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What works and AI success factors?

Key Success Factors

Coding is a tiny part. Five days or 100 lines of code in Python can predict malignancy in cancer tumours. Just 250 lines went into AlphaGo

Problem-Focused Approach: Successful AI implementation starts with identifying specific, tangible business problems that AI is well-suited to solve.

"80% of AI projects stalled. And 96% had problems with data quality." (Dimensional Research)

Data Quality and Representation Model: Investing in data cleaning, integration, and creating a robust representation model is crucial. This provides a strong foundation for AI to generate reliable insights.

'Many large companies misrepresent data science roles and want more people for data or business intelligence areas' (Spackman at Huxley Associates)

Collaboration: AI projects thrive when business users, data scientists, and machine learning engineers work together. Business users provide context and domain knowledge, while engineers and scientists bring technical expertise.

Less than 5% apply AI to core areas of the business. And less than 8% can get it to scale and deliver apparent benefits enterprise-wide.

Focus on Analytics Translation: Rather than emphasizing coding alone, organizations must have strong analytics translators who bridge the gap between technical models and actionable business solutions.

People's skills and knowledge are 2.5 times greater than the value of all assets combined including offices, plants, and machinery

Building Trust: Prioritize trust in the AI models and their outputs by ensuring transparency, explainability, and demonstrating the value generated.

Boldness and Experimentation: Embracing a culture of innovation and willingness to take calculated risks is essential to pushing the boundaries of AI applications.

Companies that are bold and adventurous and look to solve real problems with tangible delivery of successes are best placed to see AI deliver. The promises of AI to human resources are many, but putting it to work is a complex undertaking.


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