753: Blend Any Programming Languages in Your ML Workflows — with Dr. Greg Michaelson

Organizational Challenges

Organizational challenges in AI implementation often stem from bureaucratic hurdles and the complexity of integrating new models into existing systems. Greg Michaelson shares an example of a bank that discovered a glitch in the foreign exchange market, which could have generated significant revenue, but faced years of bureaucratic delays in implementation 1. He emphasizes the need for business buy-in and robust infrastructure to maintain models in production, noting that many organizations struggle with these aspects 2.

The implementation is also a hard part around the success of these projects.

--- Greg Michaelson

Jon Krohn adds that even large companies often have only a handful of models in production, highlighting the ongoing demand for skilled data scientists to manage these challenges 2.

   

Business-Driven AI

Aligning AI projects with business goals is crucial for success, yet many projects fail due to misalignment between technical and business teams. Greg Michaelson argues that technical teams must deeply understand the business's idiosyncrasies to create valuable solutions 3. He highlights the importance of framing AI projects correctly to ensure they address genuine business needs 4.

The biggest way to get value is to make sure that your technical people know as much about the business as possible.

--- Greg Michaelson

Jon Krohn notes that many AI projects fail because they are not solving the right problems, underscoring the need for clear communication between data science and business teams 4.