Enterprise Responsible AI Adoption – A Holistic AI Perspective

Enterprise Trade-off: Enterprises can use multiple open-source models to achieve around 90% accuracy, compared to using the latest OpenAI model and achieving 95% accuracy with a single model. Open-source models also require additional training and Reinforcement Learning from Human Feedback (RLHF). The trade-off between achieving 60% accuracy with open-source versus 90% with proprietary models needs careful evaluation.

• Model and Data Alignment: Failing to invest time in understanding the models, aligning them with the right data, and establishing proper benchmarks will lead to a random, fragile implementation. A "lift-and-shift" approach to building AI products is not a sustainable strategy.
• Data and Model Understanding: If you don’t fully understand the data sources and the limitations of the models you're using, don’t assume that handling only the happy path scenarios is enough to deliver successful GenAI applications.
• Responsible AI Adoption: Relying on open-source models that deliver subpar accuracy does not constitute responsible AI adoption. It reflects a short-term vision and a failure to prioritize long-term sustainability.
• Open Source Paradox: There's a growing push to leverage open-source models and frameworks, but expectations for state-of-the-art accuracy remain unrealistically high.
• Long-term Costs: The broader impact and cost of fixing data issues or model errors are often overlooked in favor of flashy, short-term demo solutions that generate applause but don't provide lasting value.

Key Questions to Ask About the Model:

1. Data: Is the data representative, reliable, and aligned with the intended use case?
2. Domain: Does the model have domain-specific knowledge to perform effectively?
3. Benchmark: Have clear benchmarks and performance metrics been set and evaluated?
4. Key Questions to Ask About the Use Case:
5. Why do we need an LLM?: Is an LLM the best solution for this problem, or are there alternatives?
6. How much effort does it save?: What quantifiable efficiencies or cost savings does the LLM offer compared to traditional methods?
7. What is the plan to improve accuracy?: How will you progress from the current accuracy level, and what steps will be taken to continuously improve the model's performance?
8. Leadership Clarity: Leaders must understand that simply purchasing a platform or tool will not solve the underlying challenges of responsible AI adoption. A clear vision and strategy are critical for long-term success.