Mentoring programs can be a major member benefit, but it depends on the quality of the matches. Finding a good fit between many mentors and mentees is not easy and can make the difference between a life-changing positive experience or a waste of time for both the mentor and mentee.

Mentoring software can help refine the search by recommending mentors who have relevant attributes. This is done using strategic registration-form questions that can be used in the matching algorithm.

Common useful questions include:



• Subsector and role: Identify current position to find a next-step mentor

• Location: State/city/chapter


• Gender: The mentee can select their preference in a mentor


• Personality type: Using the Myers-Briggs or DISC method — similar personalities make better matches


• Hobbies: Common interests help break the ice


• Topics of interest: Find out what exactly the mentee is looking for and what the mentors can help with — common topics include networking skills, negotiation, starting a business, managing teams, and conflict resolution

• Career goals and aspirations: Get them to tell their story and vision



Most of these questions can be set in the registration forms as drop-down selections or a set of check marks which can be easily compared by the mentoring software. Questions that require writing short paragraphs cannot be easily analyzed; that’s where the new artificial intelligence tools can help.



AI Finds Patterns in Text



AI tools can read through text fields and even full resumes and CVs looking for similar terms or phrases. Some mentoring software providers then display the findings in context.



For example, say mentor Kathy and mentee George are both in agriculture and therefore a possible match. By analyzing George’s career goal description and both resumes, the AI finds they are both interested in vertical farming and attended related events. It highlights the phrase “vertical farming” and where it is written in sentences which makes it easy to find. While other potential mentors are also in agriculture, this very specific common interest makes them a better fit.



Artificial intelligence can help find that needle-in-a-haystack type of information that would be impossible to find otherwise, as it requires reading and comparing a lot of text.



Reviewing Potential Mentors

AI tools can also list the potential mentors for each mentee in a more precise order, using the common interests it found and other criteria.



Reading through open text responses and resumes, AI can automatically detect details like the name of a university, company, job title, and common topics and build a database of this information in seconds. This is possible because it already has a lot of knowledge built in and can detect if a term is a name of a university versus a company. This is called entity detection, which means automatically assigning meaning to a term from a repository of past knowledge in the system.



Leveraging this mix of deep knowledge and detection capability and comparing the findings enables it to review vast amounts of text and use it to sort a list of potential mentors in the best order for each mentee.

Even with all this knowledge, AI should not be making any decisions. The mentoring program manager or the mentee should only use these features to help guide them toward the mentor that is the best fit. There are many considerations to take into account, but AI tools can make that decision easier than ever.