When it comes to AI, bigger isn’t always better

A broader issue is the dominance of generative AI in public discourse, which has somewhat overshadowed decades of valuable non-generative tools. As teams improve at tackling real enterprise-scale data problems, we’re likely to see a shift toward a more balanced, pragmatic toolbox—one that blends statistical models, optimization techniques, structured data, and specialized LLMs or SLMs, depending on the task.

In many ways, we’ve been here before. It all echoes the “feature engineering” era of machine learning when success didn’t come from a single breakthrough, but from carefully crafting workflows, tuning components, and picking the right technique for each challenge. It wasn’t glamorous, but it worked. And that’s where I believe we’re heading again: toward a more mature, layered approach to AI. Ideally, one with less hype, more integration, and a renewed focus on combining what works to solve real business problems, and without getting too caught up in the trend lines.

After all, success doesn’t come from a single model. Just as you wouldn’t run a bank on a database alone, you can’t build enterprise AI on raw intelligence in isolation. You need an orchestration layer: search, retrieval, validation, routing, reasoning, and more.