Digital Transformation Trends to Watch in 2025: Revolutionary Technologies Reshaping Business Operations and Customer Experiences

import pandas as pd
import numpy as np
from datetime import datetime, timedelta
from typing import Dict, List, Optional, Any
from dataclasses import dataclass, field
from enum import Enum
import json
import uuid

class TransformationCategory(Enum):
    AI_AUTOMATION = "ai_automation"
    CLOUD_EVOLUTION = "cloud_evolution"
    DATA_ANALYTICS = "data_analytics"
    CUSTOMER_EXPERIENCE = "customer_experience"
    CYBERSECURITY = "cybersecurity"
    SUSTAINABILITY = "sustainability"

class MaturityLevel(Enum):
    BASIC = "basic"
    DEVELOPING = "developing"
    ADVANCED = "advanced"
    OPTIMIZED = "optimized"
    INNOVATING = "innovating"

@dataclass
class TechnologyTrend:
    """Represents a digital transformation technology trend"""
    trend_id: str
    name: str
    category: TransformationCategory
    description: str
    adoption_rate: float  # Percentage of organizations adopting
    impact_score: float   # Business impact rating (1-10)
    implementation_complexity: str  # low, medium, high
    investment_required: float  # Estimated investment in millions
    time_to_value: int  # Months to realize value
    key_benefits: List[str] = field(default_factory=list)
    risk_factors: List[str] = field(default_factory=list)
    
@dataclass
class OrganizationProfile:
    """Profile of organization's digital transformation readiness"""
    org_id: str
    name: str
    industry: str
    size: str  # small, medium, large, enterprise
    current_maturity: Dict[TransformationCategory, MaturityLevel] = field(default_factory=dict)
    technology_investments: Dict[str, float] = field(default_factory=dict)
    transformation_goals: List[str] = field(default_factory=list)
    budget_allocation: float = 0.0
    
@dataclass
class TransformationInitiative:
    """Specific digital transformation initiative"""
    initiative_id: str
    org_id: str
    trend_id: str
    name: str
    objectives: List[str]
    timeline_months: int
    budget: float
    expected_roi: float
    success_metrics: List[str] = field(default_factory=list)
    status: str = "planned"  # planned, active, completed, paused
    
class DigitalTransformationAnalyzer:
    """Comprehensive digital transformation trends analysis and strategy framework"""
    
    def __init__(self, analysis_name: str):
        self.analysis_name = analysis_name
        self.technology_trends: Dict[str, TechnologyTrend] = {}
        self.organizations: Dict[str, OrganizationProfile] = {}
        self.initiatives: List[TransformationInitiative] = []
        
        # Initialize 2025 key technology trends
        self._initialize_2025_trends()
        
        # Transformation assessment criteria
        self.assessment_criteria = {
            TransformationCategory.AI_AUTOMATION: {
                'strategic_importance': 0.25,
                'implementation_feasibility': 0.20,
                'business_impact': 0.30,
                'cost_effectiveness': 0.25
            },
            TransformationCategory.CLOUD_EVOLUTION: {
                'scalability': 0.30,
                'security': 0.25,
                'cost_optimization': 0.25,
                'agility': 0.20
            }
        }
        
        # Industry-specific priorities
        self.industry_priorities = {
            'manufacturing': [TransformationCategory.AI_AUTOMATION, TransformationCategory.DATA_ANALYTICS],
            'healthcare': [TransformationCategory.AI_AUTOMATION, TransformationCategory.CYBERSECURITY],
            'retail': [TransformationCategory.CUSTOMER_EXPERIENCE, TransformationCategory.DATA_ANALYTICS],
            'financial_services': [TransformationCategory.CYBERSECURITY, TransformationCategory.AI_AUTOMATION],
            'education': [TransformationCategory.CLOUD_EVOLUTION, TransformationCategory.CUSTOMER_EXPERIENCE]
        }
        
    def _initialize_2025_trends(self):
        """Initialize key digital transformation trends for 2025"""
        trends_data = [
            {
                'trend_id': 'GENAI_001',
                'name': 'Generative AI Integration',
                'category': TransformationCategory.AI_AUTOMATION,
                'description': 'Integration of generative AI for content creation, automation, and customer interaction',
                'adoption_rate': 68.0,
                'impact_score': 9.5,
                'implementation_complexity': 'medium',
                'investment_required': 2.5,
                'time_to_value': 6,
                'key_benefits': ['60% content production efficiency', 'Personalized customer experiences', 'Automated creative processes'],
                'risk_factors': ['Data privacy concerns', 'Model bias issues', 'Integration complexity']
            },
            {
                'trend_id': 'QUANTUM_001',
                'name': 'Quantum Computing Applications',
                'category': TransformationCategory.DATA_ANALYTICS,
                'description': 'Quantum computing for complex optimization and cryptographic applications',
                'adoption_rate': 15.0,
                'impact_score': 9.8,
                'implementation_complexity': 'high',
                'investment_required': 10.0,
                'time_to_value': 18,
                'key_benefits': ['Exponential processing power', 'Advanced cryptography', 'Complex optimization'],
                'risk_factors': ['High implementation cost', 'Limited expertise', 'Technology maturity']
            },
            {
                'trend_id': 'SPATIAL_001',
                'name': 'Spatial Computing and AR/VR',
                'category': TransformationCategory.CUSTOMER_EXPERIENCE,
                'description': 'Immersive spatial computing experiences and augmented/virtual reality applications',
                'adoption_rate': 45.0,
                'impact_score': 8.5,
                'implementation_complexity': 'medium',
                'investment_required': 3.8,
                'time_to_value': 9,
                'key_benefits': ['Immersive customer experiences', 'Enhanced training programs', 'Virtual collaboration'],
                'risk_factors': ['Hardware limitations', 'User adoption challenges', 'Content development costs']
            },
            {
                'trend_id': 'EDGE_001',
                'name': 'Edge Computing and Intelligence',
                'category': TransformationCategory.DATA_ANALYTICS,
                'description': 'Distributed computing at the edge for real-time processing and reduced latency',
                'adoption_rate': 52.0,
                'impact_score': 8.8,
                'implementation_complexity': 'medium',
                'investment_required': 4.2,
                'time_to_value': 8,
                'key_benefits': ['Reduced latency', 'Improved data privacy', 'Real-time processing'],
                'risk_factors': ['Infrastructure complexity', 'Security management', 'Maintenance overhead']
            },
            {
                'trend_id': 'SUSTAIN_001',
                'name': 'Sustainable Technology Solutions',
                'category': TransformationCategory.SUSTAINABILITY,
                'description': 'Green technology initiatives and sustainable digital transformation practices',
                'adoption_rate': 72.0,
                'impact_score': 7.5,
                'implementation_complexity': 'low',
                'investment_required': 1.8,
                'time_to_value': 12,
                'key_benefits': ['Reduced carbon footprint', 'Cost savings', 'Regulatory compliance'],
                'risk_factors': ['Initial investment costs', 'Technology limitations', 'ROI uncertainty']
            }
        ]
        
        for trend_data in trends_data:
            trend = TechnologyTrend(**trend_data)
            self.technology_trends[trend.trend_id] = trend
            
    def register_organization(self, org: OrganizationProfile) -> bool:
        """Register organization for transformation analysis"""
        self.organizations[org.org_id] = org
        
        # Set default maturity levels if not provided
        if not org.current_maturity:
            for category in TransformationCategory:
                org.current_maturity[category] = MaturityLevel.DEVELOPING
                
        print(f"Registered organization: {org.name} ({org.industry})")
        return True
        
    def assess_transformation_readiness(self, org_id: str) -> Dict[str, Any]:
        """Assess organization's digital transformation readiness"""
        if org_id not in self.organizations:
            return {'error': 'Organization not found'}
            
        org = self.organizations[org_id]
        
        # Calculate readiness scores by category
        readiness_scores = {}
        overall_readiness = 0
        
        for category in TransformationCategory:
            maturity = org.current_maturity.get(category, MaturityLevel.BASIC)
            maturity_score = self._calculate_maturity_score(maturity)
            
            # Adjust score based on industry priorities
            industry_priorities = self.industry_priorities.get(org.industry, [])
            priority_multiplier = 1.2 if category in industry_priorities else 1.0
            
            adjusted_score = maturity_score * priority_multiplier
            readiness_scores[category.value] = {
                'maturity_level': maturity.value,
                'base_score': maturity_score,
                'adjusted_score': min(adjusted_score, 10.0),
                'industry_priority': category in industry_priorities
            }
            
            overall_readiness += adjusted_score
            
        overall_readiness = min(overall_readiness / len(TransformationCategory), 10.0)
        
        # Generate recommendations
        recommendations = self._generate_readiness_recommendations(org, readiness_scores)
        
        assessment = {
            'organization_id': org_id,
            'organization_name': org.name,
            'industry': org.industry,
            'size': org.size,
            'overall_readiness_score': overall_readiness,
            'readiness_level': self._classify_readiness_level(overall_readiness),
            'category_scores': readiness_scores,
            'recommendations': recommendations,
            'priority_areas': self._identify_priority_areas(readiness_scores),
            'investment_guidance': self._generate_investment_guidance(org, readiness_scores)
        }
        
        return assessment
        
    def recommend_technology_trends(self, org_id: str, focus_areas: List[TransformationCategory] = None) -> Dict[str, Any]:
        """Recommend relevant technology trends for organization"""
        if org_id not in self.organizations:
            return {'error': 'Organization not found'}
            
        org = self.organizations[org_id]
        
        # Filter trends based on focus areas or industry priorities
        if not focus_areas:
            focus_areas = self.industry_priorities.get(org.industry, list(TransformationCategory))
            
        relevant_trends = []
        for trend in self.technology_trends.values():
            if trend.category in focus_areas:
                # Calculate relevance score
                relevance_score = self._calculate_trend_relevance(org, trend)
                
                trend_recommendation = {
                    'trend_id': trend.trend_id,
                    'name': trend.name,
                    'category': trend.category.value,
                    'description': trend.description,
                    'relevance_score': relevance_score,
                    'adoption_rate': trend.adoption_rate,
                    'impact_score': trend.impact_score,
                    'implementation_complexity': trend.implementation_complexity,
                    'investment_required': trend.investment_required,
                    'time_to_value': trend.time_to_value,
                    'key_benefits': trend.key_benefits,
                    'risk_factors': trend.risk_factors,
                    'recommendation_priority': self._determine_priority(relevance_score, trend.impact_score)
                }
                
                relevant_trends.append(trend_recommendation)
                
        # Sort by relevance score
        relevant_trends.sort(key=lambda x: x['relevance_score'], reverse=True)
        
        recommendations = {
            'organization_id': org_id,
            'focus_areas': [area.value for area in focus_areas],
            'recommended_trends': relevant_trends,
            'top_3_priorities': relevant_trends[:3],
            'implementation_roadmap': self._generate_implementation_roadmap(relevant_trends[:5]),
            'budget_estimation': self._estimate_implementation_budget(relevant_trends[:5])
        }
        
        return recommendations
        
    def create_transformation_initiative(self, org_id: str, trend_id: str, 
                                       initiative_name: str, objectives: List[str], 
                                       timeline_months: int, budget: float) -> TransformationInitiative:
        """Create new digital transformation initiative"""
        if org_id not in self.organizations:
            raise ValueError('Organization not found')
        if trend_id not in self.technology_trends:
            raise ValueError('Technology trend not found')
            
        trend = self.technology_trends[trend_id]
        
        # Estimate ROI based on trend impact and organization characteristics
        expected_roi = self._estimate_initiative_roi(org_id, trend, budget, timeline_months)
        
        # Generate success metrics
        success_metrics = self._generate_success_metrics(trend, objectives)
        
        initiative = TransformationInitiative(
            initiative_id=f"INIT_{uuid.uuid4()}",
            org_id=org_id,
            trend_id=trend_id,
            name=initiative_name,
            objectives=objectives,
            timeline_months=timeline_months,
            budget=budget,
            expected_roi=expected_roi,
            success_metrics=success_metrics
        )
        
        self.initiatives.append(initiative)
        return initiative
        
    def analyze_transformation_portfolio(self, org_id: str) -> Dict[str, Any]:
        """Analyze organization's digital transformation portfolio"""
        if org_id not in self.organizations:
            return {'error': 'Organization not found'}
            
        org = self.organizations[org_id]
        org_initiatives = [i for i in self.initiatives if i.org_id == org_id]
        
        if not org_initiatives:
            return {'message': 'No transformation initiatives found for organization'}
            
        # Portfolio analysis
        total_investment = sum(init.budget for init in org_initiatives)
        total_expected_roi = sum(init.expected_roi for init in org_initiatives)
        avg_timeline = np.mean([init.timeline_months for init in org_initiatives])
        
        # Category distribution
        category_investment = {}
        category_count = {}
        
        for initiative in org_initiatives:
            trend = self.technology_trends[initiative.trend_id]
            category = trend.category.value
            
            category_investment[category] = category_investment.get(category, 0) + initiative.budget
            category_count[category] = category_count.get(category, 0) + 1
            
        # Risk assessment
        portfolio_risk = self._assess_portfolio_risk(org_initiatives)
        
        # Generate insights
        insights = self._generate_portfolio_insights(org, org_initiatives, category_investment)
        
        analysis = {
            'organization_id': org_id,
            'portfolio_summary': {
                'total_initiatives': len(org_initiatives),
                'total_investment': total_investment,
                'expected_total_roi': total_expected_roi,
                'average_timeline_months': avg_timeline,
                'portfolio_roi_ratio': total_expected_roi / total_investment if total_investment > 0 else 0
            },
            'category_distribution': {
                'investment_by_category': category_investment,
                'initiatives_by_category': category_count
            },
            'risk_assessment': portfolio_risk,
            'portfolio_balance': self._assess_portfolio_balance(category_investment),
            'insights_and_recommendations': insights,
            'optimization_opportunities': self._identify_optimization_opportunities(org_initiatives)
        }
        
        return analysis
        
    def generate_industry_benchmark_report(self, industry: str) -> Dict[str, Any]:
        """Generate industry benchmark report for digital transformation"""
        industry_orgs = [org for org in self.organizations.values() if org.industry == industry]
        
        if not industry_orgs:
            return {'error': f'No organizations found for {industry} industry'}
            
        # Calculate industry averages
        industry_metrics = self._calculate_industry_metrics(industry_orgs)
        
        # Identify leading practices
        leading_practices = self._identify_leading_practices(industry_orgs)
        
        # Technology adoption patterns
        adoption_patterns = self._analyze_adoption_patterns(industry, industry_orgs)
        
        # Investment trends
        investment_trends = self._analyze_investment_trends(industry_orgs)
        
        benchmark_report = {
            'industry': industry,
            'organizations_analyzed': len(industry_orgs),
            'industry_metrics': industry_metrics,
            'leading_practices': leading_practices,
            'technology_adoption_patterns': adoption_patterns,
            'investment_trends': investment_trends,
            'industry_challenges': self._identify_industry_challenges(industry),
            'future_opportunities': self._identify_future_opportunities(industry),
            'strategic_recommendations': self._generate_industry_recommendations(industry, industry_metrics)
        }
        
        return benchmark_report
        
    # Helper methods for calculations and analysis
    def _calculate_maturity_score(self, maturity: MaturityLevel) -> float:
        """Convert maturity level to numerical score"""
        scores = {
            MaturityLevel.BASIC: 2.0,
            MaturityLevel.DEVELOPING: 4.0,
            MaturityLevel.ADVANCED: 6.0,
            MaturityLevel.OPTIMIZED: 8.0,
            MaturityLevel.INNOVATING: 10.0
        }
        return scores.get(maturity, 2.0)
        
    def _classify_readiness_level(self, score: float) -> str:
        """Classify overall readiness based on score"""
        if score >= 8.5:
            return "Highly Ready"
        elif score >= 6.5:
            return "Ready"
        elif score >= 4.5:
            return "Moderately Ready"
        else:
            return "Needs Development"
            
    def _calculate_trend_relevance(self, org: OrganizationProfile, trend: TechnologyTrend) -> float:
        """Calculate relevance score for trend to organization"""
        base_relevance = trend.impact_score
        
        # Adjust for organization size
        size_multiplier = {'small': 0.8, 'medium': 1.0, 'large': 1.2, 'enterprise': 1.4}.get(org.size, 1.0)
        
        # Adjust for industry priority
        industry_priorities = self.industry_priorities.get(org.industry, [])
        priority_bonus = 1.5 if trend.category in industry_priorities else 1.0
        
        # Adjust for current maturity
        maturity = org.current_maturity.get(trend.category, MaturityLevel.BASIC)
        maturity_factor = self._calculate_maturity_score(maturity) / 10.0
        
        relevance_score = base_relevance * size_multiplier * priority_bonus * (1 + maturity_factor)
        return min(relevance_score, 10.0)
        
    def _determine_priority(self, relevance_score: float, impact_score: float) -> str:
        """Determine implementation priority"""
        combined_score = (relevance_score + impact_score) / 2
        if combined_score >= 8.5:
            return "High"
        elif combined_score >= 6.5:
            return "Medium"
        else:
            return "Low"
            
    # Additional helper methods (simplified implementations)
    def _generate_readiness_recommendations(self, org, scores): return ["Invest in AI capabilities", "Enhance cybersecurity", "Modernize cloud infrastructure"]
    def _identify_priority_areas(self, scores): return [category for category, data in scores.items() if data['adjusted_score'] < 6.0]
    def _generate_investment_guidance(self, org, scores): return {"total_recommended": 5.0, "priority_allocation": "AI and automation"}
    def _generate_implementation_roadmap(self, trends): return {"phase_1": trends[:2], "phase_2": trends[2:4], "phase_3": trends[4:]}
    def _estimate_implementation_budget(self, trends): return sum(t['investment_required'] for t in trends)
    def _estimate_initiative_roi(self, org_id, trend, budget, timeline): return budget * 0.3 * (trend.impact_score / 10)
    def _generate_success_metrics(self, trend, objectives): return ["User adoption rate", "Efficiency improvement", "Cost reduction"]
    def _assess_portfolio_risk(self, initiatives): return {"overall_risk": "Medium", "key_risks": ["Technology complexity", "Change management"]}
    def _generate_portfolio_insights(self, org, initiatives, investments): return ["Balanced technology portfolio", "Strong AI focus"]
    def _assess_portfolio_balance(self, investments): return "Well-balanced across transformation categories"
    def _identify_optimization_opportunities(self, initiatives): return ["Consolidate AI initiatives", "Accelerate cloud migration"]
    def _calculate_industry_metrics(self, orgs): return {"avg_investment": 5.5, "maturity_level": "Advanced"}
    def _identify_leading_practices(self, orgs): return ["AI-first strategy", "Cloud-native architecture"]
    def _analyze_adoption_patterns(self, industry, orgs): return {"top_technologies": ["AI", "Cloud", "Analytics"]}
    def _analyze_investment_trends(self, orgs): return {"trend": "Increasing", "focus_areas": ["AI", "Cybersecurity"]}
    def _identify_industry_challenges(self, industry): return ["Skills shortage", "Legacy system integration"]
    def _identify_future_opportunities(self, industry): return ["Quantum computing", "Spatial computing"]
    def _generate_industry_recommendations(self, industry, metrics): return ["Prioritize AI adoption", "Invest in talent development"]

# Example usage and demonstration
def run_digital_transformation_analysis_demo():
    print("=== Digital Transformation Trends Analysis Demo ===")
    
    # Initialize transformation analyzer
    analyzer = DigitalTransformationAnalyzer("2025 Digital Transformation Trends")
    
    # Register sample organizations
    organizations = [
        OrganizationProfile(
            org_id="ORG_TECH_001",
            name="TechCorp Solutions",
            industry="technology",
            size="large",
            current_maturity={
                TransformationCategory.AI_AUTOMATION: MaturityLevel.ADVANCED,
                TransformationCategory.CLOUD_EVOLUTION: MaturityLevel.OPTIMIZED,
                TransformationCategory.DATA_ANALYTICS: MaturityLevel.ADVANCED,
                TransformationCategory.CYBERSECURITY: MaturityLevel.DEVELOPING
            },
            budget_allocation=15.0
        ),
        OrganizationProfile(
            org_id="ORG_RETAIL_001",
            name="Global Retail Chain",
            industry="retail",
            size="enterprise",
            current_maturity={
                TransformationCategory.CUSTOMER_EXPERIENCE: MaturityLevel.ADVANCED,
                TransformationCategory.DATA_ANALYTICS: MaturityLevel.DEVELOPING,
                TransformationCategory.AI_AUTOMATION: MaturityLevel.DEVELOPING
            },
            budget_allocation=25.0
        )
    ]
    
    for org in organizations:
        analyzer.register_organization(org)
        
    print(f"\nRegistered {len(organizations)} organizations for analysis")
    
    # Assess transformation readiness
    print("\n=== Digital Transformation Readiness Assessment ===")
    
    for org in organizations:
        assessment = analyzer.assess_transformation_readiness(org.org_id)
        
        print(f"\n{assessment['organization_name']} ({assessment['industry'].title()})")
        print(f"Overall Readiness: {assessment['overall_readiness_score']:.1f}/10 ({assessment['readiness_level']})")
        
        print("Category Scores:")
        for category, scores in assessment['category_scores'].items():
            priority_indicator = "🎯" if scores['industry_priority'] else "  "
            print(f"  {priority_indicator} {category.replace('_', ' ').title()}: {scores['adjusted_score']:.1f}/10 ({scores['maturity_level']})")
            
        print(f"Priority Areas: {', '.join(assessment['priority_areas'])}")
        print(f"Investment Guidance: ${assessment['investment_guidance']['total_recommended']:.1f}M recommended")
        
    # Technology trend recommendations
    print("\n=== Technology Trend Recommendations ===")
    
    for org in organizations:
        recommendations = analyzer.recommend_technology_trends(org.org_id)
        
        print(f"\n{org.name} - Top Technology Recommendations:")
        
        for i, trend in enumerate(recommendations['top_3_priorities'], 1):
            print(f"\n{i}. {trend['name']} (Priority: {trend['recommendation_priority']})")
            print(f"   Category: {trend['category'].replace('_', ' ').title()}")
            print(f"   Impact Score: {trend['impact_score']:.1f}/10")
            print(f"   Adoption Rate: {trend['adoption_rate']:.1f}%")
            print(f"   Investment: ${trend['investment_required']:.1f}M")
            print(f"   Time to Value: {trend['time_to_value']} months")
            print(f"   Key Benefits: {', '.join(trend['key_benefits'][:2])}")
            
        budget_est = recommendations['budget_estimation']
        print(f"\nTotal Implementation Budget: ${budget_est:.1f}M")
        
    # Create transformation initiatives
    print("\n=== Creating Transformation Initiatives ===")
    
    initiatives_data = [
        {
            'org_id': 'ORG_TECH_001',
            'trend_id': 'GENAI_001',
            'name': 'AI-Powered Development Platform',
            'objectives': ['Increase developer productivity by 40%', 'Automate code review process', 'Enhance software quality'],
            'timeline': 8,
            'budget': 3.2
        },
        {
            'org_id': 'ORG_RETAIL_001',
            'trend_id': 'SPATIAL_001',
            'name': 'Immersive Shopping Experience',
            'objectives': ['Launch AR try-on features', 'Increase customer engagement', 'Reduce return rates'],
            'timeline': 12,
            'budget': 4.5
        }
    ]
    
    for init_data in initiatives_data:
        initiative = analyzer.create_transformation_initiative(
            init_data['org_id'],
            init_data['trend_id'],
            init_data['name'],
            init_data['objectives'],
            init_data['timeline'],
            init_data['budget']
        )
        
        print(f"\nCreated Initiative: {initiative.name}")
        print(f"  Organization: {analyzer.organizations[initiative.org_id].name}")
        print(f"  Budget: ${initiative.budget:.1f}M")
        print(f"  Expected ROI: ${initiative.expected_roi:.1f}M")
        print(f"  Timeline: {initiative.timeline_months} months")
        print(f"  Success Metrics: {', '.join(initiative.success_metrics)}")
        
    # Analyze transformation portfolios
    print("\n=== Portfolio Analysis ===")
    
    for org in organizations:
        portfolio = analyzer.analyze_transformation_portfolio(org.org_id)
        
        if 'error' not in portfolio and 'message' not in portfolio:
            print(f"\n{org.name} Portfolio Analysis:")
            summary = portfolio['portfolio_summary']
            print(f"  Total Initiatives: {summary['total_initiatives']}")
            print(f"  Total Investment: ${summary['total_investment']:.1f}M")
            print(f"  Expected ROI: ${summary['expected_total_roi']:.1f}M")
            print(f"  ROI Ratio: {summary['portfolio_roi_ratio']:.2f}")
            print(f"  Average Timeline: {summary['average_timeline_months']:.1f} months")
            
            print(f"  Portfolio Balance: {portfolio['portfolio_balance']}")
            print(f"  Risk Level: {portfolio['risk_assessment']['overall_risk']}")
            
    # Industry benchmark report
    print("\n=== Industry Benchmark Report ===")
    
    benchmark = analyzer.generate_industry_benchmark_report("retail")
    
    if 'error' not in benchmark:
        print(f"Retail Industry Benchmark:")
        print(f"  Organizations Analyzed: {benchmark['organizations_analyzed']}")
        metrics = benchmark['industry_metrics']
        print(f"  Average Investment: ${metrics['avg_investment']:.1f}M")
        print(f"  Maturity Level: {metrics['maturity_level']}")
        
        print(f"  Leading Practices: {', '.join(benchmark['leading_practices'])}")
        print(f"  Top Technologies: {', '.join(benchmark['technology_adoption_patterns']['top_technologies'])}")
        
        print("  Strategic Recommendations:")
        for i, rec in enumerate(benchmark['strategic_recommendations'], 1):
            print(f"    {i}. {rec}")
            
    return analyzer

# Run demonstration
if __name__ == "__main__":
    demo_analyzer = run_digital_transformation_analysis_demo()