Glossary

Return on AI (ROAI)

What is Return on AI (ROAI)?

Return on AI describes the economic return of an AI deployment relative to its costs. A simple rough calculation is: hours saved per month × hourly rate, minus the costs of licences, model usage, set-up and support. If a team handling 200 requests needs 4 instead of 10 minutes per request, it saves 20 hours a month. Such calculations remain estimates; quality, error rates and onboarding time should also be taken into account.

How KI.ordo helps

The KI.ordo page “Usage and value” shows tokens and costs per model; the template “Calculate the benefit of AI (ROAI)” sets these costs against an estimated time saving.

Learn more →: Return on AI (ROAI)

Topics

  • cost-effectiveness
  • cost-benefit
  • AI strategy

Try KI.ordo

Agents, flows and templates in one workspace – free 24-hour demo.

Start demo
← All terms

Related terms

LLM Costs

The costs of large language models are usually billed per token, i.e. text units roughly the size of a syllable to a word; input and output tokens are priced separately, with output generally more expensive. Larger models cost more per token than small, fast models such as Claude Haiku, which are often sufficient for simple tasks. Prompt caching lowers costs when the same long context is sent repeatedly. Concise instructions and carefully selected knowledge help as well.

  • Costs
  • Tokens
  • +1

AI Adoption Rate

The AI adoption rate indicates what share of companies, employees or households use AI and serves as a measure of how widely the technology has spread. According to Germany's Federal Statistical Office (Destatis), 20% of German companies with ten or more employees used AI in 2024 (2023: 12%). The digital association Bitkom found a share of 36% in 2025 among companies with 20 or more employees. Differing definitions and samples mean studies are only partly comparable.

  • Statistics
  • Uptake
  • +1

AI-Driven Economic Impact

The term covers the economic consequences of using artificial intelligence: productivity gains, new products and business models, changing tasks and skill requirements, and shifts in competition. The scale and distribution of these effects are disputed and depend heavily on sector, investment and training. For individual organisations, what counts most is the measurable benefit of specific applications. Example: shorter handling times in customer service reduce the cost per enquiry.

  • Economy
  • Productivity
  • +1

Token

A token is the smallest unit of text a language model processes, usually part of a word, a short word or a punctuation mark. Models read inputs and generate answers token by token, and the size of the context window is also measured in tokens. Because providers charge for input and output tokens, tokens directly determine costs and limits: long instructions and extensive background knowledge increase consumption.

  • LLM
  • Costs
  • +1