Leveraging AI and Statistical Insights for Project Estimations

We've all encountered the challenges of project estimation, where stakeholders demand quick ballpark figures. I've been there too. This article doesn't promise a one-size-fits-all estimation formula, but it does shed light on a data-driven approach tailored to the AI era.

Understanding the Basics:

Assuming you're working with a team and have access to the team's Average Velocity (possibly from JIRA), or can compute it based on story completions, let's dive in.

1. Task Assessment: Complexity, Criticality, and Business Value

Begin by evaluating tasks and assigning values to their "Complexity," "Criticality," and "Business Value" within the project context. Assign a scale of 1 to 3 to each parameter, indicating low to high. ??

2. Categorization and Tabulation:

Tabulate tasks in a structured format based on their aforementioned attributes. Populate the table with values representing the assigned complexity, criticality, and business value scores. ???

3. Historical Data: Establishing Reference Points

Analyze completed tasks to determine the average time your team has historically spent on similar assignments. These reference values will be crucial for building the estimation model.??

4. Statistical Relationship: Leveraging AI Tools

Integrate AI tools like ChatGPT or Bard to establish a mathematical relationship between the task attributes (complexity, criticality, business value) and historical time data. Let AI propose a formula connecting these variables (y = f(x)). ??

5. Formula Application and Validation:

Implement the derived formula within a spreadsheet application. This step streamlines the estimation process. For validation, collaborate with your development lead to cross-reference the results and refine the formula if required, using AI assistance. ??

6. Continuous Improvement: Refining the Formula

As you accumulate more sample data, iterate on the formula to enhance its accuracy. AI tools can assist in fine-tuning the model, incorporating new insights. ??

Benefits and Future Prospects:

This one-time process, though involving initial effort, yields substantial long-term benefits. Accurate estimations save time, enhance stakeholder trust, and contribute to better project planning. ?

Conclusion:

By merging the power of statistical insights, AI tools, and historical data, we've transformed the estimation process. The dynamic formula derived through this method equips you to face the estimation challenge in the AI era with confidence.

Note:

Remember, while this method doesn't guarantee perfection, it sets a robust foundation for improved estimations. Embrace this approach as a valuable tool in your estimation toolkit.

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