Evaluate the existing data infrastructure, sources, and quality.
Assess the company’s current data management, data governance practices, and data culture.
Identify gaps in data collection, storage, processing, and usage.
1.2 AI & Analytics Capability Assessment
Review the current state of AI, machine learning, and analytics capabilities within the organization.
Identify existing AI applications and their performance and utility.
Evaluate the skills, tools, and processes related to AI and analytics in the organization.
1.3 AI & Data Maturity Assessment
Assess the organization’s maturity in terms of AI and data utilization across different business units.
Understand the organization’s readiness for advanced AI and data-driven initiatives.
Refer to the AI Maturity Matrix: Awareness, Experimental, Operational, Systematic, Optimized. Determine the organization’s current stage and identify steps needed to reach the next maturity level.
2. Define AI & Data Goals
2.1 Identify Opportunities
Identify business areas where AI and data can add value, such as customer insights, predictive maintenance, risk management, etc.
Consider the potential for AI and data to drive innovation, efficiency, and competitive advantage.
2.2 Set Objectives
Set specific, measurable, achievable, relevant, and time-bound (SMART) objectives for AI and data initiatives.
Ensure objectives align with the overall business and digital strategy.
3. Develop AI & Data Strategy
3.1 Data Management & Governance
Establish robust data management and governance practices to ensure data quality, security, privacy, and compliance.
Identify required data infrastructure improvements and plan for their implementation.
3.2 AI Initiative Planning
Identify specific AI projects or initiatives to be implemented.
Plan for required resources, including data, technology, skills, and partnerships.
3.3 Ethics & Responsibility
Consider the ethical implications of AI and establish guidelines for responsible AI use.
Plan for transparency, fairness, privacy, and accountability in AI applications.
4. Implement AI & Data Strategy
4.1 Build or Acquire Capabilities
Develop in-house AI and data capabilities or partner with external providers, as required.
Invest in skill development, tools, and technology for AI and data initiatives.
4.2 Execute AI Projects
Implement AI projects, starting with pilot projects to test feasibility and impact.
Ensure rigorous project management and governance for AI initiatives.
5. Monitor & Improve
5.1 Measure Impact
Monitor the performance and impact of AI initiatives using defined KPIs.
Evaluate the ROI of AI projects and initiatives.
5.2 Learn & Improve
Use lessons learned from AI projects to improve future initiatives.
Foster a culture of continuous learning and improvement in AI and data practices.
6. AI Maturity Matrix
6.1 Awareness
Basic understanding of AI and its potential value. No AI initiatives in place.
6.2 Experimental
Some AI initiatives in pilot stage. Limited integration of AI in processes.
6.3 Operational
AI initiatives are operational and integrated into some processes. Some measurable benefits.
6.4 Systematic
AI initiatives are widely integrated across processes and are delivering consistent measurable value. Data-driven decision-making culture.
6.5 Optimized
Advanced AI capabilities with strong data infrastructure. AI is central to business strategy and delivering significant value.