One of the most common mistakes in AI strategy is assuming that success comes mainly from model quality. In this The Deep View piece, Krazimo CEO Akhil Verghese explains why that view is incomplete. The companies that lead in AI are rarely the ones that simply have access to strong models. They are the ones with the right combination of product direction, organizational urgency, technical talent, data strategy, and execution discipline. Without those pieces in place, even the most well-resourced companies can struggle to turn AI into meaningful product progress.
That lesson matters well beyond Big Tech. For enterprise leaders, the article is a reminder that AI transformation depends on far more than plugging a model into an existing workflow. Businesses need clear use cases, well-defined ownership, access to the right data, internal alignment on priorities, and the engineering maturity to turn experiments into dependable systems. AI strategy is ultimately a question of execution: how quickly an organization can move, how well it integrates AI into real workflows, and whether it can build systems people actually trust and use.
This is especially relevant for companies evaluating enterprise AI strategy, AI product execution, AI architecture decisions, and how to create long-term business value from AI investments. The real moat is rarely just raw model access. It is the ability to operationalize AI effectively inside a real product or business environment. That is why the article is such a strong match for Krazimo’s positioning around reliable AI systems, thoughtful deployment, and real-world business outcomes.
Read the full article on deepview.