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AI Model Streamlines Time-Consuming TBM Cost Category Classification Mapping
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The implementation of Technology Business Management (TBM) necessitates a significant amount of manual labor, as TBM subject matter experts must review the cost category descriptions for an organization and accurately map each with the appropriate TBM cost category. This resource-intensive process is crucial to the success of TBM implementation, serving as a fundamental basis for precise financial reporting. A typical TBM project may demand 160 hours or more to finalize the cost category mapping process, which plays a vital role in ensuring accurate financial reporting.


In an effort to decrease the labor needed for TBM cost category mapping, REI developed a predictive model utilizing AI techniques to comprehend TBM cost category descriptions and associate them with agency-specific code descriptions. This model effectively predicts the TBM cost category that can be assigned to the corresponding agency-specific code, streamlining the process and saving valuable time and resources.


By employing automation, recommendations on TBM category assignment can be provided, which significantly reduces the time needed for TBM subject matter experts to manually review descriptions. This approach not only increases the efficiency of overall processing but also allows experts to focus on more complex tasks within the project.

Capabilities Shown

  • Artificial Intelligence
  • Natural Language Processing
  • Predictive Modeling