Scaling Mentor Matching with AI-Powered Workflows in Asana

The Challenge

The organization was managing a mentor and mentee program that required matching participants based on multiple criteria, including experience, interests, and goals. As participation grew, the process became increasingly difficult to manage.

Matching was conducted manually, requiring significant time and effort to review participant data and identify appropriate pairings. With hundreds of participants to evaluate each cycle, the process was not only time-intensive but also difficult to scale.

In addition, the lack of a standardized system introduced challenges in maintaining consistency and ensuring unbiased matches. As the program expanded, the organization needed a more structured and efficient approach to support continued growth.

The Solution

Spur Reply partnered with the organization to design and implement an AI-powered workflow in Asana that automated the mentor matching process.

Using Asana’s AI Studio, the team developed a workflow that evaluates mentor and mentee profiles against defined criteria to generate recommended matches. This approach replaced manual evaluation with a structured, data-driven process.

To support scalability and consistency, Spur Reply also created standardized templates and workflows within Asana. These templates ensured that each matching cycle followed a repeatable process, reducing variability and improving efficiency over time.

The solution was developed through a series of discovery and design sessions, ensuring alignment with program goals and participant needs. Spur Reply also provided training, documentation, and best practices to enable the team to independently manage and scale the workflow for future cycles.

The Results

The implementation of an AI-powered matching workflow significantly improved the efficiency and scalability of the mentor program.

Quantified Impact

• Reduced matching time from 3+ weeks to 2-3 days
• Enabled automated matching of 200+ participants per cycle
• Saved approximately 100+ hours per matching cycle

Operational Improvements

• Centralized the entire matching process within Asana
• Standardized workflows to ensure consistency across cycles
• Reduced manual effort required to evaluate and match participants

Program Impact

• Improved match quality and participant experience
• Reduced potential for bias through structured, criteria-based matching
• Established a scalable foundation to support program growth

Why It Matters

As organizations scale internal programs, manual processes quickly become a barrier to growth. This engagement demonstrates how combining workflow standardization with AI can transform complex, time-intensive processes into efficient, repeatable systems.

By leveraging Asana as a central platform and embedding AI into core workflows, organizations can not only improve efficiency but also drive greater consistency and fairness in decision-making.

This approach is particularly valuable for programs that require evaluating large volumes of data against defined criteria, where scalability and objectivity are critical to success.