Reducing the High Failure Rate (50%) of RPA Implementation Projects: A Real-World Application Using Design Science Research
Bibliographic record
Abstract
When Cooperative Inc. faced challenges sourcing experienced CPAs to meet an increasing demand for internal controls professionals, we turned to emerging technologies—specifically Robotic Process Automation (RPA). Recognizing the high failure rates often associated with RPA initiatives, I developed, tested (Eligibility, Calculation of ROI, & Design), and validated an RPA implementation framework known as the Virtuous RPA Circle Framework, through design science research (DSR) and practitioner surveys. This framework was tailored for SOX compliance and its Canadian equivalent (52-109). Leveraging DSR allowed us to rigorously assess the automation potential of 500 internal controls, each traditionally requiring three hours to test manually, but reducible to under a minute per control with RPA—yielding an estimated annual savings of 2,000 hours, or slightly more than one full-time equivalent (FTE). The agile and iterative features of the Virtuous RPA Circle Framework promote successful deployment and sustainable maintenance through robust governance structures. Although conceived and evaluated within a SOX/52-109 context, survey participants expressed considerable interest in applying the framework to broader use cases, such as business process optimization, underscoring its versatility and potential for broader organizational impact.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.100 | 0.158 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".