A Leader’s Guide To Value Max Enterprise AI

Ameya Kanitkar is the Co-founder and CTO of Larridin, a Bay Area-based startup building an organizational platform powered by AI.

​As enterprise AI enters a new phase, the focus on accessing the newest model, the largest context window or the strongest benchmark performance is now giving way to maximizing value.

As AI spending grows with its capacity and the variety of models available, leaders are asking a more practical question: What is the most cost-effective combination of models, infrastructure and human expertise that can reliably produce the business outcome we need?

In truth, enterprises are spending substantially more on AI while expecting to extract more output value from fewer people. Worldwide end-user spending on AI models and platforms is projected to increase 63% in 2026 compared to 2025. At the same time, Gartner predicts 60% of organizations will adopt smaller software engineering teams by 2029.

That growth makes AI spending harder to justify at the board level, pushing enterprise leaders to rethink which models can best perform at each task. Increasingly, the goal is to build a portfolio that matches capability and cost to the value of the work.

This correlates to workforce planning. Companies do not need a senior professional to copy information between spreadsheets. Senior expertise is responsible for solving problems that justify the expense and lower-cost resources are typically assigned work that does not.

AI should be managed the same way as enterprise expertise.

The Model Vs. The Value Of The Job

Every leading AI model has different strengths. Some perform better at complex reasoning and planning. Others may be more economical for coding, research, summarization, extraction or repetitive workflow tasks. Companies are increasingly aware of that; however, value-maxxing does not mean simply selecting the least expensive model.