# ===================================================================
# MODULE 7, LESSON 6: STRATEGIC ROADMAP FOR TECHNOLOGY ADOPTION
# ===================================================================

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from datetime import datetime, timedelta
import warnings
warnings.filterwarnings('ignore')

# Set style
sns.set_style("whitegrid")
np.random.seed(42)

print("="*70)
print("STRATEGIC ROADMAP FOR TECHNOLOGY ADOPTION IN FINANCE")
print("="*70)

# ----------------------------------------------------------------
# PART A: TECHNOLOGY ASSESSMENT MATRIX
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART A: Technology Assessment Matrix")
print("-"*60)

# Define technologies
technologies = [
    "Cloud Computing",
    "Big Data Analytics",
    "Machine Learning",
    "Deep Learning",
    "NLP / LLMs",
    "Computer Vision",
    "Generative AI",
    "Explainable AI (XAI)",
    "Robotic Process Automation",
    "Blockchain / DLT",
    "DeFi / Smart Contracts",
    "Web3 / Decentralised Identity",
    "Quantum Computing",
    "Edge AI / IoT",
    "Synthetic Data Generation",
    "Federated Learning",
    "Digital Twins",
    "Metaverse / AR/VR"
]

# Assess each technology on dimensions
np.random.seed(42)
assessment = []
for tech in technologies:
    strategic_alignment = np.random.uniform(0.3, 0.95)
    feasibility = np.random.uniform(0.2, 0.9)
    impact = np.random.uniform(0.4, 0.95)
    risk = np.random.uniform(0.1, 0.8)
    cost = np.random.uniform(0.2, 0.9)
    
    # Some technologies are further along
    if "Cloud" in tech:
        feasibility = 0.9
        strategic_alignment = 0.85
    if "Quantum" in tech:
        feasibility = 0.2
        risk = 0.5
    if "Web3" in tech or "DeFi" in tech:
        strategic_alignment = 0.7
        risk = 0.7
    
    assessment.append({
        'Technology': tech,
        'Strategic Alignment': strategic_alignment,
        'Feasibility': feasibility,
        'Impact': impact,
        'Risk': risk,
        'Cost': cost
    })

assessment_df = pd.DataFrame(assessment)

# Calculate score (weighted)
weights = {'Strategic Alignment': 0.25, 'Feasibility': 0.20, 'Impact': 0.25, 'Risk': 0.15, 'Cost': 0.15}
# Invert risk and cost for scoring (lower risk/cost is better)
assessment_df['Risk_Score'] = 1 - assessment_df['Risk']
assessment_df['Cost_Score'] = 1 - assessment_df['Cost']

assessment_df['Total_Score'] = (
    weights['Strategic Alignment'] * assessment_df['Strategic Alignment'] +
    weights['Feasibility'] * assessment_df['Feasibility'] +
    weights['Impact'] * assessment_df['Impact'] +
    weights['Risk'] * assessment_df['Risk_Score'] +
    weights['Cost'] * assessment_df['Cost_Score']
)

# Sort by total score
assessment_df = assessment_df.sort_values('Total_Score', ascending=False)
print("Technology Assessment Results (Top 10):")
print(assessment_df[['Technology', 'Total_Score']].head(10).to_string(index=False))

# Visualise
fig, ax = plt.subplots(figsize=(12, 8))
top_techs = assessment_df.head(10)
ax.barh(top_techs['Technology'], top_techs['Total_Score'], color='blue', alpha=0.7)
ax.set_xlabel('Total Score')
ax.set_title('Technology Assessment – Priority Ranking')
ax.grid(True, alpha=0.3)
plt.tight_layout()
plt.savefig('technology_assessment.png', dpi=300)
plt.show()

# ----------------------------------------------------------------
# PART B: GARTNER HYPE CYCLE POSITIONING
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART B: Gartner Hype Cycle Positioning")
print("-"*60)

# Assign each technology to a hype cycle phase
hype_phases = {
    'Innovation Trigger': [],
    'Peak of Inflated Expectations': [],
    'Trough of Disillusionment': [],
    'Slope of Enlightenment': [],
    'Plateau of Productivity': []
}

phase_assignments = {
    'Quantum Computing': 'Innovation Trigger',
    'Generative AI': 'Peak of Inflated Expectations',
    'Web3 / Decentralised Identity': 'Peak of Inflated Expectations',
    'Metaverse / AR/VR': 'Trough of Disillusionment',
    'Blockchain / DLT': 'Slope of Enlightenment',
    'Synthetic Data Generation': 'Slope of Enlightenment',
    'Federated Learning': 'Slope of Enlightenment',
    'Digital Twins': 'Slope of Enlightenment',
    'Edge AI / IoT': 'Slope of Enlightenment',
    'DeFi / Smart Contracts': 'Slope of Enlightenment',
    'Explainable AI (XAI)': 'Plateau of Productivity',
    'Machine Learning': 'Plateau of Productivity',
    'Cloud Computing': 'Plateau of Productivity',
    'Robotic Process Automation': 'Plateau of Productivity',
    'NLP / LLMs': 'Slope of Enlightenment',
    'Computer Vision': 'Slope of Enlightenment',
    'Deep Learning': 'Plateau of Productivity'
}

for tech in technologies:
    phase = phase_assignments.get(tech, 'Slope of Enlightenment')
    hype_phases[phase].append(tech)

print("Gartner Hype Cycle Positioning:")
for phase, techs in hype_phases.items():
    print(f"\n{phase}:")
    for tech in techs:
        print(f"  • {tech}")

# Visualise hype cycle
fig, ax = plt.subplots(figsize=(14, 8))
phases = list(hype_phases.keys())
x_positions = np.linspace(0, 10, len(phases))
y_positions = [8, 9.5, 5, 7, 6]  # Approximate hype cycle curve

# Plot the hype curve
x_curve = np.linspace(0, 10, 100)
y_curve = 6 + 3 * np.sin((x_curve - 1) * np.pi / 5) + 0.5 * (x_curve - 5) * 0.1
ax.plot(x_curve, y_curve, 'b-', linewidth=2, alpha=0.3)

# Plot technologies
for phase, techs in hype_phases.items():
    idx = phases.index(phase)
    x = x_positions[idx]
    y = y_positions[idx]
    ax.scatter(x, y, s=80, color='red', marker='o')
    ax.text(x, y + 0.3, phase, ha='center', fontsize=9, fontweight='bold')
    for tech in techs[:3]:  # Show a few technologies per phase
        ax.text(x + np.random.uniform(-0.3, 0.3), y - np.random.uniform(0.1, 0.5), 
                tech, ha='center', va='top', fontsize=7, alpha=0.7)

ax.set_xlim(-0.5, 10.5)
ax.set_ylim(3, 11)
ax.set_xticks([])
ax.set_yticks([])
ax.set_title('Gartner Hype Cycle – Emerging Technologies in Finance', fontsize=14)
plt.tight_layout()
plt.savefig('hype_cycle.png', dpi=300)
plt.show()

# ----------------------------------------------------------------
# PART C: PHASED IMPLEMENTATION ROADMAP
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART C: Phased Implementation Roadmap")
print("-"*60)

# Define roadmap phases
roadmap = {
    'Phase 1 (0-12 months): Foundation': {
        'Focus': 'Build core data and AI capabilities',
        'Technologies': ['Cloud Computing', 'Big Data Analytics', 'Machine Learning', 'RPA'],
        'Key Activities': [
            'Migrate to cloud (hybrid/multi-cloud)',
            'Establish data lake and data governance',
            'Implement basic ML for credit scoring',
            'Automate routine processes (RPA)'
        ],
        'KPIs': [
            'Data quality score > 95%',
            'Customer data accessible within 100ms',
            'Automation rate > 30%'
        ]
    },
    'Phase 2 (12-24 months): Acceleration': {
        'Focus': 'Scale AI and enhance customer experience',
        'Technologies': ['Deep Learning', 'NLP / LLMs', 'Explainable AI', 'Synthetic Data'],
        'Key Activities': [
            'Deploy deep learning for fraud detection',
            'Implement NLP for customer service chatbots',
            'Develop XAI capabilities for regulatory compliance',
            'Use synthetic data for model training'
        ],
        'KPIs': [
            'Fraud detection accuracy > 99%',
            'Customer satisfaction (NPS) > 70',
            'Model validation time reduced by 50%'
        ]
    },
    'Phase 3 (24-36 months): Transformation': {
        'Focus': 'Innovate with emerging technologies',
        'Technologies': ['Web3 / DID', 'Federated Learning', 'Digital Twins', 'Edge AI'],
        'Key Activities': [
            'Pilot decentralised identity (DID) for KYC',
            'Implement federated learning for cross-bank collaboration',
            'Develop digital twins for portfolio management',
            'Deploy edge AI for ATM/real-time fraud detection'
        ],
        'KPIs': [
            'KYC onboarding time < 5 minutes',
            'Cross-bank fraud detection rate > 95%',
            'Portfolio simulation accuracy > 90%'
        ]
    },
    'Phase 4 (36+ months): Leadership': {
        'Focus': 'Industry leadership and ecosystem orchestration',
        'Technologies': ['Quantum Computing', 'Metaverse', 'DeFi Integration'],
        'Key Activities': [
            'Explore quantum computing for optimisation',
            'Develop metaverse banking presence',
            'Integrate with DeFi protocols',
            'Build open banking ecosystem'
        ],
        'KPIs': [
            'New business lines revenue > 10% of total',
            'Metaverse customer engagement > 1M users',
            'Quantum advantage demonstrated for key problems'
        ]
    }
}

# Print roadmap
for phase, details in roadmap.items():
    print(f"\n{phase}")
    print(f"  Focus: {details['Focus']}")
    print(f"  Technologies: {', '.join(details['Technologies'])}")
    print("  Key Activities:")
    for activity in details['Key Activities']:
        print(f"    • {activity}")
    print("  KPIs:")
    for kpi in details['KPIs']:
        print(f"    • {kpi}")

# ----------------------------------------------------------------
# PART D: BUSINESS CASE TEMPLATE
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART D: Business Case Template")
print("-"*60)

def generate_business_case(project_name, description, benefits, costs, risks, timeline):
    """
    Generate a structured business case.
    """
    business_case = f"""
--- BUSINESS CASE: {project_name} ---

1. EXECUTIVE SUMMARY
   {description}

2. OBJECTIVES
   - {benefits[0] if benefits else 'No objectives defined.'}

3. STRATEGIC ALIGNMENT
   - Supports digital transformation strategy.
   - Enhances customer experience and operational efficiency.
   - Positions the bank as an innovator.

4. BENEFITS
   - Financial Benefits:
     • Revenue uplift: ${benefits.get('revenue_uplift', 0):,.2f}
     • Cost savings: ${benefits.get('cost_savings', 0):,.2f}
     • ROI: {benefits.get('roi', 0):.1f}%
   - Non-Financial Benefits:
     • {benefits.get('non_financial_1', 'Improved customer satisfaction')}
     • {benefits.get('non_financial_2', 'Enhanced competitive position')}
     • {benefits.get('non_financial_3', 'Regulatory compliance')}

5. COSTS
   - Capital Expenditure: ${costs.get('capex', 0):,.2f}
   - Operational Expenditure: ${costs.get('opex', 0):,.2f}
   - Total Investment: ${costs.get('total', 0):,.2f}

6. RISKS AND MITIGATIONS
   - {risks.get('risk_1', 'Technology risk')}: {risks.get('mitigation_1', 'Phased implementation, pilot testing')}
   - {risks.get('risk_2', 'Resource risk')}: {risks.get('mitigation_2', 'Training and hiring plan')}
   - {risks.get('risk_3', 'Regulatory risk')}: {risks.get('mitigation_3', 'Early engagement with regulators')}

7. TIMELINE
   - Start Date: {timeline.get('start', 'Q1 2025')}
   - Key Milestones:
     • {timeline.get('milestone_1', 'Pilot launch: Q2 2025')}
     • {timeline.get('milestone_2', 'Full deployment: Q4 2025')}
     • {timeline.get('milestone_3', 'Optimisation: Q2 2026')}
   - Go-Live: {timeline.get('go_live', 'Q3 2026')}

8. RECOMMENDATION
   APPROVE the project with a {benefits.get('roi', 0):.1f}% ROI over 3 years.
"""
    return business_case

# Generate a sample business case for AI-powered fraud detection
sample_case = generate_business_case(
    project_name="AI-Powered Fraud Detection System",
    description="Implement a real-time, AI-driven fraud detection system to reduce financial losses and improve customer trust.",
    benefits={
        'revenue_uplift': 0,
        'cost_savings': 5000000,
        'roi': 250,
        'non_financial_1': 'Reduced false positives (50% improvement)',
        'non_financial_2': 'Enhanced customer trust and satisfaction',
        'non_financial_3': 'Compliance with regulatory expectations'
    },
    costs={
        'capex': 2000000,
        'opex': 500000,
        'total': 2500000
    },
    risks={
        'risk_1': 'Model performance degradation',
        'mitigation_1': 'Continuous monitoring and retraining',
        'risk_2': 'Integration with legacy systems',
        'mitigation_2': 'Phased integration with API-first approach',
        'risk_3': 'Regulatory scrutiny of AI models',
        'mitigation_3': 'XAI implementation and model validation'
    },
    timeline={
        'start': 'Q1 2025',
        'milestone_1': 'Pilot launch: Q2 2025',
        'milestone_2': 'Full deployment: Q4 2025',
        'milestone_3': 'Optimisation: Q2 2026',
        'go_live': 'Q3 2026'
    }
)

print(sample_case)

# ----------------------------------------------------------------
# PART E: SUCCESS FACTORS AND RISK MANAGEMENT
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART E: Success Factors and Risk Management")
print("-"*60)

success_factors = {
    "Leadership Commitment": {
        "Description": "Executive sponsorship and visible commitment.",
        "Action": "Establish Digital Transformation Steering Committee."
    },
    "Talent and Skills": {
        "Description": "Attract and retain skilled data scientists, engineers, and AI specialists.",
        "Action": "Develop talent pipeline; offer competitive compensation; continuous learning."
    },
    "Data Infrastructure": {
        "Description": "Robust data governance, quality, and accessibility.",
        "Action": "Invest in data lake, data mesh, and data quality tools."
    },
    "Agile Culture": {
        "Description": "Embrace Agile methodologies and fail-fast mindset.",
        "Action": "Train teams in Agile; create innovation labs; celebrate failures as learning."
    },
    "Customer-Centricity": {
        "Description": "Focus on customer outcomes, not just technology.",
        "Action": "Involve customers in design; track NPS and customer satisfaction."
    },
    "Regulatory Engagement": {
        "Description": "Proactive engagement with regulators.",
        "Action": "Regular updates; joint working groups; early consultations."
    },
    "Partnerships": {
        "Description": "Collaborate with fintechs, tech vendors, and academia.",
        "Action": "Establish innovation partnerships; invest in fintech ventures."
    }
}

print("Critical Success Factors:")
for factor, details in success_factors.items():
    print(f"\n{factor}:")
    print(f"  {details['Description']}")
    print(f"  Action: {details['Action']}")

# ----------------------------------------------------------------
# PART F: CHANGE MANAGEMENT
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART F: Change Management")
print("-"*60)

change_management = {
    "Awareness": {
        "Description": "Communicate the vision and why change is needed.",
        "Tools": "Town halls, newsletters, leadership communications."
    },
    "Desire": {
        "Description": "Build motivation and commitment.",
        "Tools": "Incentives, recognition, early adopters as champions."
    },
    "Knowledge": {
        "Description": "Provide training and resources.",
        "Tools": "Training programs, workshops, online courses."
    },
    "Ability": {
        "Description": "Ensure teams have the skills and tools to succeed.",
        "Tools": "Coaching, mentoring, hands-on practice."
    },
    "Reinforcement": {
        "Description": "Sustain change and embed it in culture.",
        "Tools": "Feedback loops, performance metrics, continuous improvement."
    }
}

print("Change Management Framework (ADKAR):")
for stage, details in change_management.items():
    print(f"\n{stage}:")
    print(f"  {details['Description']}")
    print(f"  Tools: {details['Tools']}")

# ----------------------------------------------------------------
# PART G: PERSONAL CAREER ROADMAP
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART G: Personal Career Roadmap")
print("-"*60)

career_roadmap = {
    "0-2 Years: Foundation": {
        "Focus": "Build core technical skills and domain knowledge.",
        "Skills": ["SQL", "Python", "Statistics", "Data Visualisation", "Banking Fundamentals"],
        "Certifications": ["CFA Level I", "FRM Part I", "Data Science Certifications"],
        "Roles": ["Data Analyst", "Junior Data Scientist"]
    },
    "2-5 Years: Specialisation": {
        "Focus": "Develop deep expertise in a specific domain.",
        "Skills": ["Machine Learning", "Deep Learning", "Financial Risk", "Model Validation"],
        "Certifications": ["CFA Level II/III", "FRM Part II", "AI Certifications"],
        "Roles": ["Senior Data Scientist", "Risk Analyst", "ML Engineer"]
    },
    "5-10 Years: Leadership": {
        "Focus": "Lead teams and shape strategy.",
        "Skills": ["Strategy", "Leadership", "Communication", "Stakeholder Management"],
        "Certifications": ["MBA", "Executive Education"],
        "Roles": ["Head of Data Science", "Chief Data Officer", "Director of Analytics"]
    },
    "10+ Years: Visionary": {
        "Focus": "Drive innovation and industry transformation.",
        "Skills": ["Industry Thought Leadership", "Innovation", "Entrepreneurship"],
        "Certifications": ["Leadership Programs", "Advisory Roles"],
        "Roles": ["Chief AI Officer", "Chief Digital Officer", "Board Advisor"]
    }
}

print("Personal Career Roadmap:")
for phase, details in career_roadmap.items():
    print(f"\n{phase}")
    print(f"  Focus: {details['Focus']}")
    print(f"  Skills: {', '.join(details['Skills'])}")
    print(f"  Certifications: {', '.join(details['Certifications'])}")
    print(f"  Roles: {', '.join(details['Roles'])}")

# ----------------------------------------------------------------
# PART H: FINAL SUMMARY AND RECOMMENDATIONS
# ----------------------------------------------------------------

print("\n" + "="*70)
print("PART H: Final Summary and Recommendations")
print("="*70)

print("""
STRATEGIC ROADMAP FOR TECHNOLOGY ADOPTION – KEY TAKEAWAYS:

1. Technology adoption is strategic, not just technical.
2. Use structured frameworks (Hype Cycle, Adoption Lifecycle) to guide decisions.
3. Assess technologies on multiple dimensions: strategic alignment, feasibility, impact, risk, cost.
4. Develop a phased roadmap: Foundation → Acceleration → Transformation → Leadership.
5. Build a compelling business case with clear ROI.
6. Critical success factors: leadership, talent, culture, partnerships, regulatory engagement.
7. Manage change with ADKAR: Awareness, Desire, Knowledge, Ability, Reinforcement.
8. Develop a personal career roadmap to stay relevant and progress.

FINAL RECOMMENDATIONS:

For Banks:
  - Invest in data infrastructure and AI capabilities.
  - Embrace open banking and ecosystem partnerships.
  - Experiment with emerging technologies (Web3, quantum) in controlled pilots.
  - Foster a culture of innovation and continuous learning.
  - Engage proactively with regulators and stakeholders.

For Individuals:
  - Build a strong foundation in data science and finance.
  - Specialise in high-demand areas (AI, risk, data governance).
  - Stay curious and continuously learn.
  - Network with peers and industry leaders.
  - Develop leadership and communication skills.
  - Contribute to the community (open source, publications, speaking).

THE FUTURE OF FINANCIAL DATA ANALYTICS:
  - AI will be ubiquitous and embedded in all processes.
  - Data privacy and ethics will be paramount.
  - Real-time analytics will become the norm.
  - Integration of traditional and decentralised finance.
  - Sustainability and ESG will drive innovation.
  - Human-AI collaboration will be essential.
  - Continuous learning will be a career imperative.

YOU ARE NOW EQUIPPED TO LEAD THE FUTURE OF FINANCIAL DATA ANALYTICS!
""")

print("="*70)
print("END OF LESSON 6 – MODULE 7")
print("="*70)
print("END OF MODULE 7")
print("END OF THE DIPLOMA PROGRAM")
print("="*70)