Automation & AI
Automation is a design problem before it is a tooling problem.
Where automation and AI earn their place, where they do not, and what has to be true operationally before either is worth starting.
Request an executive briefingAutomation & Orchestration
Infrastructure and process automation designed around the exceptions, not the happy path.
Explore Automation & Orchestration →AIOps
Signal before automation. Alert noise and correlation are fixed before anything is allowed to act on its own.
Explore AIOps →AI Strategy & Governance
Use cases ranked by measurable value, gated on operational readiness, and governed once in production.
Explore AI Strategy & Governance →Automation & Orchestration
Automation applied to an unstable process produces faster failure, not fewer failures. The work starts with the process as it actually runs, including the exceptions people currently handle by hand, because those are what determine whether automation holds. What gets automated is then defined by triggers, handoffs, failure behaviour and rollback — not by the tool.
- Processes redesigned before they are automated, with exceptions and human checkpoints defined
- Failure behaviour and rollback specified as part of the design, not discovered in production
- Auditability built in, so an automated action can be explained after the fact
AIOps
Operational automation is only as good as the signal underneath it. Most estates generate far more alerts than any team can act on, with correlation weak enough that the same incident arrives five times. Automated remediation introduced on top of that amplifies noise. Signal quality is assessed first; automation follows where it is supported, inside explicit guardrails.
- Alert volume and correlation assessed before any remediation is automated
- Runbook coverage mapped against the incidents that actually occur
- Automated remediation limited to cases where the signal justifies it, with defined guardrails
AI Strategy & Governance
AI initiatives rarely fail on the model. They fail on unclear ownership, unreliable data, undefined workflows and absent governance. Use cases are therefore assessed for measurable value and for operational readiness, and funded in that order. Governance runs alongside delivery rather than ahead of it, and is proportionate to the risk of the system in question.
- Use cases scored on value; rejected candidates recorded with reasons
- Funding released in stages against defined readiness criteria
- Governance proportionate to risk, satisfying AI Act obligations where they apply