
Pillar 5: SCaling ai
Scale and manage AI capabilities to harness future waves of tech advancement.
Executive Snapshot
Organizations must tune their technical capabilities to support the scaling of AI.
AI Ops builds, deploys, monitors and maintains production models—automation where possible to reduce human error.
There is a significant element of people-centric scaling and change management required here.
Why Scaling AI Matters
One-time consolidation and readiness of data combined with a few AI-driven workloads does not a future-ready organisation make.”
“In time, most organisations will turn their attention from future readiness… to scaling (and sustaining) their investment in AI and the data platform upon which it depends.
AI Ops
When we speak of AI Operations (AIOps), we’re talking about the patterns, best practices, and enabling tools used to develop, tune, test, incrementally improve, and productionise AI workloads… AIOps is a sub-discipline within DevOps, and mirrors many of the patterns (CI/CD), best practices (automate where possible) and enabling tools (Azure DevOps) found in ‘traditional’ DevOps.
Data Governance
Data governance is essential to the care and safeguarding of AI’s most important asset… Microsoft Purview provides security, quality, lineage, compliance across the data estate.
Technical Debt
Technical debt not only incinerates IT budgets and distracts from the hard work organisations must undertake to modernise for the age of AI, but, more insidiously, AI itself exposes organisations to immense risk due to the technical debt found in their existing application estates.
Monitoring & Metrics
Scaling AI is far from a purely technical endeavour… Well-rounded monitoring regimes should account for maturity & risk, adoption, content moderation, technical performance, ROI.
Digital Literacy
There is a significant element of people-centric scaling and change management required… baking AI into the way people work.
Success Checklist
✔ AIOps & MLOps pipelines operational.
✔ Purview-powered data governance baseline live.
✔ Roadmap for retiring highest-risk technical debt approved.
✔ WAU & ROI dashboards tracking every AI workload.
✔ Digital-literacy programme launched across business units.
FAQs
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AIOps uses DevOps-style automation and MLOps patterns to build, deploy, monitor and continuously improve production AI workloads at scale.
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Legacy point solutions multiply unsecured copies of data and dramatically increase the risk of data leakage into generative AI responses.
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Weekly Active Users (WAU), content-moderation flags, model accuracy and ROI for anchor workloads.