Why Choice Matters Most In AI

Shane Buckley is President and Chief Executive Officer of Gigamon, a leader in deep observability.

​For the past several years, the AI market has largely been framed as a race. Which model will win? Which provider will dominate? Which platform should enterprises bet on?

For CEOs making significant AI investments, the bigger question is what happens when those bets need to change. A model that delivers the best results today may become too expensive or no longer meet the organization’s requirements. AI models and capabilities are advancing at a pace that challenges traditional enterprise planning cycles, even as workloads and business requirements continue to change.

Just as organizations learned that no single cloud environment could satisfy every need, they are finding that no single AI model is best suited to every workload. Different workloads call for different combinations of model capability and deployment environment. Complex reasoning may justify a frontier model’s cost, while routine tasks may be better served by a smaller, specialized model. Data sensitivity and control requirements can also help determine where those models should run.

The result is a hybrid approach in which organizations select and combine models based on capability, cost, performance, security, governance and the requirements of each workload. This shift is already happening, with recent research reporting that 46% of enterprises are using open-weight models as they balance AI costs and flexibility, while continuing to rely on frontier models where capabilities justify the expense.

Build Flexibility Into The Architecture

The hybrid cloud era offers a useful lesson, as early cloud strategies often prioritized speed and scale, with governance and cost controls following later. AI is moving through a similar cycle, but at a faster pace. Enterprises cannot afford to wait to apply those lessons. ​