Most AI tools assume clean data, modern APIs, and a team that knows what to do. Most businesses have none of those. The software is $20 a month. Making it work costs ten times that — because the real work is everything the vendor skips.
Data readiness comes first
60% of UK businesses cite limited AI skills as a barrier to adoption. 80% neither use nor plan to use AI. Before deploying anything, I look at where the data actually lives — HL7 feeds from a lab system that drops fields, a CRM with 15 years of free-text notes nobody cleaned, PDF reports that were never digitized. Then I determine whether AI can work with what exists today or whether the plumbing needs fixing first. No one benefits from automating garbage.
Integration is the expensive part
A language model that cannot reach your data is a toy. I connect AI to the systems you actually run — EHR platforms, financial databases, legacy CRMs, file shares full of PDFs. HL7, FHIR, MLLP, proprietary exports. The integration respects your existing permission model, audit requirements, and data residency rules.
Production deployment, not a proof of concept
A demo that works on sample data proves nothing. I deploy where the real data lives, with real users, under real compliance constraints. Monitoring from day one — not as an afterthought, but as part of the system. If something drifts, you know before it becomes an incident.
What ships
Data readiness assessment with a clear go/no-go. System integration between AI and your existing tools. Production deployment with monitoring, logging, and audit trails. A running system, not a slide deck.