Know exactly where AI will matter — before you spend a dollar.
We map your operational workflows end-to-end, quantify inefficiencies, and identify the specific processes where AI-driven automation or augmentation will generate the highest return — using a structured four-feasibility assessment model.
Four-Feasibility Assessment
Technical Feasibility
- Sufficient quality data available?
- Suitable algorithms or models exist?
- Infrastructure for production scaling?
Operational Feasibility
- Fit into existing workflows?
- Process or role adjustments needed?
- Retraining requirements?
Financial Feasibility
- Realistic cost estimation (dev, test, deploy, maintain)
- Conservative ROI projections
- Guard against vendor-inflated savings claims
Risk Assessment
- Security and privacy vulnerabilities
- Bias potential
- Regulatory and compliance exposure
- Reliability for incorrect decisions
Four-feasibility use case assessment with structured scoring rubric
Adapted from Wendt, AI Strategy and Security (2025)Value-Versus-Effort Prioritization
After assessment, every candidate use case is plotted on a two-axis matrix: expected value versus effort. This yields four quadrants — quick wins, strategic bets, incremental gains, and deprioritize — giving leadership a clear visual framework for investment decisions.
Tri-Level KPI Framework
- Model Performance KPIs: precision, recall, F1 score, AUC-ROC
- Business Outcome KPIs: revenue growth, cost reduction, customer satisfaction, employee productivity
- Operational Efficiency KPIs: model training time, data processing speed, infrastructure costs
Tri-level KPI framework for measuring AI impact across model, business, and operational dimensions
Adapted from Zabala, Grow Your Business with AI (2023)Typical Engagement: 3–5 weeks | Outcome: Prioritized automation roadmap with financial justification, feasibility scores, and KPI definitions.