A scorecard for the AI age

The question I hear from CFOs everywhere is simple: how do we get more value from our AI spend?

For years, the market measured the success of software through adoption: seats purchased, users active, licenses renewed. Understanding the value of AI demands a more powerful measure: work accomplished.

The basic economic question facing CFOs and other business leaders is whether the value of the work AI completes grows faster than the cost of producing it.

Answering that question requires looking more deeply than a metric such as cost per token. A lower-cost model may have cheaper tokens, but getting great results may require more attempts, more time, or more human review. A more capable model may have more expensive tokens, but complete the same task in one pass. What matters is the full cost of producing a successful outcome, measured against the value that outcome creates.

The ultimate scorecard for the age of AI could be looked at as “Useful Intelligence per Dollar.” This metric answers four key questions:

1. Is AI completing work that matters?

2. What does each successful task cost?

3. Can people depend on the result?

4. Does each AI dollar produce more value as usage grows?

1. How much useful work gets done?

Start with the work itself.

How many customer issues did AI help resolve? How many code changes did it help ship? How many contracts did it review? How much time did it give back to people? How many decisions improved because the right context was available at the right moment?

Tokens create value when they transform into work people can use. As models become more capable, they can take on longer and more complex tasks: maintaining context, reasoning through multiple steps, working across tools, and adapting as they go.

The best place to begin is with one workflow. Define what “done” means and measure that outcome in the system where the work happens.

2. What does a successful task actually cost?

The next question is what it costs to complete that work well.