Business intelligence and AI transformation, wherever the data lives.
The platform pattern doesn't change by industry: clean data, real governance, and AI that's actually in production. What changes is the domain on top of it. Below is a look at where that's been proven, and how AI transformation differs from (and builds on) plain digital transformation.
Not the same project. One usually has to come first.
"Digital transformation" and "AI transformation" get used interchangeably. They shouldn't be.
Digital Transformation
- Process digitization, paper to system-of-record
- Legacy system modernization
- Cloud migration & consolidation
- Self-service BI & reporting
- System-to-system integration
Shared Foundation
- Clean, governed data architecture
- Cloud platform & DevOps discipline
- Change management & user adoption
- Security & compliance controls
- Measurable KPIs tied to outcomes
AI Transformation
- Predictive & generative models
- Multi-agent automation & orchestration
- Autonomous, AI-driven decisioning
- Continuous model monitoring & governance
- Retrieval-augmented knowledge systems
Most "AI transformation" failures are actually digital transformation gaps wearing an AI label: ungoverned data, no cloud discipline, no change management. The shared foundation in the middle is what makes AI transformation possible at all, which is why it's the first thing we assess, and often the first thing we build.
Where this has been proven.
Four industries, four very different data problems, the same underlying platform discipline.
Value-based care & health data
Population health, risk adjustment, claims and clinical data exchange, MDM/EMPI, and a multi-agent AI platform running inside a regulated environment.
Project, financial & field data
Real-time integration across project management, accounting, and preconstruction platforms, unified into one operational data layer.
Legacy modernization
Financial reporting and infrastructure asset data moved off legacy mainframe/DB2 and Access silos onto a governed SQL Server warehouse.
SAP sales data, made usable
A governed sales analytics warehouse sourced from SAP, with row-level security so each role sees exactly their slice of the business.
Insurance and financial services are target markets for this practice, regulated, data-heavy, a strong fit for the same platform pattern, but not yet backed by delivered engagements the way the four industries above are. Worth a conversation if that's your industry; just being straight about what's proven versus what's positioned.