In today’s tech landscape, businesses increasingly depend on AI assistants for critical tasks like research and procurement, relying on their perceived stability. However, our recent findings challenge this assumption.
Key Insights:
- Controlled Tests: We analyzed 200 trials involving GPT, Gemini, and Claude.
- Inconsistency Revealed:
- 61% of the runs produced different answers.
- 48% demonstrated shifts in reasoning.
- 27% showed self-contradictions.
- 34% disagreed with other models.
This instability is structural, stemming from silent model updates and a focus on plausibility rather than reproducibility.
Implications for Leadership:
- Understand the financial and regulatory risks tied to AI model volatility.
- Consider a robust governance framework for prevention and remediation.
This analysis is essential for CFOs, CIOs, and board members navigating the AI landscape.
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