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Combining ethical oversight with technical expertise
Visualisation of the Framework
Each dimension is iterative and interconnected, forming a cycle:
Discovery → Feasibility Assessment → Solution Design → Implementation → Monitoring & Optimisation → Review & Continuous Improvement
Discovery
Vision Dimension: Focusing on uncovering client goals and vision for AI adoption
- Objective: Understand the client’s needs, goals, and context.
Activities:
Conduct stakeholder interviews.
Identify pain points and opportunities for AI solutions.
Assess current processes, data infrastructure, and organisational readiness.
Define business objectives and success metrics.
Output: A clear problem statement and alignment on desired outcomes.
Feasibility Assessment
Reality Dimension: Grounding ideas in data, resources, and constraints to assess what’s possible.
- Objective: Evaluate the practicality and ROI of potential AI solutions.
Activities:
Perform a data audit to assess availability, quality, and relevance.
Identify technical constraints and resource requirements.
Analyse regulatory, ethical, and compliance considerations.
Build a high-level cost-benefit analysis.
Output: Feasibility report with prioritised recommendations and potential roadblocks.
Solution Design
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Innovation Dimension: Creativity and technical ingenuity in crafting AI solutions.
- Objective: Develop a tailored AI strategy and solution architecture.
Activities:
Define use cases and key functionalities.
Select appropriate AI models and technologies.
Plan data integration, preprocessing, and model training workflows.
Design user interfaces and system interactions.
Develop a risk mitigation strategy for bias, errors, and operational issues.
Output: Detailed AI solution blueprint and project roadmap.
Implementation
Execution Dimension: Highlighting the process of transforming designs into actionable solutions.
- Objective: Build and deploy the AI solution.
Activities:
Assemble the implementation team and assign roles.
Develop the AI models, pipelines, and associated infrastructure.
Test the solution in sandbox environments.
Integrate with existing systems and workflows.
Provide user training and change management support.
Output: A functional, operational AI system in production.
Monitoring and Optiisation
Sustainability Dimension: Focusing on long-term performance, adaptability, and continuous improvement.
- Objective: Ensure the solution performs effectively in real-world conditions.
Activities:
Set up performance monitoring and logging systems.
Track KPIs and performance metrics against benchmarks.
Conduct regular audits for accuracy, fairness, and compliance.
Optimise the system through retraining, tuning, or scaling as needed.
Output: Insights and updates for sustained AI system performance.
Review & Continuous Improvement
Growth Dimension: Leveraging insights to scale, refine, and plan future innovations
- Objective: Evaluate success and plan for future enhancements.
Activities:
Conduct a comprehensive review of project outcomes against initial objectives.
Gather feedback from stakeholders and end-users.
Document lessons learned and best practices.
Explore opportunities for further AI adoption and innovation.
Formalize a long-term AI governance and improvement strategy.
Output: Post-implementation report with recommendations for next steps.
Through the dimensions, we examine the ethical implications of data, especially in the context of big data and sophisticated analytics. There are crucial ethical questions related to AI that need to be addressed: How can we guarantee that AI systems are transparent, explainable, and accountable? How can we safeguard an individual’s privacy and data and ensure that data is used appropriately in AI systems? √
Think More Profoundly,
Feel more deeply,
See more truly®
Discovery
Feasibility Assessment
Solution Design
Implementation
Monitoring & Optimization
Review & Continuous Improvement
