# ===================================================================
# MODULE 9, LESSON 8: PREPARING FOR THE FUTURE
# ===================================================================

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

print("="*70)
print("PREPARING FOR THE FUTURE – STRATEGIC FORESIGHT AND ORGANISATIONAL AGILITY")
print("="*70)

# ----------------------------------------------------------------
# PART A: FUTURE READINESS ASSESSMENT
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART A: Future Readiness Assessment")
print("-"*60)

readiness_dimensions = {
    'Foresight Capability': {'Current Score': 3, 'Target Score': 5, 'Priority': 'High'},
    'Organisational Agility': {'Current Score': 3, 'Target Score': 5, 'Priority': 'High'},
    'Innovation Culture': {'Current Score': 3, 'Target Score': 5, 'Priority': 'High'},
    'Talent Readiness': {'Current Score': 3, 'Target Score': 5, 'Priority': 'High'},
    'Leadership Capability': {'Current Score': 3, 'Target Score': 5, 'Priority': 'High'},
    'Digital Maturity': {'Current Score': 3, 'Target Score': 5, 'Priority': 'High'},
    'Change Resilience': {'Current Score': 3, 'Target Score': 5, 'Priority': 'High'}
}

readiness_df = pd.DataFrame(readiness_dimensions).T
print("Future Readiness Assessment:")
print(readiness_df)

# Visualise
fig, ax = plt.subplots(figsize=(10, 6))
dimensions = list(readiness_df.index)
current = readiness_df['Current Score'].tolist()
target = readiness_df['Target Score'].tolist()

x = np.arange(len(dimensions))
width = 0.35

ax.barh(x - width/2, current, width, label='Current', color='blue', alpha=0.7)
ax.barh(x + width/2, target, width, label='Target', color='green', alpha=0.7)

ax.set_yticks(x)
ax.set_yticklabels(dimensions)
ax.set_xlabel('Maturity Score (1-5)')
ax.set_title('Future Readiness Assessment')
ax.legend()
ax.grid(True, alpha=0.3, axis='x')

plt.tight_layout()
plt.savefig('future_readiness.png', dpi=300, bbox_inches='tight')
plt.show()
print("Future readiness visualisation saved as 'future_readiness.png'")

# ----------------------------------------------------------------
# PART B: STRATEGIC FORESIGHT PROCESS
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART B: Strategic Foresight Process")
print("-"*60)

foresight_process = pd.DataFrame({
    'Stage': ['Scan', 'Analyse', 'Envision', 'Plan', 'Act'],
    'Description': [
        'Identify signals of change',
        'Analyse implications',
        'Envision possible futures',
        'Develop strategic responses',
        'Execute and monitor'
    ],
    'Activities': [
        'Environmental scanning, trend analysis',
        'Impact analysis, scenario planning',
        'Scenario development, visioning',
        'Strategy development, roadmap',
        'Implementation, monitoring'
    ],
    'Status': ['🟢', '🟡', '🟡', '🟡', '🟡']
})

print("Strategic Foresight Process:")
print(foresight_process.to_string(index=False))

# ----------------------------------------------------------------
# PART C: ORGANISATIONAL AGILITY ASSESSMENT
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART C: Organisational Agility Assessment")
print("-"*60)

agility_dimensions = {
    'Structure': {'Current Score': 3, 'Target Score': 5, 'Priority': 'High'},
    'Processes': {'Current Score': 3, 'Target Score': 5, 'Priority': 'High'},
    'Culture': {'Current Score': 3, 'Target Score': 5, 'Priority': 'High'},
    'Technology': {'Current Score': 3, 'Target Score': 5, 'Priority': 'High'},
    'Leadership': {'Current Score': 3, 'Target Score': 5, 'Priority': 'High'},
    'Talent': {'Current Score': 3, 'Target Score': 5, 'Priority': 'High'}
}

agility_df = pd.DataFrame(agility_dimensions).T
print("Organisational Agility Assessment:")
print(agility_df)

# Visualise
fig, ax = plt.subplots(figsize=(10, 6))
dimensions = list(agility_df.index)
current = agility_df['Current Score'].tolist()
target = agility_df['Target Score'].tolist()

x = np.arange(len(dimensions))
width = 0.35

ax.barh(x - width/2, current, width, label='Current', color='blue', alpha=0.7)
ax.barh(x + width/2, target, width, label='Target', color='green', alpha=0.7)

ax.set_yticks(x)
ax.set_yticklabels(dimensions)
ax.set_xlabel('Maturity Score (1-5)')
ax.set_title('Organisational Agility Assessment')
ax.legend()
ax.grid(True, alpha=0.3, axis='x')

plt.tight_layout()
plt.savefig('organisational_agility.png', dpi=300, bbox_inches='tight')
plt.show()
print("Organisational agility visualisation saved as 'organisational_agility.png'")

# ----------------------------------------------------------------
# PART D: FUTURE TALENT STRATEGY
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART D: Future Talent Strategy")
print("-"*60)

talent_strategy = pd.DataFrame({
    'Area': ['Skills', 'Recruitment', 'Development', 'Retention', 'Workforce'],
    'Focus': [
        'Build future skills',
        'Attract future talent',
        'Develop capabilities',
        'Retain high performers',
        'Flexible workforce'
    ],
    'Activities': [
        'AI, data, digital, leadership training',
        'Campus recruitment, diversity hiring',
        'Training, rotations, mentoring',
        'Career paths, engagement, incentives',
        'Remote, hybrid, gig workforce'
    ],
    'Priority': ['High', 'High', 'High', 'High', 'Medium']
})

print("Future Talent Strategy:")
print(talent_strategy.to_string(index=False))

# ----------------------------------------------------------------
# PART E: FUTURE READINESS METRICS
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART E: Future Readiness Metrics Dashboard")
print("-"*60)

future_metrics = pd.DataFrame({
    'Metric': [
        'Foresight Capability Score',
        'Organisational Agility Score',
        'Innovation Index',
        'Talent Readiness Score',
        'Leadership Capability Score',
        'Digital Maturity Score',
        'Change Resilience Score',
        'Future Vision Clarity'
    ],
    'Current Value': [
        '3.0/5',
        '3.2/5',
        '3.0/5',
        '3.0/5',
        '3.2/5',
        '3.0/5',
        '3.0/5',
        '3.0/5'
    ],
    'Target Value': [
        '> 4.5/5',
        '> 4.5/5',
        '> 4.5/5',
        '> 4.5/5',
        '> 4.5/5',
        '> 4.5/5',
        '> 4.5/5',
        '> 4.5/5'
    ],
    'Status': ['🟡', '🟡', '🟡', '🟡', '🟡', '🟡', '🟡', '🟡']
})

print("Future Readiness Metrics Dashboard:")
print(future_metrics.to_string(index=False))

# ----------------------------------------------------------------
# PART F: FUTURE READINESS ROADMAP
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART F: Future Readiness Roadmap")
print("-"*60)

roadmap = {
    "Phase 1 (0-12 months) – Foundation": {
        "Focus": "Build future readiness foundation.",
        "Activities": [
            "Establish strategic foresight capability.",
            "Build organisational agility.",
            "Develop future talent strategy.",
            "Enhance leadership capabilities."
        ],
        "Success Metrics": ["Foresight capability > 3.5", "Agility score > 3.5"]
    },
    "Phase 2 (12-24 months) – Scale": {
        "Focus": "Scale future readiness.",
        "Activities": [
            "Scale foresight across organisation.",
            "Implement agile processes.",
            "Build digital capabilities.",
            "Develop future-ready workforce."
        ],
        "Success Metrics": ["Foresight capability > 4.0", "Agility score > 4.0"]
    },
    "Phase 3 (24-36 months) – Advanced": {
        "Focus": "Advanced future readiness.",
        "Activities": [
            "Achieve future-ready maturity.",
            "Lead in innovation and agility.",
            "Build global capabilities.",
            "Achieve industry leadership."
        ],
        "Success Metrics": ["Foresight capability > 4.5", "Agility score > 4.5"]
    },
    "Phase 4 (36+ months) – Leadership": {
        "Focus": "Industry-leading future readiness.",
        "Activities": [
            "Lead industry future readiness.",
            "Build global future-ready organisation.",
            "Achieve leadership.",
            "Continuous improvement."
        ],
        "Success Metrics": ["Industry-leading future readiness", "Continuous innovation"]
    }
}

for phase, details in roadmap.items():
    print(f"\n{phase}:")
    print(f"  Focus: {details['Focus']}")
    print("  Activities:")
    for activity in details['Activities']:
        print(f"    • {activity}")
    print("  Success Metrics:")
    for metric in details['Success Metrics']:
        print(f"    • {metric}")

# ----------------------------------------------------------------
# PART G: FINAL SUMMARY AND REFLECTION
# ----------------------------------------------------------------

print("\n" + "="*70)
print("PART G: Final Summary and Reflection")
print("="*70)

print("""
Preparing for the Future – Key Takeaways:

1. Strategic foresight is essential for anticipating and preparing for the future.
2. Organisational agility enables rapid adaptation to change.
3. Future-ready talent and leadership are critical for success.
4. Key dimensions: structure, processes, culture, technology, leadership, talent.
5. Key metrics: foresight capability, agility score, innovation index, talent readiness.
6. Roadmap: foundation → scale → advanced → leadership.

Final Recommendations:
  - Establish strategic foresight capability.
  - Build organisational agility.
  - Develop future-ready talent and leadership.
  - Embrace innovation and experimentation.
  - Continuously monitor and adapt.
  - Lead with purpose and integrity.

REFLECTION QUESTIONS:
  1. What are the most important trends shaping the future of banking?
  2. What is your organisation's level of future readiness?
  3. What are the biggest barriers to future readiness?
  4. What actions will you take to prepare for the future?
  5. What is your personal vision for the future of digital banking?

Congratulations on completing the Diploma in Digital Banking Technology!
""")

print("="*70)
print("END OF LESSON 8 – MODULE 9")
print("="*70)
print("END OF MODULE 9")
print("="*70)
print("END OF THE DIPLOMA IN DIGITAL BANKING TECHNOLOGY")
print("="*70)