Data & Technology Decision Framework

Strategic Case-Based Decision Framework

Each module guides executives through critical technology investment decisions with structured frameworks that address business objectives, technical requirements, risk management, and success metrics. Make informed decisions grounded in both strategic thinking and operational reality.

Module 01

Portfolio Optimization: Low vs. High Margin IT Services

Should we reduce low-margin services and invest in high-growth offerings?

Core Question

Which services deliver the highest ROI and long-term growth, and which underperform relative to cost?

Key Risks

Revenue Disruption Customer Attrition Capital Misallocation

Module 02

Demand Forecasting vs. Inventory Buffers

Should we invest in AI forecasting or maintain safety stock?

Core Question

What's the optimal balance between prediction accuracy and operational resilience?

Key Risks

Stockouts Model Drift Data Quality

Module 03

RAG Build vs. SaaS Buy Decision

Should we deploy internal RAG or purchase a SaaS solution?

Core Question

When does proprietary knowledge infrastructure justify build costs?

Key Risks

Data Security Vendor Lock-in Technical Debt

Module 04

Predictive Maintenance Investment

Should we implement AI-driven predictive maintenance?

Core Question

What failure costs justify sensor infrastructure and ML investment?

Key Risks

False Positives Integration Complexity Legacy Systems

Module 05

Pricing Optimization Strategy

Should we deploy dynamic pricing algorithms?

Core Question

How do we balance revenue optimization with customer trust and regulatory compliance?

Key Risks

Price Discrimination Reputation Damage Competitive Response

Module 06

Fraud Detection System Upgrade

Should we replace rule-based systems with ML models?

Core Question

When does ML detection justify increased false positive rates and explainability challenges?

Key Risks

Adversarial Attacks Regulatory Scrutiny Customer Friction

Module 07

GenAI Customer Service Automation

Should we automate customer service with Generative AI?

Core Question

When does AI augmentation create more value than full automation?

Key Risks

Hallucinations Brand Risk ROI Uncertainty

Technical Deep Dives

CEO Translation: Enough to ask smart questions, not build it yourself. Each concept explained in one sentence, with questions to ask vendors/CTOs, red flags to watch for, and the 80/20 rule that matters most.

Built by statisticians for executives—grounded in statistical rigor, not AI hype.

Statistical Foundations

The fundamentals that most AI frameworks ignore—start here for rigorous decision-making

SF1

Causality vs Correlation

Why correlation doesn't mean you can intervene—only causality lets you take action.

SF2

Hypothesis Testing & P-Values

Statistical proof that your result isn't just random luck—understanding significance.

SF3

Confidence Intervals & Uncertainty

Every prediction has error bars—understanding what "70% confident" really means.

SF4

A/B Testing & Experimental Design

How to test before full deployment and prove ROI with controlled experiments.

SF5

Regression Analysis

Finding mathematical relationships between inputs and outputs—the workhorse of analytics.

SF6

Sample Size & Power Analysis

How many observations you need to detect a real effect—avoid inconclusive tests.

SF7

Time Series Analysis & Forecasting

Predicting future values from historical patterns—trends, seasonality, and noise.

SF8

Bayesian Methods & Updating Beliefs

Starting with prior knowledge, then updating as new evidence arrives.

SF9

Sampling & Survey Design

How to collect data that actually represents your population—avoid garbage in, garbage out.

SF10

Statistical vs Practical Significance

Just because a result is significant doesn't mean it matters—effect size vs p-value.

General Foundations

Core decision-making concepts beyond statistics

Descriptive vs Predictive vs Prescriptive

Understanding the three types of analytics and when each matters for business decisions.

Causality vs Correlation

Why correlation doesn't mean you can intervene—only causality lets you take action.

Uncertainty & Confidence Intervals

Every prediction has error bars—understanding what "70% confident" really means.

A/B Testing & Experimentation

How to test before full deployment and prove ROI with controlled experiments.

Machine Learning & Predictive Analytics

What your data science team is building

Supervised vs Unsupervised Learning

The three main ML paradigms—knowing if you have the right data for your problem.

Retrieval-Augmented Generation (RAG)

Grounding LLM responses in your documents to reduce hallucinations.

Fine-tuning vs Prompt Engineering

Two ways to customize LLMs—understanding the cost/control trade-offs.

Vector Embeddings & Semantic Search

How computers understand meaning, not just keywords.

Time Series Forecasting

Predicting future values from historical patterns for demand and capacity planning.

Anomaly Detection

Finding unusual patterns without labeled examples of what's "bad."

Infrastructure & Operations

Why technology projects are harder than they look

MLOps Overview

DevOps for machine learning—understanding ongoing operational costs.

Cloud Trade-offs (AWS vs Azure vs GCP)

Comparing major cloud providers for AI workloads and cost optimization.

Model Drift & Monitoring

Models degrade over time as the world changes—budgeting for maintenance.

Data Pipelines & ETL

Moving and transforming data for ML—the hidden infrastructure costs.

Governance & Risk

How to stay out of trouble

Explainability (SHAP, LIME)

Understanding why models make predictions—regulatory compliance and trust.

Bias & Fairness Testing

Detecting discriminatory outcomes before they become legal risks.

Adversarial Attacks

Bad actors gaming your models—security and fraud evolution.

Data Privacy & Security

GDPR, SOC2, data residency—compliance and customer trust requirements.

Hallucination Risk Management

LLMs making up plausible nonsense—protecting brand from wrong answers.

False Positives vs False Negatives

Two types of errors with different business costs—setting the right threshold.

Tools & Templates Coming Soon

Interactive calculators, assessments, and board-ready templates to support your AI decisions

Interactive Tools

Calculators and assessments to de-risk and quantify AI investments

#	Tool Name	Description	Key Inputs	Output	Linked Modules
T1	GenAI ROI Calculator Calculator	Calculate break-even point and 3-year ROI for automating customer support with GenAI	Current ticket volume Avg cost per ticket Expected automation % Implementation cost	Break-even timeline 3-year NPV Cost/ticket savings	Module 1
T2	AI Automation Maturity Assessment Assessment	15-question diagnostic to determine organizational readiness for AI automation across data, culture, and infrastructure	Data infrastructure maturity Team ML capabilities Change management readiness Governance frameworks	Maturity score (1-5) Gap analysis Priority actions	All Modules
T3	Inventory Optimization Calculator Calculator	Compare costs of AI forecasting vs. traditional safety stock buffers with scenario modeling	Current inventory levels Carrying cost % Stockout cost estimate Forecast accuracy baseline	Optimal safety stock Working capital impact Service level trade-offs	Module 2
T4	Build vs Buy Decision Tree Assessment	Interactive decision tree weighing TCO, control, and speed-to-market for RAG implementation	Team ML expertise Security requirements Budget constraints Time to launch	Build/Buy/Hybrid recommendation 3-year TCO comparison Risk assessment	Module 3
T5	Predictive Maintenance ROI Model Calculator	Model sensor infrastructure costs vs. downtime reduction to calculate payback period	Unplanned downtime hours Revenue/hour lost Sensor infrastructure cost Current maintenance spend	Payback period 5-year NPV Downtime reduction %	Module 4
T6	Dynamic Pricing Risk Simulator Calculator	Scenario planning tool to model revenue uplift vs. customer backlash risk across price sensitivity segments	Price elasticity by segment Current revenue baseline Churn risk sensitivity Competitive dynamics	Revenue uplift range Churn impact estimate Optimal pricing strategy	Module 5
T7	Fraud Detection Threshold Optimizer Calculator	Find optimal decision threshold balancing false positives (customer friction) vs. false negatives (fraud losses)	Cost per false positive Cost per false negative Transaction volume Current fraud rate	Optimal threshold setting Expected loss reduction Customer impact estimate	Module 6
T8	Bias & Fairness Audit Checklist Assessment	Pre-deployment governance checklist to identify bias risks across protected demographics	Model features used Training data demographics Decision impact severity Regulatory requirements	Risk score (low/med/high) Compliance gaps Mitigation recommendations	Module 5, 6

Board-Ready Templates

Pre-formatted documents to present AI decisions to board, investors, and leadership

#	Template Name	Format	Description	Use Case	Linked Modules
TM1	Executive Decision Memo Template	Word (.docx)	1-2 page memo documenting decision rationale, investment required, ROI analysis, and risk mitigation for board approval	CEO documenting AI investment decision for board review	All Modules
TM2	Board Presentation Deck Template	PowerPoint (.pptx)	12-15 slide deck covering business case, technical approach, financial projections, risks, and success metrics	Presenting AI initiative to board of directors or investors	All Modules
TM3	One-Pager: AI Investment Summary Template	PDF	Single-page executive summary with problem statement, solution, investment, ROI, and timeline—perfect for quick stakeholder briefings	Quick approval from CFO or exec team	All Modules
TM4	Vendor RFP Template Template	Word (.docx)	Structured RFP with technical requirements, evaluation criteria, security/compliance questions, and pricing structure for AI vendors	Procurement process for GenAI platforms, MLOps tools, or consulting services	Module 1, 3
TM5	AI Governance Policy Template	Word (.docx)	Company-wide policy template covering responsible AI principles, approval workflows, bias testing, and monitoring requirements	Establishing governance framework before AI deployment	All Modules
TM6	ROI Tracking Dashboard Template	Excel (.xlsx)	Pre-built financial model tracking implementation costs, ongoing expenses, and realized benefits with automated charts and KPI calculations	Quarterly reporting on AI initiative performance to board/investors	All Modules
TM7	Risk Assessment Matrix Template	Excel (.xlsx)	Structured risk register with likelihood/impact scoring, mitigation strategies, and owner assignment for AI-specific risks	Risk committee review or audit compliance	All Modules
TM8	Pilot Test Plan Template	Word (.docx)	A/B test framework with hypothesis, success metrics, test duration, sample size calculations, and rollback criteria	De-risking AI deployment with controlled experiments	Module 1, 5, 6
TM9	CTO Briefing: Technical Deep Dive Template	PowerPoint (.pptx)	Technical architecture slides with infrastructure diagrams, data flows, model selection rationale, and MLOps requirements	CEO aligning with CTO on implementation approach	Module 2, 3, 4
TM10	Quarterly Business Review Slide Template	PowerPoint (.pptx)	Single slide template showing AI initiative progress: budget vs. actual, KPIs achieved, issues/risks, and next quarter priorities	Regular board updates on AI program performance	All Modules

📋 Note on Tools & Templates: These tools and templates are directional aids designed to save executives time by providing pre-built frameworks, calculations, and documents aligned to the decision modules. They are **available on request** and should be adapted to your company’s specific data, governance, and risk profile before use.

Decision Context

You're choosing between investing in sophisticated ML forecasting systems versus maintaining traditional safety stock buffers. This affects working capital, service levels, and operational agility.

Key Trade-off: Inventory carrying costs vs. forecast accuracy investment and stockout risk.

Required Data

Historical sales data with granularity (SKU, location, channel, time)
External factors: weather, promotions, holidays, competitor actions
Lead times, supplier reliability, and fulfillment constraints
Current inventory levels, carrying costs, and stockout costs
Product lifecycle stages and cannibalization patterns
Returns data and quality issues affecting net demand

Applicable Models & Techniques

Time Series Forecasting

ARIMA, Prophet, or neural approaches (LSTM, Temporal Fusion Transformers)

Gradient Boosting

XGBoost/LightGBM for incorporating external features and interactions

Ensemble Methods

Combine multiple models to improve robustness and reduce variance

Probabilistic Forecasting

Generate prediction intervals for optimized safety stock calculations

Risks, Biases & Governance

Model Drift: Consumer preferences change rapidly; models require continuous retraining and monitoring.

Black Swan Events: COVID-like disruptions break historical patterns, requiring rapid manual intervention.

Data Quality Dependencies: Garbage in, garbage out—POS errors, returns delays, or promotional miscoding corrupt forecasts.

Governance Requirements:

Define override protocols when domain experts disagree with models
Establish model performance monitoring dashboards (MAE, RMSE by category)
Set retraining cadence and triggers for significant accuracy degradation
Maintain human judgment for new product launches and discontinuations

Success Metrics

15-25%

Inventory Reduction

20-30%

Forecast Accuracy Improvement

98%+

In-Stock Rate Target

8-12mo

ROI Timeline

Business Success Criteria: Working capital improves by 20% through inventory optimization. Stockouts decrease by 30% despite lower safety stock. Forecast accuracy (MAPE) improves from 35% to 25% within first year of deployment.

Decision Context

You need to make internal knowledge accessible to employees through conversational AI. The choice is between building a custom RAG system or subscribing to an enterprise AI search platform.

Key Trade-off: Control and customization vs. speed to market and ongoing maintenance burden.

Required Data

Volume and diversity of internal documents (PDFs, wikis, databases, Slack)
Security classification levels and access control requirements
Current knowledge retrieval tools and usage patterns
IT team capacity, ML expertise, and infrastructure readiness
Budget constraints for both CapEx (build) and OpEx (SaaS subscription)
Compliance requirements (SOC2, GDPR, HIPAA, industry-specific)

Applicable Models & Techniques

Vector Embeddings

Convert documents to semantic representations for similarity search

Hybrid Search

Combine keyword (BM25) and semantic search for better retrieval

Chunking Strategies

Optimize document segmentation for context windows and relevance

Prompt Engineering

Craft system prompts to minimize hallucinations and cite sources

Risks, Biases & Governance

Data Security: SaaS requires sending proprietary documents to third-party infrastructure; build keeps data in-house but requires robust security implementation.

Vendor Lock-in: SaaS creates dependency on provider's roadmap and pricing; switching costs can be prohibitive.

Technical Debt: Build option requires ongoing maintenance, model updates, and infrastructure management.

Governance Requirements:

Document retention and deletion policies for both source documents and embeddings
Access controls that mirror existing permission structures
Audit trails for queries and retrieved documents
Regular security assessments and penetration testing

Success Metrics

40%+

Query Satisfaction Rate

60%

Reduction in Support Tickets

5hrs/wk

Time Saved per Employee

TCO Analysis

3-Year Cost Comparison

Business Success Criteria: Employees resolve knowledge queries 50% faster. Adoption reaches 70% of target user base within 6 months. Total cost of ownership (including development, infrastructure, and maintenance) is justified by productivity gains and reduced support burden.

Decision Context

You're evaluating whether to move from reactive or scheduled maintenance to predictive maintenance using IoT sensors and ML models. This affects equipment uptime, maintenance costs, and capital investment in sensor infrastructure.

Key Trade-off: Upfront sensor/infrastructure costs vs. reduced downtime and optimized maintenance schedules.

Required Data

Historical failure data: frequency, root causes, time-to-failure distributions
Current maintenance costs (labor, parts, downtime) and schedules
Revenue impact of unplanned downtime by production line/asset
Sensor availability: vibration, temperature, pressure, acoustic data
Asset age, criticality, and replacement timelines
Maintenance team skills and capacity for model-driven workflows

Applicable Models & Techniques

Anomaly Detection

Isolation Forest, Autoencoders to identify unusual sensor patterns

Survival Analysis

Cox models, Weibull distributions for time-to-failure predictions

Deep Learning (RNNs/CNNs)

Capture complex temporal patterns in multivariate sensor data

Digital Twin Simulation

Physics-informed models augmented with ML for comprehensive monitoring

Risks, Biases & Governance

False Positives: Excessive maintenance alerts create "alarm fatigue" and waste resources on unnecessary interventions.

Integration Complexity: Legacy equipment may lack sensor compatibility; retrofitting can be expensive and disruptive.

Skill Gap: Maintenance teams need training to interpret model outputs and integrate into workflows.

Governance Requirements:

Define alert thresholds balancing false positives vs. missed failures
Establish feedback loops to improve models from maintenance outcomes
Create override protocols for experienced technicians
Plan phased rollout starting with most critical assets

Success Metrics

30-50%

Unplanned Downtime Reduction

20-25%

Maintenance Cost Reduction

80%+

Failure Prediction Accuracy

18-24mo

Payback Period

Business Success Criteria: Catastrophic failures decrease by 60% within first year. Maintenance transitions from 60% reactive to 70% predictive. Asset utilization improves by 15% through reduced unplanned downtime. ROI positive within 2 years including sensor infrastructure costs.

Decision Context

You're considering implementing ML-driven dynamic pricing to optimize revenue. This requires balancing revenue growth with customer fairness perceptions, competitive dynamics, and regulatory constraints.

Key Trade-off: Revenue optimization vs. customer trust and regulatory/reputational risk.

Required Data

Historical transaction data: price points, volumes, margins, discounts
Price elasticity estimates by product, customer segment, and channel
Competitive pricing data and market positioning
Customer demographics, purchase history, and loyalty indicators
Inventory levels, perishability, and markdown schedules
Legal and regulatory constraints by jurisdiction

Applicable Models & Techniques

Price Elasticity Modeling

Regression, causal inference to quantify demand sensitivity to price

Reinforcement Learning

Multi-armed bandits or Q-learning for sequential pricing decisions

Customer Segmentation

Clustering to identify willingness-to-pay groups for targeted pricing

A/B Testing

Controlled experiments to validate pricing strategies before scaling

Risks, Biases & Governance

Price Discrimination Concerns: Charging different prices based on protected characteristics (race, gender) is illegal and unethical.

Reputation Damage: Perceived unfair pricing (e.g., surge pricing during crises) can trigger backlash and boycotts.

Competitive Retaliation: Aggressive pricing may trigger price wars that erode industry margins.

Governance Requirements:

Prohibit use of protected attributes (race, gender, zip code proxies) in pricing
Implement price caps and floors to prevent extreme fluctuations
Maintain transparency where legally required (B2B contracts, regulated industries)
Establish ethical review board for pricing strategy alignment with brand values

Success Metrics

3-7%

Revenue Lift

2-4%

Margin Improvement

Maintained

Customer Satisfaction

Zero

Regulatory Violations

Business Success Criteria: Revenue per customer increases 5% without degrading NPS. Price optimization reduces markdown losses by 30%. Competitive positioning maintained or improved. Zero discrimination complaints or regulatory actions. System achieves target ROI within 9 months.

Decision Context

Your current fraud detection uses expert-defined rules. ML promises better detection rates but introduces explainability challenges and requires significant data infrastructure. The decision impacts fraud losses, false positives, and regulatory compliance.

Key Trade-off: Detection accuracy vs. explainability and customer friction from false positives.

Required Data

Historical fraud cases with confirmed outcomes (labels can be delayed months)
Transaction features: amount, merchant, location, device, time, user behavior
Current detection rates: true positives, false positives, false negatives
Cost of fraud losses vs. cost of friction from declined legitimate transactions
Regulatory requirements for explainability and adverse action notices
Attack patterns and adversarial evolution over time

Applicable Models & Techniques

Gradient Boosted Trees

XGBoost/LightGBM for high accuracy with reasonable interpretability

Graph Neural Networks

Detect fraud rings and relationship patterns across entities

Anomaly Detection

Identify unusual patterns for novel fraud types (unsupervised learning)

SHAP/LIME

Explainability frameworks for regulatory compliance and appeals

Risks, Biases & Governance

Adversarial Attacks: Fraudsters adapt to detection patterns; models require continuous retraining and monitoring.

Class Imbalance: Fraud is rare (often <1% of transactions), requiring specialized sampling and evaluation techniques.

Bias and Fairness: Models may disproportionately flag certain demographics, creating disparate impact and discrimination risk.

Governance Requirements:

Implement model explanation capabilities for customer appeals and regulators
Monitor model performance across demographic groups for bias
Establish rapid response protocols for emerging fraud patterns
Maintain human review for high-value or ambiguous cases
Regular adversarial testing and red-team exercises

Success Metrics

30-50%

Fraud Detection Rate Improvement

20%

False Positive Reduction

$MM

Annual Loss Prevention

<100ms

Real-time Scoring Latency

Business Success Criteria: Fraud losses decrease by 40% while false positive rate drops by 25%, improving customer experience. Model achieves 95%+ precision at 70%+ recall. System passes regulatory audits for explainability. ROI positive within 12 months through reduced fraud and operational efficiency.