Context
AI use cases were developing across the organisation, but experimentation alone was not enough. The programme needed to turn separate initiatives into a reusable capability that teams could consume safely and operate reliably.
The first major use case focused on customer-service chat and call summarisation, with later expansion into quality-assurance automation.
Scale and complexity
The programme brought together Data, ML Engineering, Platform Engineering, Infrastructure, Security and Customer Operations. The underlying capability covered model training and serving, identity and access, storage, model lifecycle management, CI/CD, observability and operational support.
The technology landscape included Kubernetes and OpenStack infrastructure, Kubeflow, MLflow, MinIO, PostgreSQL, GitLab runners, Nexus/Harbor, Prometheus/Grafana and enterprise identity integration.
My role
I led the programme across the technical and operational workstreams. The job was not to architect the platform myself; it was to keep the platform foundations, ML requirements, security controls and consuming business use cases moving as one programme.
That became especially important when the priorities of the platform teams and the priorities of the business use cases started to pull in different directions.
How I structured delivery
I established one programme view of milestones, dependencies, risks, decisions and readiness, with particular attention on the interfaces between teams: environment readiness, authentication, model-serving dependencies, storage, observability, deployment pipelines and operational ownership.
A model-agnostic API layer reduced coupling between consuming applications and individual models, allowing the platform to evolve without every use case integrating directly with a specific serving implementation.
Operational readiness was part of the delivery plan from the start, including security hardening, monitoring and 24/7 support requirements.
Key delivery judgement
The main tension was between investing in platform foundations and showing useful business outcomes quickly enough to maintain momentum.
I did not treat every technical component as equally urgent. Delivery was re-sequenced around the capabilities needed to unlock working use cases while protecting the parts of the platform design that would make the service reusable later.
That avoided the opposite failure modes: a technically complete platform with no timely business value, or a collection of quick use cases with no sustainable foundation underneath them.
Outcome
The programme created a reusable foundation for enterprise AI use cases rather than a one-off implementation. For the initial automation use case, the forecast was to remove more than 9,000 hours of manual effort per month through AI-enabled workflows, while the platform created a route for further use cases to be introduced on the same capability.