Poor data quality, silos across departments, limited access to trusted enterprise data, weak governance, and the tension between accessibility and security are less exciting to fix than a flashy AI pilot — which is exactly why so many organisations skip straight past them.
Where this gets hard
- Poor quality data leads directly to poor AI performance, regardless of model sophistication.
- Data silos across departments prevent AI systems from having a complete, accurate picture.
- Limited access to trusted enterprise data forces teams toward workarounds or lower-quality sources.
- Data governance is often immature relative to the pace of AI adoption.
- Balancing accessibility with security creates genuine tension that many organisations resolve by defaulting to restriction, stalling AI initiatives.
Where to start
- Treat data readiness as its own workstream with its own budget, not a footnote to the AI project plan.
- Prioritise breaking down the specific silos that block your highest-value use cases, rather than a generic 'fix all data' initiative.
- Establish a small number of trusted, governed data sources for AI use before scaling further.
- Modernise data governance in parallel with AI governance, not as an afterthought.
- Use tiered access models so accessibility and security can both be served, rather than treating them as a binary trade-off.
The consulting document includes a data-readiness checklist and a simple maturity scale to assess whether your data foundations can support your AI ambitions.
Would your best data scientist trust the data behind your most important AI use case?