The AI honeymoon is over. Not because AI failed, but because the hype did.
98% of enterprises have deployed generative AI, yet only 5% have reached million-dollar impact. The gap between pilot and production is not a technology problem. It is a specialization problem.
General-purpose AI was never built for your world. It was built for everyone's world, which means it is perfectly adequate for no one's. Boards want ROI. Legal wants compliance. Operations wants consistency. Most general AI deployments cannot deliver all three at the same time.
The right AI, not more AI
The answer is not more AI or more agents. It is the right AI and the right agents. Large language models are genuinely powerful, but left unbounded, they drift. To deliver real value in a real business, they need to be constrained on purpose:
- Accuracy: trained to minimize hallucinations and aligned to your use case, so your team can trust what it tells them.
- Consistency: every user gets the same quality of experience in every engagement, not just when the model is having a good day.
- Privacy: your data and your clients' data treated with the security and compliance they deserve, not fed into a model that belongs to someone else.
The companies winning with AI right now are not the ones with the biggest models. They are the ones with the most bounded ones.
Adapted from LinkedIn post on April 16, 2026.