Practical Frameworks to Map AI Metrics to Business Impact (Illustrative Case Studies)

​To build products that offer AI capabilities requires product managers to think beyond the technology to bridge the gap between AI model evaluation metrics and business impact. They must answer "How do we know when our model is good enough to ship?"

​Product Managers need to thoroughly answer this question to be successful in their current jobs and in future job interviews. Illustrative case studies include predictive models and generative AI models.

​3 Takeaways:

​a) Attendees will leave the meeting equipped to justify AI model release criteria to stakeholders to help their organizations properly invest in AI engineering and meet customer expectations.

​b) Attendees will learn how to look beyond generic model completion criteria by evaluating the unique monetary costs of incorrect model predictions.

​c) Attendees will gain a five-step framework to work backwards from customer expectations to link gen AI model metrics to tangible business value.

​About the Speaker

​Craig Guarraci is a product leader with 30+ years of experience in the tech industry working at leading companies like Microsoft, Amazon, agile start-ups and his own company Tech PM Career Path, LLC. He has shipped a variety of products spanning compilers, data warehouses, B2C websites, cloud services, AI products and more. He has worked as a project, program, product manager, and technical program manager. He is mentoring start-up founders. He is the author of a new book available on Amazon titled "Beyond AI Models. Enduring AI Product Leadership Principles for a Changing Landscape". Finally, he still writes code in Python because he’s a geek!