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DATA CENTERS

Rapidly Upskilling Through AI

Leveraging an LLM-Enabled Advanced Talent Risk Analysis for Data Center Builds

Client Objective 

Rocks and risk. A multinational corporation and technology company sought a scalable, quantitative method to identify talent risks for specific occupations in targeted regions. Their current view was choppy and didn’t mitigate risk. They dreamt of a solution that could help them evaluate talent pools with actionable insights that weren’t murky. 

Unique Challenge 

Paddling upstream. The client has data center projects in remote areas, but they faced significant challenges in securing skilled labor. Existing datasets from government resources were rough, fragmented, and lacked an integrated risk framework. The team felt like they weren’t making the progress they needed. 

Bespoke Solution 

Moving with the current. Unify used an LLM as a collaborative partner to rapidly upskill in statistical reasoning, advanced Excel formulas, and workforce analytics. The result was quite splashy. They created a new methodology that provided dynamic, location-specific risk quotients for targeted occupations. Unify built an Excel-based Risk Report that made risk easy to navigate. Thanks to Unify, the team could select any major U.S. city to view occupation-specific risk quotients, compare it to a national baseline, view adjacent occupation data for recruitment flexibility, and see population trends and state infrastructure grades. 

Proven Impact 

Ready for new adventures. The company gained a repeatable and adaptable model that could instantly assess workforce availability risks. The new methodology informed data center site planning, reducing the risk of delayed builds due to staffing shortages. The project was so successful that the client extended their engagement with Unify for additional regions and roles. Where will the journey go next? We have a feeling that no matter what, smooth waters are ahead.

To learn more about Unify’s proven impact and custom solutions, contact us today

 

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