AI ‘tokenomics’ and the cost of assumptions

AI Technology:TechWyse

With AI, the latest metric attracting attention is credit usage. Leaders want to know how many credits are being consumed, how usage can be predicted and whether the investment is delivering value. High consumption does not necessarily mean high value.

If there is one lesson business leaders should take from the fast-moving world of AI, it is this: success will not be determined by who uses the most AI, but by who makes the best decisions about how, when and where to use it.

Borrowed from the world of cryptocurrency, ‘tokenomics’ is about how a finite digital resource is designed, distributed and used to create value. The same principle gives business leaders a useful way to think about AI credits.

Whether your organisation measures usage in credits, tokens or licences, these resources have a cost. And the question is not simply how many you consume, but what you achieve as a result. Too many AI discussions focus on efficiency in isolation. Can we automate this? Can we generate that? These are valid questions, but they often miss the more important one: how do we use AI most effectively to complete the right task?

Good leaders have always understood that resources are limited. Budget is limited. Time is limited. Attention is limited. AI simply adds another resource that must be managed wisely.

Consider two organisations. One consumes large numbers of AI credits creating content, refining wording and experimenting with endless prompts. The other uses fewer credits by defining the outcome, planning the input, prompting carefully and applying AI to recurring problems to remove operational friction. Which creates more value?

Technology projects rarely fail because the technology itself was incapable. More often, they fail because organisations make assumptions. They assume the tool will solve the problem. They assume staff know how to use it well. They assume the data is ready. They assume governance can be added later.

At TechWyse, we see strong similarities between AI adoption and the way organisations have approached cyber security, cloud migration and digital transformation. The businesses that achieve the best results are rarely those that rush in first; they are the ones that ask better questions.

• What problem are we trying to solve?
• How will we define the goal and provide good input with the prompt?
• How will we measure success?
• What risks are we introducing?

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