Despite AI’s potential, many strategies fail and technical debt is a key contributor.
Gartner reports that 85% of AI projects deliver poor outcomes due to data issues, while McKinsey finds 70% of AI transformations miss their goals due to poor strategic focus. Forrester highlights poor integration into operations as a key challenge.
These insights show why thorough preparation and expert guidance are crucial for AI success.
Here’s an eight-step guide to ensure AI readiness and avoid common pitfalls:
1. Align with Business Objectives
Identify your organisation’s goals. AI should directly support these, whether improving efficiency, enhancing customer service, or driving innovation. Clear objectives ensure focused and valuable AI efforts.
2. Review Data Infrastructure
AI needs strong data management, storage, and processing capabilities. Ensure your infrastructure can handle AI demands, and upgrade where necessary.
3. Focus on Data Quality
AI thrives on accurate, consistent, and complete data. Prioritise data cleansing and make information accessible across departments. Breaking down data silos is vital for decision-making and AI success.
4. Establish Data Governance
Create robust data governance policies to manage data usage, security, and privacy, ensuring compliance with laws like GDPR. This safeguards data integrity and mitigates risks like bias or misuse.
5. Create and Review AI Policy and Principles
Establish both AI Policies and Principles:
- AI Policy is a formal framework outlining legal, ethical, and operational rules for AI use, ensuring consistency and compliance.
- AI Principles are ethical guidelines promoting transparency, fairness, and accountability in AI decisions.
The key difference: Policies are enforceable rules, while Principles guide values. Together, they ensure AI is used ethically and legally.
6. Engage Expert Support
AI success requires technical and strategic expertise. A practical AI expert can guide your organisation, helping avoid common mistakes and streamline efforts.
7. Start with Pilot Projects
Test AI on a small scale. Pilot projects refine your approach, demonstrate value, and build stakeholder confidence before broader rollouts.
8. Adapt and Evolve
AI is a continuous journey. Regularly review and update your AI systems to align with changing business needs. Foster a learning culture to stay at the forefront of AI advancements.
By following these eight steps, organisations can successfully implement AI and address technical debt by combining quality data and governance.
Want to learn more? Feel free to make contact.
And in case you missed it, here's a link to last week’s very popular blog on Technical debt.
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