Enterprise leaders are shifting their focus from token pricing to determining when dedicated hardware becomes cheaper than paying per request.
The shift from pilot to production
Organisations are moving past isolated experiments. They are now deploying assistants, retrieval systems, and agentic applications that run continuously. These tools handle multi-step workflows across IT, research, and customer service, creating a steady stream of demand.
Data from Deloitte’s 2026 State of AI in the Enterprise supports this trend. Worker access to AI rose 5% in 2025. The proportion of companies with at least 40% of their AI projects in production is expected to double within six months.
When AI becomes a portfolio of always-on workloads, the business model changes. Consumption pricing offers flexibility, but it does not suit large, predictable usage. Leaders must decide if buying capacity in advance makes more economic sense than paying for every single request.
How much you run determines the cost
Ownership is not automatically cheaper. It only works when a company can keep that capacity productive.
Every organisation has a crossover point. This is the level of sustained use where owning infrastructure beats paying per request. There is no universal figure for this. It depends on the models used, the ratio of input to output tokens, performance needs, energy costs, and the operating model required.
A retrieval-heavy knowledge system costs differently from a simple assistant. Agentic workflows differ again. A single business task might involve repeated reasoning, retrieval, model calls, and tool use. Because of this, generic benchmarks are insufficient. Enterprises must model their actual workloads and size capacity accordingly.
At the right utilisation level, the benefit is lower cost and greater predictability. Companies can manage AI as a strategic infrastructure investment rather than watching a monthly spend line fluctuate.
Capacity creates value only when used
Buying the hardware is only half the equation. Even when the economics support ownership, capacity creates value only when the business gets workloads into production quickly.
This requires more than installing servers. It demands an operating model that connects technology to adoption. Teams must bring users on board, govern how AI is used, review utilisation, and continually identify the next high-value use case.
The goal is to create value early, then build on it. This means measuring use, finding underutilised capacity, and adding high-value workloads to the platform over time. Without that discipline, the business may never realise the economic value that justified the investment. With it, AI capacity becomes an asset the business can optimise and expand.
Three questions to ask
Before committing capital, leaders should consider three questions:
- Is demand becoming steady, predictable, and large enough to justify dedicated capacity?
- At what level of usage does ownership make economic sense?
- Can the company keep that capacity productive through adoption, governance, and continued use-case expansion?
Organisations that create the most value will look beyond token prices and the latest model. They will know when recurring demand calls for a different economic model. They will also have the operating discipline to make that capacity productive.
That is when AI stops being an expense and becomes an asset.
This content was produced by HPE. It was not written by MIT Technology Review’s editorial staff.
