A billion dollars idea: Learning Workflows and Item Response Theory

Quality control is a vital aspect of many business processes, from financial transactions to procurement. In larger organizations, however, the need for multiple reviewers can lead to time-consuming bureaucracy and a drain on resources. But what if we could revolutionize these processes using learning workflows and item response theory?

An example: One common process that requires multiple approvals for quality control is the expense reimbursement process within an organization. Employees often incur various expenses while performing their job duties, such as travel, meals, and office supplies. To ensure accurate and fair reimbursement, the organization typically implements a multi-step approval process.

1. Expense submission: The employee gathers all necessary receipts and documentation and submits an expense report, detailing the nature and amount of each expenditure.
2. Managerial approval: The employee's direct manager reviews the submitted expense report to verify that the expenses are legitimate, reasonable, and in line with company policy. The manager may ask for additional information or clarification if needed before approving the report.
3. Finance department review: After managerial approval, the expense report is sent to the finance department. The finance team checks the report for compliance with company policies, budgetary constraints, and tax regulations. They also ensure the accuracy of the expense categorization and proper documentation.
4. Final approval and reimbursement: Upon the finance department's approval, the expense report is processed for reimbursement. The employee receives the approved reimbursement amount.

In this example, the multi-step approval process is designed to maintain quality control and ensure that all expense reimbursements are accurate, reasonable, and compliant with the organization’s and governmental policies and regulations.

Learning workflows would differentiate between users based on their performance, allowing those who consistently excel to gain a "master-user" status. You just need to treat every form submission as a test, and keep the record for each employee. This approach would ensure that quality control efforts are focused on those who need it most, saving time and resources while maintaining high standards.

Item response theory (IRT) is a statistical framework used in test development to analyze and model the relationship between an individual's latent ability (e.g., proficiency in a subject) and their probability of responding correctly to test items (e.g., questions). IRT is widely used in educational testing and psychometric research to design and evaluate assessments, ensuring they are reliable, valid, and fair. In IRT, each test item is characterized by a set of parameters, which provide information about the item's difficulty, discrimination, and guessing. These parameters help in understanding how well an item can differentiate between individuals with different levels of ability. It assumes that if you can answer a more difficult test question, you are very likely to answer the easier ones.

Applying IRT (Rasch modeling, to be more specific) to business processes means treating each form submission as a test, incentivizing employees to be more careful with their work. Consistently high performance could lead to master-user status, reducing the need for managers to spend time reviewing numerous forms and requests. Of course, master-users should be paid more, for they cost less to oversee.

This combination of smart workflows and item response theory offers a significant business opportunity. Developing an algorithm that transforms every workflow into a learning system could revolutionize the way organizations operate. As users become more competent and achieve master-user status, the algorithm would automatically streamline processes, freeing up more resources and reducing bureaucracy.

If you want to be the next billionaire and understand organization theory, go for it. Thank me when you make it big.