For the last two years, we've spent an extraordinary amount of time debating models.
GPT or Claude? Open-source or closed-source? Who has the biggest context window? Who tops the benchmarks?
Those conversations still matter, but they're no longer where competitive advantage gets created.
After working on dozens of enterprise AI implementations, one thing has become clear to me. Very few AI projects fail because the model isn't good enough. They fail because businesses are far more complex than the technology itself.
→ Data is scattered across systems that were never built to talk to each other. → Processes have evolved over decades and don't match how a clean AI workflow expects things to work. → People, understandably, don't like changing how they've always worked.
Intelligence is no longer the hardest part. The implementation is.
That is why I think we are entering a different phase of AI adoption. The winners will not be the companies building the best models. They'll be the companies exceptionally good at deploying AI inside real businesses.
That is exactly where the leading AI companies are investing right now, building deployment capabilities instead of just bigger research labs.
At Indus Net Technologies (INT.), we've arrived at the same conclusion. This month, we started training our first batch of FDEs, because we need people who understand businesses, not just model architecture.
People who can spot where AI creates measurable value, redesign workflows instead of bolting on another tool, and drive adoption long after the pilot ends.
It reminds me of the ERP wave. Buying SAP never transformed a business. Implementing it well did. AI is entering the same phase.
Access to powerful models will keep getting cheaper and more commoditised. The real edge will belong to whoever knows how to convert that capability into an actual business outcome.
The question isn't which AI model to use anymore. It is: Who in your organisation can actually make it work.