The main reason for Corporate GenAI Failure: Measurement Issue

Frontier language models are now able to produce professional Deliverable and expert assessments confirm that these outputs are equivalent to human work in the task with many economic values. Yet, most of the corporate GenAI initiatives have not been able to show measurable work effect, and it is expected to cancel an important part of non-surgical projects. Based on a new research published in ArXiv, the main reason for this contradiction is due to the problem of a**imeter.

The problem arises at:

• The current public benchmarks respond to the question “What model can do” but not answer the question “What value does this work” for the actual corporate deployment decision
• Only model performance metrics are used to evaluate the success of GenAI projects, missing measuring frames based on work results and ROI
• There is a critical gap between creating the original economic value with producing professional quality output

This research released with EnterpriseVal header in ArXiv emphasizes that it is necessary to paradigize a new assessment to measure the real impact of GenAI in the enterprise environment. While Frontier models are undisputed for technical competence, it doesn’t matter whether these capabilities are integrated into corporate workflows, concrete money savings, efficiency rise or income increase.

The research suggests that businesses should develop** bonded measurement methodology to perform expected return from GenAI investments. Thus, from the selection of models to the distribution strategy, it is possible to make decisions based on data and reduce failure rates.