Nick Jestead
Vice President of Total Rewards and Analytics
PowerSchool
Episode 88
AI's Secret: Data-Driven Career Frameworks for True Pay-for-Performance.
Current chapter: Built by People podcast features insights from world's top HR leaders
Built By PeopleJuly 29, 2025 · 14:56
Thesis
“Implementing a structured, data-driven career framework is essential for unlocking effective pay-for-performance, empowering managers with organizational insights, and providing employees with clear career progression paths, significantly accelerated by leveraging AI for defining skills and competencies.”
Show notes
Nick Jestead didn't come up through HR. He's a mathematician and economist who stumbled in through data science — and that outside perspective is exactly what PowerSchool needed when it faced a problem every fast-growing company eventually hits: 1,000+ employees and no career framework.
Without defined leveling, everything becomes manual. Succession planning is guesswork. Compensation decisions lack defensible logic. Managers can't have meaningful career conversations because there's no shared vocabulary for what "getting to the next level" actually means. Nick built that vocabulary from scratch — using external salary benchmarking data to cluster employees into an initial schema, then partnering with business leaders over 12 months to validate and refine it. The part that surprised even him: AI compressed what would have been a year's worth of skills and competency definition work into a single week.
His most practical insight for any HR leader staring at a blank slate: don't wait until it's perfect. Draft something. People give feedback on drafts. They don't give feedback on blank processes — they just avoid them.
- Why the absence of a career framework makes compensation planning essentially impossible
- How Nick used salary data as a clustering proxy to slot 1,000+ employees before the framework was finalized
- The exact AI prompt strategy he used to define skills and competencies at scale
- How the framework unlocked true pay-for-performance by creating a prioritization logic for budget allocation
- Why "draft something" is the most powerful advice for HR leaders tackling daunting structural projects
This episode is brought to you by Previ — an employer network that saves employees thousands on the necessities they already pay for, at no cost to the company.
What you'll take away
- 1Building a comprehensive career framework requires adopting an external leveling structure (e.g., Radford) and a 12-month iterative journey of validation with business partners and managers.
- 2Data analytics, such as using salary as a proxy and clustering, can provide an initial schema for slotting employees into new career framework levels.
- 3AI and prompt engineering can drastically reduce the time to define skills and competencies at various mastery levels across numerous job families, turning a year-long task into a week-long draft.
- 4A well-implemented career framework unlocks pay-for-performance by providing a prioritization framework for compensation budgets, allowing companies to strategically reward top talent low in market.
- 5Such a framework empowers managers with clear competitive salary ranges and improves employee career conversations, fostering self-service growth and skill development.
What most organizations get wrong
- •People are scared of a blank piece of paper, but they're less scared of a draft. Draft something. You'll get feedback on a draft more than feedback on a blank process that's undefined.
- •With AI, it can get you off of the blank piece of paper. It's not going to be perfect and it shouldn't be treated as gospel, but it's a great way to get started, especially in the HR space.
In Nick's words
“I started as a mathematician economist by trade. I was starting in the finance space... Got introduced to HR by a headhunter that said, do you want to come and do data science in HR? And I said, I don't know what data science is, and I certainly don't think I belong in HR.”
Illustrates the non-traditional path into HR for a data-focused professional, highlighting the nascent stage of people analytics.
“If you wanted to know how many directors we had, how many VPs we had, you're just looking at individual titles and trying to manually sort. And I really do mean manually sort to assemble What do these populations look like? It made reporting super difficult for one.”
Clearly articulates the fundamental operational pain points and inefficiencies of lacking a structured career framework.
“Data analytics came into play big time because we can use salary as a proxy. If you're paying somebody kind of entry-level wages, presumably they're entry-level. If you're paying somebody more mid-level wages, you can get this schema of where folks fit and where they exist within your organization.”
Explains a practical, data-driven approach to initially categorize employees within a new career framework.
“We leveraged AI and the that we prompted AI to more or less say, I want you— and this is hacks of AI, right? I want you to take on the role of a talent development advisor. I want you to define the mastery using this job schema, and I want you to produce definitions of all of these competencies at each of these mastery levels.”
Provides a specific example of prompt engineering to rapidly generate skill and competency definitions, demonstrating AI's practical application in HR.
“I think this is the real hack to it, though, because it gives us a prioritization framework. What I mean by that is I can take even people with even performance and I can understand that somebody is low in market versus somebody that's at market, and I can prioritize that person that's low in market.”
Describes how a career framework enables precise, data-backed pay-for-performance decisions and strategic budget allocation.
“People are scared of a blank piece of paper, but they're less scared of a draft. Draft something. You'll get feedback on a draft more than feedback on a blank process that's undefined.”
Offers practical advice for overcoming analysis paralysis and fostering collaboration by starting with an imperfect draft.
The problems this episode addresses
- •Difficulty reporting on organizational structure and populations (e.g., number of directors, VPs) without a standardized leveling system, leading to manual sorting.
- •Inability to conduct meaningful succession planning due to a lack of clear levels, experience, and progression paths within the organization.
- •Challenges in assessing competitive pay and making fair compensation decisions when job titles are inconsistent and lack clear definitions of roles and responsibilities.
- •Struggles with effectively allocating compensation budgets and rewarding top talent without a prioritization framework that identifies employees low in market or with promotional readiness.
- •A significant time investment (e.g., a year for a few people) traditionally required to define and contextualize hundreds of skills and competencies at multiple mastery levels for various job families.
In this episode
Built by People podcast features insights from world's top HR leaders
Built by People
Dave Gentry is the VP of Total Rewards and Analytics for PowerSchool
WSJD Live: The Career Journey
Powerhouse implemented a career framework at a 1,000-person company
The Career Framework at Powerhouse
You leveraged AI to scale your team's capabilities during this implementation
How Microsoft Leveraged AI to Scale Our Team
Data-driven career framework has transformed how employees understand their career journeys
The LinkedIn Data-driven Career Framework
Nick: Creating something from scratch is daunting. Whether it's a career framework
Creating a new HR system is daunting
Topics covered
Organizations and entities mentioned
Full transcript
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