People sometimes ask me why I still talk about machine learning. "Isn't ML dead? Didn't genAI kill it off?"

No. Not at all!

Generative AI may dominate the headlines, but ML/AI is alive and well. It's still in operation in plenty of companies, handling tasks like document classification, content moderation, and fraud detection.

Compared to genAI, an in-house ML/AI model …

▶️ ... is more transparent. (You know what training data went into the model, because you built it.)

▶️ ... offers you more control. (You decide when the model gets a new update, and when it gets retired from service. No surprise background tweaks that wreck your product!)

▶️ ... doesn't create a data privacy nightmare. (A third-party provider can't train on your queries, because they don't see them.)

▶️ ... is immune to drama in the genAI space. (Executive in-fighting? Risk of loan default? Datacenters not getting built? Your model – therefore, your business – keeps running.)

▶️ ... sometimes exhibits better performance on similar tasks. (Because it was trained on your data, specific to your business.)

All in all: as you plan your "AI" transformation, remember to account for both ML/AI and genAI. Both are useful to a business, in different ways and for different reasons.