In her Forbes article, Rogayeh Tabrizi, CEO of Theory+Practice, explores how large language models (LLMs) and AI—when implemented through a systems thinking lens—can drive enterprise-wide transformation and measurable results.
Rather than applying AI in isolated teams or one-off use cases, Rogayeh advocates for a holistic approach—one that considers how each business function, model, and decision is interconnected. From enhancing demand forecasting and customer engagement to improving the interpretability of predictive models, this approach allows businesses to move from data to insight—and from insight to action.
She also cautions that misusing LLMs can increase risk, particularly when biases and poor assumptions go unchecked. Expert guidance, Bayesian thinking, and a clear organizational context are all essential to avoid pitfalls and maximize impact.
Key Insights include:
- Why systems thinking is critical to effective AI and LLM deployment
- How LLMs can accelerate insight generation, narrative storytelling, and real-time personalization
- Where traditional AI projects fall short—and how to avoid repeating those mistakes
- How Bayesian thinking and human oversight improve AI reliability and adoption
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