Before the Magic: AI Readiness Assessment

1. Trust Your Data

Is your data timely, complete, and reliable, even under pressure?

We measure data latency across key systems We can detect and alert on missing or delayed data Data quality is tracked and regularly reported Business-critical telemetry is never manually curated

0/4 completed

2. Monitor What Matters

Are we tracking signals that reflect business and user impact, not just infrastructure?

Our dashboards highlight customer-facing issues We include real user experience (RUM or synthetic data) Alerting is mapped to business KPIs Execs see the same data as technical teams

0/4 completed

3. Centralise Visibility

Can all stakeholders access shared truth?

Unified platform for logs, metrics, traces No reliance on tribal knowledge Incident response uses shared data views Tools promote collaboration, not silos

0/4 completed

4. Align to Business

Does telemetry reflect real outcomes and risks?

Maps directly to user journeys Downtime is measured by user impact Business teams help define alerts Metrics are explained in business language

0/4 completed

5. Prepare for AI

Is our data model ready for explainability and trustworthy automation?

AI decisions are traceable Hallucinations/anomalies are detectable Supports low-latency inference Observability validates predictions

0/4 completed