, report summarisation, and the governance questions nobody is asking yet.
In February 2022 we deployed Forethought’s AI triage model across the support operation at Q4 Inc. The model replaced the manual classification work of 6 FTEs, achieving 89% accuracy across 54,821 predictions in year one and saving $200K annually. One of the earlier enterprise AI deployments in a CX operation — before AI implementation became a default item on every ops agenda.