interface AIAdoptionEvolution {
  phase_1_experimentation: {
    timeframe: "2021-2023";
    characteristics: [
      "Pilot projects and proof of concepts",
      "Technology-focused evaluation",
      "Limited budget accountability",
      "Success measured by technical feasibility"
    ];
    investment_approach: "Experimental budgets and innovation funds";
    success_metrics: "Technical capabilities and user adoption";
  };
  
  phase_2_skeptical_evaluation: {
    timeframe: "2024-2025";
    characteristics: [
      "Demand for business case justification",
      "Scrutiny of AI spending and results",
      "Focus on practical applications",
      "Emphasis on measurable outcomes"
    ];
    investment_approach: "Business case-driven funding decisions";
    success_metrics: "ROI, cost savings, productivity improvements";
  };
  
  phase_3_strategic_optimization: {
    timeframe: "2025+";
    characteristics: [
      "AI as core business capability",
      "Systematic ROI optimization",
      "Portfolio approach to AI investments",
      "Competitive advantage through AI"
    ];
    investment_approach: "Strategic budget allocation with clear ROI targets";
    success_metrics: "Business impact, competitive advantage, market share";
  };
}

class AIBudgetReality:
    def __init__(self):
        self.current_pressures = {
            'economic_uncertainty': {
                'description': 'Economic headwinds driving cost consciousness',
                'impact_on_ai': 'Increased scrutiny of AI spending and ROI',
                'response_required': 'Clear value demonstration and optimization'
            },
            
            'budget_competition': {
                'description': 'AI budgets competing with other priorities',
                'impact_on_ai': 'Need to prove superior ROI vs alternatives',
                'response_required': 'Comparative value analysis and optimization'
            },
            
            'stakeholder_skepticism': {
                'description': 'Growing skepticism about AI hype vs reality',
                'impact_on_ai': 'Demand for concrete evidence of value',
                'response_required': 'Data-driven proof of business impact'
            }
        }
    
    def analyze_spending_trends(self):
        """Analyze AI spending trends and budget allocation patterns"""
        
        return {
            'total_ai_spending': {
                '2023': '$150B globally',
                '2024': '$200B globally',
                '2025_projected': '$250B globally'
            },
            
            'roi_expectations': {
                'year_1_roi_targets': '150-300% for pilot projects',
                'year_2_roi_targets': '200-500% for scaled implementations',
                'long_term_roi_targets': '300-1000% for strategic AI capabilities'
            },
            
            'budget_allocation_shifts': {
                'from_experimentation': 'Decreasing budget for pure R&D',
                'to_production_deployment': 'Increasing budget for scaled solutions',
                'measurement_infrastructure': 'New budget category for ROI tracking'
            },
            
            'funding_decision_factors': [
                'Clear business case with quantified benefits',
                'Proven track record with previous AI initiatives',
                'Competitive advantage and market differentiation',
                'Risk mitigation and operational efficiency'
            ]
        }

interface ComprehensiveAIROI {
  direct_financial_impact: {
    cost_reduction: {
      operational_savings: "Reduced manual labor and operational costs";
      efficiency_gains: "Faster processes and reduced cycle times";
      error_reduction: "Fewer mistakes and rework costs";
      resource_optimization: "Better utilization of existing resources";
    };
    
    revenue_generation: {
      new_products_services: "AI-enabled offerings and capabilities";
      market_expansion: "Access to new markets and customers";
      pricing_optimization: "Better pricing strategies and margins";
      customer_lifetime_value: "Improved retention and expansion";
    };
    
    risk_mitigation: {
      compliance_risk: "Reduced regulatory and compliance risks";
      security_risk: "Better threat detection and prevention";
      operational_risk: "Reduced downtime and failures";
      competitive_risk: "Maintaining market position";
    };
  };
  
  productivity_improvements: {
    developer_productivity: {
      code_generation_speed: "Faster development cycles";
      bug_detection_accuracy: "Earlier and more accurate issue identification";
      documentation_automation: "Automated documentation generation";
      testing_efficiency: "Improved test coverage and automation";
    };
    
    business_process_efficiency: {
      decision_making_speed: "Faster and more informed decisions";
      data_analysis_automation: "Automated insights and reporting";
      customer_service_efficiency: "Improved response times and quality";
      workflow_optimization: "Streamlined business processes";
    };
  };
  
  strategic_value: {
    competitive_advantage: {
      market_differentiation: "Unique capabilities and offerings";
      innovation_velocity: "Faster innovation and time-to-market";
      customer_experience: "Superior customer interactions";
      operational_excellence: "Best-in-class operations";
    };
    
    future_readiness: {
      technology_advancement: "Cutting-edge capabilities";
      talent_attraction: "Attracting top talent";
      ecosystem_positioning: "Strategic partnerships and alliances";
      scalability_foundation: "Infrastructure for future growth";
    };
  };
}

class AIROIMeasurementSystem:
    def __init__(self):
        self.measurement_framework = self.build_measurement_framework()
        self.data_collection = self.setup_data_collection()
        self.analysis_engine = self.create_analysis_engine()
        
    def build_measurement_framework(self):
        """Comprehensive framework for AI ROI measurement"""
        
        return {
            'baseline_establishment': {
                'pre_ai_metrics': 'Measure performance before AI implementation',
                'control_groups': 'Maintain non-AI control groups for comparison',
                'historical_baselines': 'Use historical data for trend analysis',
                'industry_benchmarks': 'Compare against industry standards'
            },
            
            'metric_categories': {
                'efficiency_metrics': [
                    'Time reduction in key processes',
                    'Cost per unit of output',
                    'Resource utilization rates',
                    'Error rates and quality improvements'
                ],
                
                'effectiveness_metrics': [
                    'Accuracy improvements',
                    'Customer satisfaction scores',
                    'Decision quality metrics',
                    'Outcome achievement rates'
                ],
                
                'innovation_metrics': [
                    'Time to market for new features',
                    'Innovation pipeline velocity',
                    'Competitive advantage measures',
                    'Market share and positioning'
                ],
                
                'financial_metrics': [
                    'Direct cost savings',
                    'Revenue attribution',
                    'Profit margin improvements',
                    'Total cost of ownership'
                ]
            },
            
            'measurement_intervals': {
                'real_time': 'Operational metrics and system performance',
                'daily': 'Productivity and efficiency measures',
                'weekly': 'Quality and customer satisfaction metrics',
                'monthly': 'Financial and business impact measures',
                'quarterly': 'Strategic value and competitive position'
            }
        }
    
    def calculate_comprehensive_roi(self, ai_initiative):
        """Calculate comprehensive ROI including all value dimensions"""
        
        financial_benefits = self.calculate_financial_benefits(ai_initiative)
        productivity_gains = self.calculate_productivity_gains(ai_initiative)
        strategic_value = self.calculate_strategic_value(ai_initiative)
        
        total_investment = self.calculate_total_investment(ai_initiative)
        
        return {
            'financial_roi': (financial_benefits - total_investment) / total_investment,
            'productivity_roi': self.convert_productivity_to_financial(productivity_gains) / total_investment,
            'strategic_roi': self.estimate_strategic_value_financial(strategic_value) / total_investment,
            
            'comprehensive_roi': {
                'total_benefits': financial_benefits + self.convert_productivity_to_financial(productivity_gains) + self.estimate_strategic_value_financial(strategic_value),
                'total_investment': total_investment,
                'net_benefit': 'total_benefits - total_investment',
                'roi_percentage': '(total_benefits - total_investment) / total_investment * 100'
            },
            
            'payback_analysis': {
                'simple_payback_period': total_investment / (financial_benefits / 12),  # months
                'discounted_payback_period': self.calculate_discounted_payback(ai_initiative),
                'break_even_point': self.calculate_break_even_point(ai_initiative)
            }
        }

class CodeGenerationROI:
    def __init__(self):
        self.organization = {
            'type': 'Fortune 500 Technology Company',
            'team_size': 500,
            'annual_development_budget': 50000000,
            'ai_implementation': 'GitHub Copilot + Custom AI Tools'
        }
    
    def calculate_development_productivity_roi(self):
        """Detailed ROI calculation for AI-powered development"""
        
        baseline_metrics = {
            'average_developer_salary': 120000,
            'lines_of_code_per_developer_day': 50,
            'bug_rate_per_1000_lines': 15,
            'code_review_time_hours_per_week': 8,
            'documentation_time_hours_per_week': 6
        }
        
        ai_improved_metrics = {
            'lines_of_code_per_developer_day': 75,  # 50% improvement
            'bug_rate_per_1000_lines': 9,           # 40% reduction
            'code_review_time_hours_per_week': 5,   # 37.5% reduction
            'documentation_time_hours_per_week': 2  # 66.7% reduction
        }
        
        annual_benefits = {
            'productivity_gain': {
                'additional_code_output': (75 - 50) * 250 * 500,  # 25 lines/day * 250 days * 500 devs
                'value_per_line': 10,  # Estimated value per line of code
                'total_productivity_value': 31250000  # $31.25M
            },
            
            'quality_improvement': {
                'bugs_prevented': ((15 - 9) / 1000) * (75 * 250 * 500),  # 6 fewer bugs per 1000 lines
                'cost_per_bug_fix': 500,  # Average cost to fix a bug
                'total_quality_value': 2812500  # $2.81M
            },
            
            'time_savings': {
                'code_review_hours_saved': (8 - 5) * 50 * 500,  # 3 hours/week * 50 weeks * 500 devs
                'documentation_hours_saved': (6 - 2) * 50 * 500,  # 4 hours/week * 50 weeks * 500 devs
                'hourly_rate': 60,  # $60/hour loaded rate
                'total_time_value': 21000000  # $21M
            }
        }
        
        total_annual_benefits = 55062500  # $55.06M
        
        ai_investment = {
            'tool_licenses': 500 * 20 * 12,     # $20/month per developer
            'implementation_costs': 500000,      # Initial setup and training
            'ongoing_support': 200000,          # Annual support and maintenance
            'training_time': 500 * 8 * 60,      # 8 hours training per dev at $60/hour
            'total_annual_investment': 1140000   # $1.14M
        }
        
        return {
            'annual_roi': (total_annual_benefits - ai_investment['total_annual_investment']) / ai_investment['total_annual_investment'] * 100,
            'roi_percentage': 4730,  # 4,730% ROI
            'payback_period_months': 0.25,  # 3 weeks
            'net_annual_benefit': 53922500,  # $53.92M
            
            'benefit_breakdown': {
                'productivity_gains': '56.7% of total benefits',
                'quality_improvements': '5.1% of total benefits',
                'time_savings': '38.2% of total benefits'
            }
        }

interface CustomerServiceAIROI {
  organization: {
    type: "Large E-commerce Company";
    monthly_support_tickets: 50000;
    average_ticket_cost: 25;
    customer_satisfaction_baseline: 72;
  };
  
  ai_implementation: {
    solution: "AI chatbot + agent assistance + sentiment analysis";
    implementation_cost: 850000;
    annual_operational_cost: 300000;
  };
  
  impact_metrics: {
    ticket_volume_reduction: {
      automated_resolution_rate: 65;  // 65% of tickets resolved by AI
      tickets_deflected_monthly: 32500;
      cost_savings_monthly: 812500;
      annual_cost_savings: 9750000;
    };
    
    agent_productivity: {
      resolution_time_improvement: 40;  // 40% faster resolution
      tickets_per_agent_increase: 67;   // 67% more tickets per agent
      agent_efficiency_value: 2100000;  // Annual value from efficiency
    };
    
    customer_satisfaction: {
      satisfaction_score_improvement: 23;  // From 72% to 88%
      customer_retention_improvement: 8;   // 8% better retention
      retention_value: 5200000;           // Annual value from retention
    };
    
    quality_improvements: {
      response_consistency: 95;     // 95% consistent responses
      24_7_availability: true;     // Round-the-clock support
      multilingual_support: true;  // Instant language support
      quality_value: 1800000;      // Annual value from quality
    };
  };
  
  roi_calculation: {
    total_annual_benefits: 18850000;  // $18.85M
    total_annual_costs: 1150000;     // $1.15M (including amortized implementation)
    net_annual_benefit: 17700000;    // $17.7M
    roi_percentage: 1539;            // 1,539% ROI
    payback_period_months: 0.7;      // Less than 1 month
  };
}

class FinancialServicesAIROI:
    def __init__(self):
        self.bank_profile = {
            'type': 'Regional Bank',
            'annual_loan_volume': 2000000000,  # $2B
            'average_loan_amount': 50000,
            'current_default_rate': 0.035,     # 3.5%
            'processing_cost_per_application': 150
        }
    
    def calculate_credit_ai_roi(self):
        """Calculate ROI for AI-powered credit risk assessment"""
        
        baseline_performance = {
            'loan_applications_per_year': 40000,
            'approval_rate': 0.70,
            'default_rate': 0.035,
            'processing_time_days': 7,
            'manual_review_cost': 150
        }
        
        ai_enhanced_performance = {
            'loan_applications_per_year': 40000,
            'approval_rate': 0.75,           # 5% increase in approvals
            'default_rate': 0.023,          # 34% reduction in defaults
            'processing_time_days': 2,       # 71% faster processing
            'automated_review_percentage': 0.80  # 80% automated processing
        }
        
        financial_impact = {
            'reduced_defaults': {
                'baseline_defaults': 40000 * 0.70 * 0.035 * 50000,
                'ai_defaults': 40000 * 0.75 * 0.023 * 50000,
                'default_reduction_value': 25750000  # $25.75M saved
            },
            
            'increased_approvals': {
                'additional_approvals': 40000 * (0.75 - 0.70),
                'revenue_per_approval': 2500,  # Net interest and fees
                'additional_revenue': 5000000  # $5M additional revenue
            },
            
            'operational_efficiency': {
                'automated_applications': 40000 * 0.80,
                'cost_savings_per_application': 120,  # Reduced manual work
                'annual_operational_savings': 3840000  # $3.84M
            },
            
            'faster_processing': {
                'competitive_advantage': 'Improved customer experience',
                'market_share_gain': 0.05,  # 5% market share increase
                'market_share_value': 10000000  # $10M value
            }
        }
        
        total_annual_benefits = 44590000  # $44.59M
        
        ai_investment = {
            'initial_development': 2000000,   # $2M for custom model
            'data_infrastructure': 500000,   # Data platform upgrade
            'annual_operational': 800000,    # Ongoing costs
            'training_and_change': 300000,   # Staff training
            'compliance_validation': 200000, # Regulatory compliance
            'total_first_year': 3800000      # $3.8M
        }
        
        return {
            'first_year_roi': (total_annual_benefits - ai_investment['total_first_year']) / ai_investment['total_first_year'] * 100,
            'roi_percentage': 1073,  # 1,073% ROI
            'payback_period_months': 1.0,  # 1 month
            'net_annual_benefit': 40790000,  # $40.79M
            
            'risk_mitigation_value': {
                'regulatory_compliance': 'Improved audit trail and decision transparency',
                'competitive_positioning': 'Faster decisions attract customers',
                'operational_resilience': 'Reduced dependency on manual processes'
            }
        }

class AIAttributionAnalysis {
  private controlGroups: ControlGroup[];
  private confoundingVariables: Variable[];
  private statisticalMethods: StatisticalMethod[];
  
  analyzeAIAttribution(aiInitiative: AIInitiative): AttributionAnalysis {
    return {
      experimental_design: {
        randomized_controlled_trial: {
          description: "Random assignment to AI vs control groups";
          validity: "High - strongest causal inference";
          applicability: "Best for pilot programs and A/B tests";
          example: "50% of customer service agents use AI, 50% use traditional methods";
        };
        
        quasi_experimental: {
          description: "Natural experiments with AI rollout timing";
          validity: "Medium - good causal inference with controls";
          applicability: "Gradual rollouts and phased implementations";
          example: "Teams get AI tools in different months, compare performance";
        };
        
        difference_in_differences: {
          description: "Compare changes before/after AI vs control group";
          validity: "Medium - controls for time trends";
          applicability: "When randomization isn't possible";
          example: "Compare productivity changes in AI vs non-AI departments";
        };
      };
      
      confounding_variable_control: {
        temporal_factors: {
          controls: ["Seasonal effects", "Market conditions", "Economic cycles"];
          methods: ["Time series analysis", "Seasonal decomposition"];
        };
        
        organizational_factors: {
          controls: ["Team changes", "Process improvements", "Technology upgrades"];
          methods: ["Regression analysis", "Propensity score matching"];
        };
        
        external_factors: {
          controls: ["Industry trends", "Competitive actions", "Regulatory changes"];
          methods: ["External benchmarking", "Industry controls"];
        };
      };
      
      statistical_validation: {
        significance_testing: "Ensure results are statistically significant";
        confidence_intervals: "Provide ranges for estimated benefits";
        sensitivity_analysis: "Test robustness of results to assumptions";
        bootstrap_validation: "Validate results through resampling";
      };
    };
  }
  
  calculateAttributableImpact(
    observedImpact: number,
    confoundingFactors: ConfoundingFactor[]
  ): AttributableImpact {
    
    // Advanced statistical modeling to isolate AI impact
    const baselineModel = this.buildBaselineModel(confoundingFactors);
    const aiModel = this.buildAIModel(confoundingFactors);
    
    return {
      total_observed_impact: observedImpact,
      baseline_expected_impact: baselineModel.predictedImpact,
      confounding_adjustments: this.calculateConfoundingAdjustments(confoundingFactors),
      ai_attributable_impact: this.isolateAIImpact(observedImpact, baselineModel, confoundingFactors),
      
      confidence_metrics: {
        statistical_significance: this.calculateSignificance(),
        confidence_interval: this.calculateConfidenceInterval(),
        r_squared: this.calculateRSquared(),
        p_value: this.calculatePValue()
      }
    };
  }
}

class DynamicROITracker:
    def __init__(self):
        self.real_time_metrics = self.setup_real_time_tracking()
        self.optimization_engine = self.create_optimization_engine()
        
    def setup_real_time_tracking(self):
        """Implement real-time ROI tracking system"""
        
        return {
            'data_collection': {
                'automated_metrics': [
                    'System performance and usage',
                    'User productivity measures',
                    'Business process efficiency',
                    'Financial impact indicators'
                ],
                
                'human_reported_metrics': [
                    'Qualitative impact assessments',
                    'User satisfaction surveys',
                    'Business outcome reports',
                    'Strategic value evaluations'
                ],
                
                'external_data_sources': [
                    'Market performance data',
                    'Competitor benchmarks',
                    'Industry trend indicators',
                    'Economic condition metrics'
                ]
            },
            
            'processing_pipeline': {
                'data_validation': 'Ensure data quality and consistency',
                'normalization': 'Standardize metrics across sources',
                'aggregation': 'Combine data at appropriate levels',
                'analysis': 'Calculate ROI components in real-time'
            },
            
            'alerting_system': {
                'performance_degradation': 'Alert when ROI drops below thresholds',
                'optimization_opportunities': 'Identify areas for improvement',
                'anomaly_detection': 'Flag unusual patterns requiring investigation',
                'trend_analysis': 'Predict future ROI trajectory'
            }
        }
    
    def calculate_dynamic_roi(self, time_period='real_time'):
        """Calculate ROI with dynamic adjustments"""
        
        current_benefits = self.measure_current_benefits()
        current_costs = self.measure_current_costs()
        trend_adjustments = self.calculate_trend_adjustments()
        
        return {
            'instantaneous_roi': (current_benefits - current_costs) / current_costs,
            
            'trending_roi': {
                'short_term_trend': self.calculate_short_term_trend(),
                'medium_term_projection': self.project_medium_term_roi(),
                'long_term_forecast': self.forecast_long_term_roi()
            },
            
            'optimization_recommendations': {
                'cost_optimization': self.identify_cost_optimizations(),
                'benefit_enhancement': self.identify_benefit_enhancements(),
                'process_improvements': self.suggest_process_improvements(),
                'technology_upgrades': self.recommend_technology_upgrades()
            },
            
            'risk_factors': {
                'downside_risks': self.assess_downside_risks(),
                'mitigation_strategies': self.recommend_mitigations(),
                'contingency_plans': self.develop_contingency_plans()
            }
        }
    
    def optimize_roi_continuously(self):
        """Implement continuous ROI optimization"""
        
        optimization_cycle = {
            'monitor': {
                'frequency': 'Real-time data collection',
                'metrics': 'All key performance indicators',
                'alerts': 'Automated anomaly detection'
            },
            
            'analyze': {
                'frequency': 'Daily analysis cycles',
                'methods': 'Statistical analysis and ML models',
                'outputs': 'Insights and recommendations'
            },
            
            'optimize': {
                'frequency': 'Weekly optimization reviews',
                'scope': 'Process and parameter adjustments',
                'validation': 'A/B testing of optimizations'
            },
            
            'improve': {
                'frequency': 'Monthly strategic reviews',
                'scope': 'Fundamental improvements and upgrades',
                'planning': 'Long-term optimization roadmap'
            }
        }
        
        return optimization_cycle

interface StakeholderBusinessCases {
  executive_leadership: {
    primary_concerns: ["Strategic advantage", "Market position", "Shareholder value"];
    key_metrics: ["Revenue growth", "Market share", "Competitive differentiation"];
    
    value_proposition: {
      strategic_advantage: {
        message: "AI provides sustainable competitive advantages";
        evidence: [
          "30% faster time-to-market for new products",
          "25% improvement in customer satisfaction",
          "40% reduction in operational costs"
        ];
        risk_mitigation: "Staying competitive in AI-driven markets";
      };
      
      financial_returns: {
        message: "AI delivers exceptional financial returns";
        evidence: [
          "300-1000% ROI within 24 months",
          "$50M+ annual cost savings potential",
          "15-25% revenue growth from AI-enabled capabilities"
        ];
        benchmarking: "Industry leaders report similar or higher returns";
      };
    };
  };
  
  financial_leadership: {
    primary_concerns: ["Cost control", "ROI optimization", "Risk management"];
    key_metrics: ["Cost reduction", "ROI percentage", "Payback period"];
    
    value_proposition: {
      cost_optimization: {
        message: "AI dramatically reduces operational costs";
        evidence: [
          "45% reduction in manual processing costs",
          "60% decrease in error-related expenses",
          "35% improvement in resource utilization"
        ];
        financial_modeling: "Detailed NPV and IRR calculations";
      };
      
      risk_mitigation: {
        message: "AI reduces financial and operational risks";
        evidence: [
          "70% reduction in compliance violations",
          "50% decrease in security incidents",
          "40% improvement in forecast accuracy"
        ];
        contingency_planning: "Clear risk mitigation strategies";
      };
    };
  };
  
  operations_leadership: {
    primary_concerns: ["Efficiency", "Quality", "Scalability"];
    key_metrics: ["Process efficiency", "Quality improvements", "Scalability metrics"];
    
    value_proposition: {
      operational_excellence: {
        message: "AI enables world-class operational performance";
        evidence: [
          "50% reduction in processing time",
          "90% improvement in accuracy",
          "Unlimited scalability without proportional cost increase"
        ];
        implementation_roadmap: "Clear path to operational transformation";
      };
    };
  };
}

class RiskAdjustedBusinessCase:
    def __init__(self):
        self.risk_assessment = self.assess_ai_investment_risks()
        self.scenario_modeling = self.create_scenario_models()
        
    def assess_ai_investment_risks(self):
        """Comprehensive risk assessment for AI investments"""
        
        return {
            'technology_risks': {
                'performance_risk': {
                    'description': 'AI may not perform as expected',
                    'probability': 0.25,
                    'impact': 'High - could reduce benefits by 50%',
                    'mitigation': 'Thorough testing and phased rollout'
                },
                
                'integration_risk': {
                    'description': 'Difficulty integrating with existing systems',
                    'probability': 0.30,
                    'impact': 'Medium - could delay benefits by 6 months',
                    'mitigation': 'Comprehensive integration planning'
                },
                
                'obsolescence_risk': {
                    'description': 'Technology becomes outdated quickly',
                    'probability': 0.20,
                    'impact': 'Medium - may require additional investment',
                    'mitigation': 'Choose flexible, upgradeable solutions'
                }
            },
            
            'organizational_risks': {
                'adoption_risk': {
                    'description': 'Users may resist or poorly adopt AI',
                    'probability': 0.40,
                    'impact': 'High - could reduce benefits by 70%',
                    'mitigation': 'Comprehensive change management'
                },
                
                'capability_risk': {
                    'description': 'Lack of internal AI expertise',
                    'probability': 0.35,
                    'impact': 'Medium - could increase costs by 30%',
                    'mitigation': 'Training and external partnerships'
                }
            },
            
            'market_risks': {
                'competitive_response': {
                    'description': 'Competitors may neutralize AI advantages',
                    'probability': 0.60,
                    'impact': 'Medium - may reduce competitive benefits',
                    'mitigation': 'Continuous innovation and improvement'
                },
                
                'regulatory_changes': {
                    'description': 'New regulations may impact AI use',
                    'probability': 0.30,
                    'impact': 'Variable - depends on regulatory scope',
                    'mitigation': 'Compliance-first design and monitoring'
                }
            }
        }
    
    def create_scenario_models(self):
        """Create multiple scenario models for business case"""
        
        return {
            'optimistic_scenario': {
                'probability': 0.25,
                'assumptions': [
                    'AI performs better than expected',
                    'Rapid user adoption',
                    'Minimal integration issues',
                    'Favorable market conditions'
                ],
                'financial_outcomes': {
                    'roi': '500-1000%',
                    'payback_period': '6-9 months',
                    'net_benefit': '150-200% of base case'
                }
            },
            
            'base_case_scenario': {
                'probability': 0.50,
                'assumptions': [
                    'AI performs as planned',
                    'Expected adoption rates',
                    'Manageable integration challenges',
                    'Stable market conditions'
                ],
                'financial_outcomes': {
                    'roi': '300-500%',
                    'payback_period': '12-18 months',
                    'net_benefit': '100% of base case'
                }
            },
            
            'conservative_scenario': {
                'probability': 0.20,
                'assumptions': [
                    'AI underperforms initial expectations',
                    'Slower adoption than planned',
                    'Significant integration challenges',
                    'Competitive pressure'
                ],
                'financial_outcomes': {
                    'roi': '150-300%',
                    'payback_period': '24-36 months',
                    'net_benefit': '50-75% of base case'
                }
            },
            
            'pessimistic_scenario': {
                'probability': 0.05,
                'assumptions': [
                    'AI fails to deliver value',
                    'Poor user adoption',
                    'Major technical difficulties',
                    'Adverse market conditions'
                ],
                'financial_outcomes': {
                    'roi': '0-50%',
                    'payback_period': '>36 months',
                    'net_benefit': '0-25% of base case'
                }
            }
        }
    
    def calculate_risk_adjusted_roi(self, scenarios):
        """Calculate expected ROI considering all scenarios"""
        
        weighted_roi = 0
        for scenario_name, scenario in scenarios.items():
            scenario_roi = scenario['financial_outcomes']['roi_numeric']
            scenario_probability = scenario['probability']
            weighted_roi += scenario_roi * scenario_probability
        
        return {
            'expected_roi': weighted_roi,
            'risk_adjusted_npv': self.calculate_risk_adjusted_npv(scenarios),
            'value_at_risk': self.calculate_value_at_risk(scenarios),
            'confidence_intervals': self.calculate_confidence_intervals(scenarios)
        }

class ROIMeasurementProgram {
  setupProgram(): MeasurementProgram {
    return {
      phase_1_foundation: {
        duration: "4-6 weeks";
        activities: [
          "Define measurement framework and metrics",
          "Establish baseline measurements",
          "Set up data collection infrastructure",
          "Train team on measurement methods"
        ];
        deliverables: [
          "ROI measurement framework document",
          "Baseline performance report",
          "Data collection systems",
          "Team training completion"
        ];
      };
      
      phase_2_implementation: {
        duration: "8-12 weeks";
        activities: [
          "Deploy measurement systems",
          "Begin regular data collection",
          "Implement analysis and reporting",
          "Establish governance processes"
        ];
        deliverables: [
          "Live measurement dashboard",
          "Regular reporting cadence",
          "Analysis and insights process",
          "Governance framework"
        ];
      };
      
      phase_3_optimization: {
        duration: "Ongoing";
        activities: [
          "Continuous measurement refinement",
          "Advanced analytics implementation",
          "ROI optimization initiatives",
          "Stakeholder communication"
        ];
        deliverables: [
          "Optimized measurement system",
          "Advanced analytics capabilities",
          "ROI improvement initiatives",
          "Executive reporting"
        ];
      };
    };
  }
  
  createGovernanceFramework(): GovernanceFramework {
    return {
      roles_and_responsibilities: {
        roi_program_manager: {
          responsibilities: [
            "Overall program leadership and strategy",
            "Stakeholder communication and reporting",
            "Measurement framework evolution",
            "Cross-functional coordination"
          ];
        };
        
        data_analysts: {
          responsibilities: [
            "Data collection and validation",
            "Analysis and insight generation",
            "Reporting and visualization",
            "Technical measurement support"
          ];
        };
        
        business_stakeholders: {
          responsibilities: [
            "Requirements definition and validation",
            "Business context and interpretation",
            "Action planning based on insights",
            "Success criteria definition"
          ];
        };
      };
      
      processes_and_procedures: {
        measurement_cadence: {
          daily: "Operational metrics collection",
          weekly: "Performance analysis and reporting",
          monthly: "Business impact assessment",
          quarterly: "Strategic review and optimization"
        };
        
        quality_assurance: {
          data_validation: "Automated and manual data quality checks",
          methodology_review: "Regular review of measurement methods",
          external_validation: "Third-party validation of key metrics",
          continuous_improvement: "Ongoing refinement of processes"
        };
      };
    };
  }
}

class ROICommunicationStrategy:
    def __init__(self):
        self.audience_segments = self.define_audience_segments()
        self.messaging_framework = self.create_messaging_framework()
        
    def define_audience_segments(self):
        """Define different audience segments for ROI communication"""
        
        return {
            'board_and_investors': {
                'interests': ['Strategic value', 'Market position', 'Financial returns'],
                'communication_style': 'High-level, strategic, quantitative',
                'frequency': 'Quarterly',
                'format': 'Executive summary with key metrics'
            },
            
            'executive_leadership': {
                'interests': ['Business impact', 'Competitive advantage', 'Risk mitigation'],
                'communication_style': 'Strategic with operational details',
                'frequency': 'Monthly',
                'format': 'Dashboard with narrative insights'
            },
            
            'functional_leaders': {
                'interests': ['Operational impact', 'Team performance', 'Process improvement'],
                'communication_style': 'Detailed, actionable, specific',
                'frequency': 'Weekly',
                'format': 'Detailed reports with recommendations'
            },
            
            'project_teams': {
                'interests': ['Technical performance', 'User adoption', 'Implementation success'],
                'communication_style': 'Technical, detailed, frequent',
                'frequency': 'Daily/Weekly',
                'format': 'Operational dashboards and alerts'
            }
        }
    
    def create_roi_storytelling_framework(self):
        """Create compelling narratives around ROI data"""
        
        return {
            'success_story_template': {
                'challenge': 'Clearly define the business problem being solved',
                'solution': 'Describe the AI solution and implementation',
                'results': 'Quantify the specific outcomes and benefits',
                'impact': 'Connect results to broader business objectives',
                'future': 'Outline next steps and expansion opportunities'
            },
            
            'data_visualization_principles': {
                'clarity': 'Make complex data easy to understand',
                'relevance': 'Focus on metrics that matter to the audience',
                'context': 'Provide benchmarks and comparisons',
                'trends': 'Show progress over time',
                'actionability': 'Highlight areas for action and improvement'
            },
            
            'communication_channels': {
                'executive_dashboards': 'Real-time ROI metrics for leadership',
                'regular_reports': 'Scheduled analysis and insights',
                'success_showcases': 'Detailed case studies and stories',
                'team_meetings': 'Regular discussion of results and actions',
                'external_communication': 'Industry presentations and thought leadership'
            }
        }

interface PredictiveROIAnalytics {
  advanced_modeling: {
    machine_learning_prediction: {
      description: "ML models that predict ROI outcomes";
      capabilities: [
        "Real-time ROI forecasting",
        "Scenario-based outcome prediction",
        "Risk-adjusted return estimates",
        "Optimization recommendation engine"
      ];
      data_sources: [
        "Historical ROI performance data",
        "External market and economic indicators",
        "Organizational performance metrics",
        "Technology adoption patterns"
      ];
    };
    
    simulation_modeling: {
      description: "Monte Carlo simulations for ROI uncertainty";
      capabilities: [
        "Probability distributions for ROI outcomes",
        "Sensitivity analysis for key variables",
        "Risk assessment and mitigation planning",
        "Portfolio optimization across AI investments"
      ];
    };
  };
  
  autonomous_optimization: {
    self_optimizing_systems: {
      description: "AI systems that automatically optimize their own ROI";
      capabilities: [
        "Automatic parameter tuning for performance",
        "Dynamic resource allocation optimization",
        "Self-monitoring and correction mechanisms",
        "Continuous learning from performance data"
      ];
    };
    
    intelligent_alerting: {
      description: "Proactive identification of ROI opportunities and risks";
      capabilities: [
        "Early warning systems for ROI degradation",
        "Opportunity identification for ROI enhancement",
        "Automated recommendation generation",
        "Predictive maintenance for AI systems"
      ];
    };
  };
}

class AIROIStandardization:
    def __init__(self):
        self.emerging_standards = self.analyze_emerging_standards()
        
    def analyze_emerging_standards(self):
        return {
            'industry_frameworks': {
                'iso_ai_standards': {
                    'description': 'International standards for AI system evaluation',
                    'scope': 'Quality, performance, and value measurement',
                    'timeline': '2025-2027 development and adoption'
                },
                
                'accounting_standards': {
                    'description': 'Financial accounting standards for AI investments',
                    'scope': 'Asset valuation, depreciation, and ROI calculation',
                    'timeline': '2026-2028 development and implementation'
                },
                
                'industry_benchmarks': {
                    'description': 'Standardized benchmarks for AI ROI comparison',
                    'scope': 'Cross-industry and domain-specific benchmarks',
                    'timeline': '2025-2026 initial release'
                }
            },
            
            'measurement_standardization': {
                'common_metrics': 'Standardized definitions for AI ROI metrics',
                'calculation_methods': 'Consistent approaches to ROI calculation',
                'reporting_formats': 'Standard templates for ROI reporting',
                'audit_procedures': 'Standardized audit methods for AI ROI'
            },
            
            'benefits': {
                'comparability': 'Enable comparison across organizations and industries',
                'credibility': 'Increase trust in AI ROI claims and measurements',
                'efficiency': 'Reduce effort required for ROI measurement',
                'transparency': 'Improve stakeholder understanding and confidence'
            }
        }