Executive Program

Enterprise AI
Strategy

For executives and decision-makers. AI governance, use case identification, ROI frameworks, and building AI-first organizations. Navigate the strategic landscape of enterprise AI transformation.

28 Hours
Executive Level
Strategy Focus

Program Overview

Technical excellence means nothing without strategic alignment. This program equips leaders to identify high-value AI opportunities, build governance frameworks, measure ROI, and lead organizational change. No coding required—just strategic thinking.

1

AI Landscape

Understanding LLMs, agents, and automation. What's hype vs. what delivers value today.

LLMs Agents Market Trends
2

Use Case Identification

Finding high-impact, feasible AI projects. Prioritization frameworks and quick wins.

Discovery Prioritization Feasibility
3

ROI Measurement

Quantifying AI value. Cost models, productivity metrics, and business case development.

TCO Value Metrics Business Cases
4

AI Governance

Risk management, compliance, and responsible AI. Building trust with stakeholders.

Risk Compliance Ethics
5

Change Management

Leading AI adoption. Addressing fears, building skills, and creating AI champions.

Adoption Training Culture
6

Vendor & Build Strategy

Build vs. buy decisions. Evaluating vendors, managing partnerships, and internal capabilities.

Make/Buy Vendors Partnerships

Architectural Patterns for AI Governance

Even non-technical leaders benefit from understanding patterns that ensure maintainable, governable AI systems. These patterns translate technical decisions into strategic advantages.

Visitor Pattern

Add new audit, compliance, and analysis operations to AI systems without modifying core code. Essential for evolving regulatory requirements.

Interpreter Pattern

Define governance rules in domain-specific languages. Business users can modify policies without developer involvement.

Chain of Responsibility

Process AI requests through approval chains. Escalate sensitive decisions through appropriate stakeholders automatically.

Proxy Pattern

Control access to AI capabilities. Implement rate limiting, cost controls, and usage tracking at the gateway level.

governance/audit_visitor.py python
from abc import ABC, abstractmethod # Visitor Pattern: Add audit capabilities without modifying AI components class AuditVisitor(ABC): """Visitor that audits AI system components""" @abstractmethod def visit_model(self, model): pass @abstractmethod def visit_dataset(self, dataset): pass @abstractmethod def visit_pipeline(self, pipeline): pass class ComplianceAuditor(AuditVisitor): """Check regulatory compliance across all components""" def __init__(self): self.findings = [] def visit_model(self, model): # Check model documentation, bias metrics if not model.has_model_card(): self.findings.append("Missing model card documentation") if not model.bias_evaluated: self.findings.append("Bias evaluation not completed") def visit_dataset(self, dataset): # Check data lineage, PII handling if not dataset.has_data_lineage(): self.findings.append("Data lineage not documented") if dataset.contains_pii and not dataset.pii_consent_verified: self.findings.append("PII consent not verified") def visit_pipeline(self, pipeline): # Check logging, human-in-the-loop for sensitive ops if not pipeline.has_audit_logging(): self.findings.append("Audit logging not enabled") # AI components accept visitors class AIModel: def accept(self, visitor: AuditVisitor): visitor.visit_model(self) # Usage: Run compliance audit across entire AI estate auditor = ComplianceAuditor() for component in ai_inventory: component.accept(auditor) print(f"Compliance findings: {auditor.findings}")

AI ROI Framework

Value Calculation Components

Time Saved

Hours × Hourly Rate × Frequency

Quality Gains

Error Reduction × Cost per Error

Throughput

Volume Increase × Margin per Unit

Risk Reduction

Risk Probability × Impact Cost

Cost Category One-Time Recurring Notes
API Costs - $0.01-0.10/call Scales with usage; budget for growth
Infrastructure $0-50K $500-5K/mo Cloud GPUs, vector DBs, compute
Development $50-500K $10-50K/mo Build vs. vendor significantly impacts this
Training $10-50K $2-10K/mo User enablement, ongoing education
Governance $20-100K $5-20K/mo Compliance, auditing, risk management

AI Governance Framework

Governance Principles

Effective AI governance balances innovation speed with risk management. The goal isn't to prevent AI adoption—it's to enable responsible, scalable deployment that builds trust with customers, employees, and regulators.

Risk Classification

Categorize AI use cases by risk level. High-risk applications (hiring, credit, healthcare) require additional controls.

Human Oversight

Define when human review is required. Create escalation paths for edge cases and high-stakes decisions.

Model Registry

Maintain inventory of all AI models. Track versions, performance, and usage across the organization.

Incident Response

Establish procedures for AI failures. Define rollback capabilities and communication protocols.

governance/policy_interpreter.py python
# Interpreter Pattern: Business-readable governance rules class PolicyRule(ABC): @abstractmethod def evaluate(self, context: dict) -> bool: pass class RequiresApproval(PolicyRule): def __init__(self, threshold: float, approver: str): self.threshold = threshold self.approver = approver def evaluate(self, context: dict) -> bool: return context.get("cost", 0) > self.threshold class AndRule(PolicyRule): def __init__(self, *rules: PolicyRule): self.rules = rules def evaluate(self, context: dict) -> bool: return all(rule.evaluate(context) for rule in self.rules) # Define policies in business terms policies = { "external_facing": AndRule( RequiresApproval(threshold=0, approver="legal"), RequiresApproval(threshold=1000, approver="finance") ), "internal_tool": RequiresApproval(threshold=5000, approver="manager") } # Evaluate request against policy def check_approval(request_type: str, context: dict) -> str | None: policy = policies.get(request_type) if policy and policy.evaluate(context): return policy.approver return None

Pattern Quick Reference

When facing these strategic challenges, consider the corresponding design pattern:

Strategic Challenge Pattern Benefit
Evolving compliance requirements Visitor Add audits without code changes
Business-controlled policies Interpreter Non-developers can modify rules
Multi-level approvals Chain of Responsibility Flexible escalation paths
Cost and access control Proxy Centralized governance layer
Swap AI providers Strategy Avoid vendor lock-in
Track state changes Memento Audit trails and rollback

Strategic Exercises

Ready to Lead AI Transformation?

Strategic AI leadership requires both vision and practical frameworks. This program gives you the tools to drive meaningful AI adoption in your organization.

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