Case study · Scalable Service & Operations Automation
Enterprise: Building the capability to automate business at speed
Led and delivered by founder Kannan Durairaj, prior role. Client identity withheld.Challenge
As AI-enabled automation expanded, teams risked adopting different tools, duplicating engineering effort and applying controls inconsistently. The organisation needed a shared approach that could support wider adoption without rebuilding the foundations for every project.
Approach
Build reusable capability. Shared tools and engineering patterns gave teams a common starting point, reducing the need to solve the same problems repeatedly.
Embed governance and security. Model validation and security practices were integrated into delivery, bringing engineering, risk and security into one workflow.
Establish consistent delivery practices. Cross-functional teams adopted common AI engineering standards, creating a repeatable approach that could be applied across projects.
Outcome
Shared tooling and reusable patterns reduced duplicated effort, more than 200 engineers adopted standardised AI engineering practices, and model validation and security checks were embedded in delivery rather than handled as separate, late-stage activities.
- Delivery performance: Shared tooling and reusable patterns reduced duplicated effort and supported more consistent delivery.
- Team capability: More than 200 engineers adopted standardised AI engineering practices, giving teams a common foundation for new automation.
- Operational control: Model validation and security checks were embedded in delivery, rather than handled as separate, late-stage activities.
Tools
- Before: fragmented
- Teams sourced and built their own
- After: reusable and governed
- Shared AI engineering tools used across teams
Practices
- Before: fragmented
- Varied by team and project
- After: reusable and governed
- Standardised AI engineering practices
Controls
- Before: fragmented
- Applied inconsistently, often late
- After: reusable and governed
- Model validation built into delivery
Security
- Before: fragmented
- A separate workstream
- After: reusable and governed
- DevSecOps embedded in the delivery pipeline
Teams
- Before: fragmented
- Specialists working in silos
- After: reusable and governed
- Cross-functional teams delivering together
| Stage | Before: fragmented | After: reusable and governed |
|---|---|---|
| Tools | Teams sourced and built their own | Shared AI engineering tools used across teams |
| Practices | Varied by team and project | Standardised AI engineering practices |
| Controls | Applied inconsistently, often late | Model validation built into delivery |
| Security | A separate workstream | DevSecOps embedded in the delivery pipeline |
| Teams | Specialists working in silos | Cross-functional teams delivering together |
Takeaway
Scaling AI requires more than successful individual projects. Shared tools, reusable patterns and embedded controls create a foundation for repeatable delivery.