Why AI Feels Like the Life of the Party

Moore Cooperative attended World at Work Total Rewards ’25 in Orlando this week, and AI wasn’t a sidebar – it was the main event. From Marci Rossell’s big-picture forecast (that AI will lift low-skill workers into high-impact roles) to hands-on demos of digital copilots drafting comp-change communications, the energy was electric.

We left convinced: AI isn’t a future nice-to-have. It’s your tireless teammate, digging into data around the clock so you can focus on strategy and people.

Across three days, we saw analytics uncover hidden pay gaps, generative tools create peer recognition messages, and live market forecasts delivered in seconds. Now it’s your turn to harness that same power — and Moore Cooperative is ready to help.

Five Reasons GenAI is Transforming Total Rewards

1. Bias-Busting Analytics
Advanced AI can now predict employee turnover with up to 87% accuracy, identifying risk before it becomes a crisis. These tools also detect pay equity gaps across demographics in minutes, replacing what used to take weeks of manual cross-tab analysis.

More than just identifying issues, AI recommends fair, data-driven compensation strategies that align with best practices. As the race to close equity gaps accelerates, bias detection is becoming one of HR’s most powerful tools.

2. Digital Colleagues
Nearly 82% of HR teams plan to expand AI usage in the next five years, signaling a seismic shift toward embedded digital “copilots.” These AI assistants draft total rewards statements, flag salary outliers, and help shape policy updates—freeing comp teams to focus on people-first work.

Yet, only 1% of organizations consider their AI programs “mature.” That means a major opportunity for early adopters. With thoughtful governance, your digital colleagues become reliable copilots—not black-box risks.

3. Generative-AI Comms
The arrival of ChatGPT was a milestone for HR. Teams now use generative AI to draft everything from peer recognition notes to executive summaries of thousands of employee survey comments.

In many orgs, AI chatbots already handle 37% of routine employee questions, dramatically shortening response times. Rather than depersonalize HR, these tools empower teams to invest more time in coaching, planning, and the high-touch conversations that matter most.

4. Predictive Insights
Conference demos showed AI tools running compensation scenarios in real time. These simulations analyzed merit pool options, equity distributions, and budget adjustments using live market and internal data. Without opening a spreadsheet, compensation leaders could test strategies and forecast outcomes. Whether adjusting to wage inflation or modeling remote-work policies, predictive AI adds clarity to complex decisions.

5. Upskilling & Transformation
About 45% of global HR leaders already embrace AI to drive transformation, with that figure poised to climb as success stories multiply. Sixty percent of HR professionals place AI and automation at the forefront of their strategic roadmap, underscoring a collective leap toward digital fluency. As Moore Cooperative’s clients have discovered, integrating AI not only sharpens comp accuracy but also accelerates talent development – lifting employees from routine tasks into high-value roles at unprecedented speed.

Five ChatGPT Prompts to Supercharge Your Comp Work

Use these prompts with ChatGPT, Copilot, or your chosen GenAI tool to generate instant insights and action. Just make sure your data is ready first.

Getting Your Data AI-Ready

Before using AI on compensation data, ensure it’s clean, complete, and privacy-compliant. Never submit personally identifiable information (PII) into non-private systems.

1. Create a Sanitized CSV
   Include only job code, title, level, location, years of service, gender, race, ethnicity, base salary, bonus percentage, and other comp fields.
2. Anonymize PII
   Replace names, emails, and employee IDs with generic codes (e.g., E001, E002).
3. Standardize Formats
   Ensure salary data is numeric, dates follow a single format, and location names match official site designations.
4. Validate Data Quality
   Check for missing values or extreme outliers. Reliable AI requires reliable input.

Prompts

1. “Comp-Change Retrospective”
   Using my sanitized CSV of the past three years' compensation adjustments for [Job Family], quantify the average salary gap percentage between men and women, recommend three remediation strategies grounded in modern comp best practices, and draft a concise, manager-friendly email template to communicate these changes.
   
2. “Market-Match Deep-Dive“
   Compare our [Department] total-rewards package (base, bonus, equity) against publicly available data for [Competitor A], [Competitor B], and [Competitor C]. Highlight the top two areas where we lag or lead, quantify the dollar-impact of each gap, and propose two strategic positioning tactics to close those gaps.
   
3. “Recognition Reimagined“
   For a cross-functional project team of [X] members, draft three peer-to-peer recognition messages. Each should spotlight specific accomplishments, use a warm, human tone, and recommend a monetary reward level based on industry benchmarks.
   
4. “Equity Simulation”
   Model the financial impact of reallocating 2 percent of our annual comp budget to mid-career women in [Location]. Estimate the total spend increase, project the shift in our internal pay-equity ratio, and draft key talking points for leadership to share.
   
5. “Job-Architecture Accelerator“
   Using my sanitized CSV of current job architecture data (job code, family, level), propose a plan to integrate a skills taxonomy. Group related skills into proficiency tiers for each role, identify two quick-win areas for pilot implementation, and draft an executive-summary slide outline highlighting benefits and next steps.

Each prompt delivers analysis, narrative, and recommendations in one run. The more context you provide, the better the results. Don’t hesitate to iterate with the AI.

Your AI Roadmap Starts Here

The journey to AI integration doesn’t have to be long or complicated. Start with these five practical steps:

1. Audit & Cleanse
   Host a data hackathon with Comp, HRIS, and Analytics teams. Standardize job titles, confirm pay fields, and remove all PII.
2. Pilot with Purpose
   Select a focused use case, like pay equity or recognition messaging. Start with a small, controlled rollout. Job description updates are also a low-risk entry point.
3. Govern & Secure
   Create an AI steering committee with Compensation, HRIS, Legal, and Comms. Define privacy standards, review processes, and update schedules.
4. Train & Empower
   Offer prompt-writing workshops and build a shared prompt library. Moore Cooperative can support your training and maturity journey.
5. Measure, Iterate & Scale
   Track metrics like time saved, accuracy gains, and user satisfaction. Refine prompts monthly and expand AI into more workflows — from comp approvals to market forecasting.

We’ve helped teams at every step of this path, always focused on outcomes over buzzwords.

What’s Next

Feeling excited, or maybe a little overwhelmed? You’re not alone. Our next post will cover how pay transparency and benchmarking create a strong foundation for AI-driven compensation. We’ll explore how to align internal equity with market competitiveness, build trust, and prepare your data for the future.

Stay tuned – we’re just getting started!