Accelerate equitable salary recommendations in higher education using Amazon Quick

The challenge

Compensation teams at universities with geographically diverse campuses manage a complex, time-consuming process to keep salary ranges equitable across every location.

HR leaders must manually reconcile multiple layers of information. Institutional data forms the foundation for this analysis and includes compensation philosophy, current salaries, role equivalencies, and tenure. Regional factors add another dimension, from cost of living and housing availability to broader economic conditions. On top of that, each campus carries its own social and cultural characteristics that shape what an equitable range actually looks like.

Even a single position requires days of manually referencing across these dimensions, which slows decisions and risks inconsistency across the system.

The solution

Amazon Quick automates the multi-step salary equity analysis by combining trusted institutional data with AI-powered regional research.

How it works

To set up the salary recommendations:

1. Configure access to institutional data, such as salaries, classifications, and tenure. When you use single sign-on, Quick aligns with the role-based access control (RBAC) to these sources. For more information, see Identity and access management in Quick.

2. Create a flow by describing the analysis that you need. You can build a flow from scratch, or create one using natural language. The following is an example prompt that you can customize:
   I want to have this flow act as a salary recommendation workflow. A user would enter a job title, along with the university they are part of. The flow should then look up comparable salaries across the system, then provide a salary range based on established salaries across the AnyOrganization University System. It should be able to understand that the AnyOrganization Duluth salary would not be exactly the same as a AnyOrganization Atlanta salary.

3. To run an automated analysis, start the flow, and then enter a job title and the campus location.

4. You receive a comprehensive salary range report in minutes. You can view the report through the Quick interface, add it to a space for governed sharing, or download it as a PDF or Microsoft Word document to share outside of Quick.

5. Use the in-depth and unbiased research to review and finalize the job classification and salary range.

Result: Days of manual analysis compressed into minutes, with consistent methodology across all campuses.

Conceptual solution architecture

Quick spaces allow governed access to information in various formats.

Quick knowledge bases support natural language, semantic-based interactions with various types of document.

Quick Research uses selected knowledge sources to complete an in-depth analysis based on the natural language instructions that you provide. Quick Research returns a completed document with references to the sources that it used for its findings.

Quick flows automate business processes so that you can run them consistently every time.

To get started, see Amazon Quick - AI assistant and Getting stared with Amazon Quick.