Systematic framework for AI training program evaluation and strategic implementation
Organizations struggle with both training AI effectively and evaluating results systematically. Training relies on trial-and-error without domain frameworks (PESTLE, SWOT, Porter's). Improvements lack statistical validation (is it skill or luck?). Traditional assessment lacks calibrated standards and systematic methodologies.
End-to-end methodology combining systematic training frameworks (PESTLE, SWOT, Porter's Five Forces for domain knowledge), statistical validation (Z-tests for engineering vs luck), and 9-dimension evaluation rubric with industry-calibrated thresholds. Transforms unpredictable AI training into repeatable, measurable performance improvement.
Complete training-to-evaluation system: (1) Train systematically using domain frameworks (PESTLE for macro, SWOT for competitive, Porter's for strategy), (2) Validate statistically (Z-tests to prove engineered vs random success), (3) Evaluate with 9-dimension rubric, (4) Communicate with executive templates. Transforms unpredictable results into repeatable performance engineering.
Phase 1: Training Foundations - Domain knowledge frameworks (PESTLE for macro analysis, SWOT for competitive positioning, Porter's Five Forces for strategic analysis), systematic training protocols for knowledge transfer
Phase 2: Statistical Validation - Z-test methodology for hypothesis testing (is improvement real or random?), significance thresholds (p<0.05), sample size calculation for reliable results, engineering predictable success vs getting lucky
Phase 3: Evaluation Rubric - 9-dimension framework (Strategic Clarity, Market Analysis, Feasibility, Data Quality, Financial Modeling, Risk Assessment, Practicality, Performance Metrics, Continuous Improvement) with industry-calibrated tiers
Phase 4: Performance Calibration - Evidence-based thresholds (World-Class: 4.5+, Competitive: 4.0+, Minimum Viable: 3.5+), benchmark validation with real training programs
Phase 5: Strategic Frameworks - AI Maturity Assessment (8 dimensions), Use Case Prioritization (Value vs Effort), Build-Buy-Partner decisions, Technology Selection (LLM comparison, 100+ tools mapped)
Phase 6: Executive Communication - SCQA memo templates, board presentations (10-slide structure), stakeholder Q&A preparation, alignment matrices for multi-level buy-in
Transferable skills and capabilities beyond the technical implementation
Built training methodology using business frameworks: PESTLE for macro environment analysis, SWOT for competitive positioning, Porter's Five Forces for strategic analysis. Transforms ad-hoc training into systematic knowledge transfer with repeatable protocols.
Implemented Z-test methodology to prove improvements are engineered (not lucky). Hypothesis testing with p<0.05 significance, sample size calculations for reliability. Distinguishes skill from randomness—critical for production AI systems.
9-dimension assessment framework with evidence-based thresholds (World-Class: 4.5+, Competitive: 4.0+). Each score requires citations. Critical gaps (<3.0) trigger remediation. Performance tiers validated with real training programs.
Combined 6 strategic frameworks: AI Maturity (8 dimensions), Use Case Prioritization (Value vs Effort), Build-Buy-Partner, Technology Selection (LLM comparison, 100+ tools), Board Presentations, Vendor Evaluation. End-to-end decision support.