An AI-driven platform that acts as an internal talent marketplace. It proactively recommends open roles, projects, or mentorship opportunities to employees based on their skills, career aspirations, and development needs.
Employees often leave to find growth they could have had internally. Internal vacancies are posted on an intranet page few people check, managers hold on to strong performers, and staff do not know which projects or roles would suit their ambitions. Meanwhile, hiring managers recruit externally at greater cost and risk, unaware that someone two teams away has the skills and the interest.
An AI internal mobility platform works as a talent marketplace inside the organisation. Employees record their skills and career goals, and the system recommends open roles, short-term projects, stretch assignments and mentors that fit. Hiring managers see suitable internal candidates before advertising externally. Recommendations explain the match and any skill gaps, so employees know what to develop. HR retains oversight of fairness and process, and managers still make selection decisions through normal channels.
Employee profiles are built from HRIS data, skills records and self-entered career interests, which the employee controls and updates.
Open roles, projects and mentoring offers are published into the platform from the applicant tracking system or by managers directly.
A recommendation engine compares skills graphs and interests with opportunity requirements and ranks matches with a short explanation.
Employees receive tailored suggestions and can express interest, while hiring managers see a list of internal candidates for each opening.
Outcomes such as moves, project completions and retention are tracked, and HR reviews recommendation patterns for fairness across groups.
An employee expresses interest in moving into product management. The AI platform suggests they join a short-term 'stretch project' with the product team and recommends an internal mentor, creating a clear development path and improving retention.
Recommendation Engines, Skills Graphs, ATS/HRIS Integration
Internal selection decisions are covered by federal and state anti-discrimination law and the Fair Work Act's general protections, so recommendation criteria must be job-related and monitored for patterns that disadvantage people by age, gender, disability, carer status or other protected attributes. Employees should be able to see why they were matched and to opt out. Handle career interests carefully; an employee exploring other roles may not want their manager to know. The Privacy Act employee records exemption may apply to some data, but good practice is to follow the Australian Privacy Principles anyway. Any enterprise agreement provisions on internal vacancies and selection processes still apply.
This solution requires careful evaluation of cost-benefit ratio. Consider implementation only after thorough analysis and risk assessment.
That depends on how you configure the platform, and it matters a great deal for adoption. Many organisations let employees browse and receive recommendations privately, with visibility to the current manager only when they formally apply. Agree the policy before launch and explain it clearly, so employees feel safe using the system.
It makes growth opportunities visible and easy to act on, which addresses one of the common reasons people leave. Stretch projects and mentoring also let employees build skills without changing jobs. Track retention and internal move rates over time to see whether the platform is making a difference in your organisation.
The one-to-two-month band covers integrating the HRIS and applicant tracking system, setting up a skills framework, configuring privacy rules and launching with a pilot group. A launch plan that seeds genuine projects and roles into the marketplace is as important as the technology itself.
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