Module 11: Getting Started

Learning Content

Introduction: Your AI Implementation Roadmap

You've learned what AI is, where it applies, and how to evaluate ROI. Now it's time for the practical question: "How do I actually get started?"

This module provides a step-by-step roadmap for Malta businesses launching their first AI project—from selecting use cases to measuring success.

Key Learning Objectives

• Learn proven frameworks for selecting your first AI project
• Understand the stages of AI implementation
• Identify the team, tools, and partners you'll need
• Navigate common pitfalls and challenges
• Create an actionable 90-day AI launch plan

Step 1: Select Your First Use Case

Ideal First Project Characteristics:

• Clear business value: Measurable impact on revenue or costs
• Data available: You already have the data needed
• Lower risk: Failure won't cause major business disruption
• Manageable scope: Can be completed in 3-6 months
• Executive sponsor: Leadership champion for the project
• User buy-in: End users are supportive, not resistant

Use Case Prioritization Matrix:

Score each potential use case on these dimensions (1-5 scale):

• Business Impact: How much value will this create?
• Technical Feasibility: Do we have data and capability?
• Speed to Value: How quickly can we see results?
• Strategic Fit: Does this align with business priorities?

Calculate Priority Score: (Impact + Speed) × Feasibility

Choose the highest-scoring option that has leadership support.

Common Strong First Projects:

• Customer service chatbot: Clear ROI, low risk, fast implementation
• Fraud detection: High impact, measurable savings, existing data
• Demand forecasting: Clear metrics, valuable across organization
• Lead scoring: Sales impact, easy to pilot, clear success metrics
• Document processing: Time savings, happy users, simple to measure

Projects to Avoid as First Initiatives:

• Mission-critical systems where failure is catastrophic
• Projects requiring perfect accuracy (99.9%+)
• Poorly defined problems without clear success metrics
• Projects that require massive organizational change
• Use cases where you lack necessary data

Step 2: Build Your AI Team

Essential Roles (Can Be Part-Time or External):

1. Executive Sponsor

• Senior leader who champions the project
• Provides budget, removes roadblocks, drives adoption
• Time commitment: 2-3 hours/month

2. Project Manager

• Coordinates workstreams, manages timeline, tracks milestones
• Can be internal staff (doesn't need AI expertise)
• Time commitment: 25-50% time

3. Business SME (Subject Matter Expert)

• Understands the business problem deeply
• Defines requirements, validates results
• Time commitment: 20-30% time

4. Data Engineer/Scientist

• Builds and trains AI models
• Often external consultant or vendor for first project
• Time commitment: 50-100% time

5. IT/Integration Specialist

• Connects AI to existing systems
• Can be internal IT staff
• Time commitment: 20-40% time

Build vs. Buy vs. Partner:

Build In-House (Hire Data Scientists):

• When: Multiple ongoing AI initiatives, proprietary competitive advantage
• Cost: €60,000-€120,000 per data scientist annually in Malta
• Timeline: 6-12 months to build capability

Buy SaaS Tools (Pre-Built AI):

• When: Common use cases (chatbots, email marketing, CRM)
• Cost: €100-€5,000/month depending on scale
• Timeline: Immediate to 3 months

Partner with AI Consultants/MAIA:

• When: Custom AI needed, lack internal expertise
• Cost: €20,000-€150,000 for first project
• Timeline: 3-6 months

Recommendation for Malta SMEs: Start with SaaS tools if they fit your use case. If custom AI is needed, partner for first project while building internal knowledge.

Step 3: The Implementation Roadmap

Phase 1: Discovery & Planning (Weeks 1-4)

• Week 1-2: Problem Definition
  • Document current state and pain points
  • Define success metrics (KPIs)
  • Estimate baseline performance
• Week 3-4: Data Assessment
  • Identify data sources
  • Assess data quality and completeness
  • Plan data collection if gaps exist

Phase 2: Development (Weeks 5-12)

• Week 5-6: Data Preparation
  • Clean and label data (30-40% of project time)
  • Create training and test datasets
  • Address data quality issues
• Week 7-10: Model Development
  • Build and train AI models
  • Test different approaches
  • Optimize for accuracy and speed
• Week 11-12: Integration
  • Connect AI to business systems
  • Build user interfaces
  • Test end-to-end workflows

Phase 3: Pilot & Validation (Weeks 13-20)

• Week 13-14: Internal Testing
  • Test with small user group
  • Identify bugs and issues
  • Gather initial feedback
• Week 15-20: Pilot Deployment
  • Launch to limited user group or geography
  • Monitor performance closely
  • Compare results to baseline
  • Iterate based on feedback

Phase 4: Scale & Optimize (Weeks 21-26+)

• Week 21-23: Full Rollout
  • Expand to all users/markets
  • Train staff on AI tool usage
  • Communicate success stories
• Week 24-26: Optimization
  • Fine-tune model performance
  • Address edge cases
  • Document lessons learned
• Ongoing: Monitoring & Maintenance
  • Track KPIs and business impact
  • Retrain models as needed
  • Plan next AI initiatives

Common Pitfalls to Avoid

• Scope creep: Adding features mid-project—finish v1 first, then enhance
• Perfectionism: Waiting for 99% accuracy when 85% delivers business value
• Ignoring change management: Technical success but user rejection
• Insufficient testing: Rushing to production without validation
• No baseline: Can't measure improvement without knowing starting point
• Poor data quality: Garbage in, garbage out—clean data first
• Lack of monitoring: Deploy and forget—AI needs ongoing attention

Your 90-Day Action Plan

Days 1-7: Foundation

• Complete this course
• Identify 3-5 potential AI use cases
• Score them using prioritization matrix
• Select your first project

Days 8-30: Planning

• Define clear success metrics
• Assess data availability
• Build business case and ROI projection
• Secure executive sponsorship and budget
• Decide build/buy/partner approach

Days 31-60: Setup

• Assemble project team
• Kick off project with vendor/consultant if partnering
• Begin data preparation
• Set up project tracking and governance

Days 61-90: Execution

• Develop AI solution
• Integrate with existing systems
• Begin internal testing
• Plan pilot deployment

MAIA as Your AI Partner

MAIA offers unique advantages for Malta businesses getting started with AI:

• Malta focus: Understands local business context, regulations, and industries
• Faster time-to-value: Neurosymbolic approach requires less data and training time
• Explainability: Decisions are transparent—critical for trust and compliance
• Ongoing support: Not just deployment—continued optimization and support
• Cost-effective: More efficient than massive LLMs, lower operational costs

Key Takeaways

• Start with one focused, high-ROI use case—prove value before scaling
• Assemble a cross-functional team with executive sponsorship
• Follow a phased approach: plan, develop, pilot, scale
• Define clear success metrics before starting
• For first projects, partner with experts rather than building from scratch
• Avoid perfectionism—85% accuracy that delivers value beats 99% that takes forever
• Change management is as important as technical execution
• MAIA provides Malta-focused expertise to accelerate your AI journey