AI Adoption Hub: Making AI adoption visible and measurable
Full portfolio visibility
One registry of all AI use cases, status, owner, system access, and value hypothesis in one place.
Standardized intake
Guided forms that capture problem, data sources, privacy flags, KPIs, and stakeholders, no more ad-hoc pitches.
Maturity & risk scoring
Score each use case on Responsible AI, data sensitivity, model risk, and business readiness.
ROI & impact tracking
Define baselines and KPIs, measure cost saved, and share board-ready summaries.
Policy & review workflow
Map policies to use cases, route for Legal/Sec/Privacy review, and keep an audit trail.
Roadmap & resourcing
Prioritize by value, plan sprints, and track dependencies and budgets.
Turn scattered AI experiments into an accountable roadmap
Centralize visibility across teams, standardize approvals, and measure results. Build momentum with clear priorities and evidence of value.
Organization wide dashboards
See adoption by department, risk distribution, maturity scores, and value delivered, updated in real time.
Intake templates
Consistent “new idea” forms capture problem, data, compliance flags, metrics, and owners, easy to compare apples to apples.
Governance workflow
Route use cases to Security, Privacy, Legal, and Finance with SLAs and audit logs. No more email chains.
ROI & KPI analytics
Track cost avoidance, revenue impact, cycle-time reduction, satisfaction, and adoption (MAU) per use case.
Policy & risk models
Configure scoring models (e.g., data sensitivity, model risk tiers, human-in-the-loop) to match your standards.
Testimonial
“Before the Hub we had zero visibility, now we have a single source of truth and can finally prioritize by value and risk.”
