https://www.linkedin.com/feed/update/urn:li:activity:7340930410727526402

When enterprise leaders drive AI transformation, the most common challenge is measuring AI’s impact on business outcomes.

The solution? Design a rigorous, structured evaluation framework tailored to your organization’s specific use cases to assess AI’s performance and reliability in targeted tasks.

This approach delivers three key benefits:

1. It helps you select tools that truly fit your needs, not just the most hyped models.
2. It quickly highlights tasks where AI can deliver the greatest value.
3. It identifies processes requiring human intervention, enabling clear human-AI collaboration workflows and targeted AI tool improvements.

When Morgan Stanley adopted AI, they prioritized evaluations, creating precise metrics for each use case—such as AI accuracy in language translation or summary quality compared to senior advisors’ outputs. They iteratively refined AI tools, task designs, and processes until proven value was achieved before full deployment.

Their results? 98% of advisors now use AI tools daily, with data retrieval efficiency soaring from 20% to 80%. This allows human advisors to focus on what matters most: client relationships.

AI is novel and fast-evolving, often lacking precedents. By defining clear evaluation metrics for specific scenarios, enterprises can empower teams to implement AI with evidence-based confidence, maximizing business value.

David Kwan

AI Literacy Consultant, ZTOA Consulting Group

https://ai.ztoagroup.com 

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