Data Slinging in Compensation: Why AI Cannot Rescue Untested Pay Analysis

Estimated reading time: 15 minutes

A friend of mine recently used AI to help her board’s compensation committee appreciate the data she had collected on the nonprofit’s executive pay package. The deck looked sharp. Her talking points landed. Members of the committee felt informed. What she did not bring into that room was IRS Section 4958, the rebuttable presumption of reasonableness, the comparability data requirements, or the distinction between planned and received compensation that auditors will eventually ask about. AI helped the output feel complete. It did not reveal the questions she had not yet learned to ask. That gap, between fluent output and genuine expertise, is the central risk facing the compensation profession right now. We are watching compensation data slinging get a glossy upgrade.

This is not a story about one person. It is the pattern. Non-comp pros (and some comp pros, honestly) pull a data point, drop it into an AI prompt, accept the polished result, and walk into the meeting feeling validated. Nobody asks where the data point came from. Furthermore, no one asks what would prove it wrong. Finally, nobody asks who validated the recommendation against the regulatory reality the organization actually lives in. The wrapper looks expert. Underneath, the thinking was never tested.

The Anatomy of Compensation Data Slinging

Compensation data slinging is the practice of pulling a data point, applying minimal judgment, and presenting it as analysis. It happens in every comp shop I have ever seen. An analyst pulls a survey cut. The midpoint becomes the answer. Nobody asks whether the job match was accurate, whether the sample is relevant to the industry, whether the aging factor reflects current movement, or whether the conclusion would survive a five-minute challenge from a skeptical CFO.

The diagnostic question is simple. Ask any comp professional why they recommended a specific number, and listen for the answer. If the answer is “the survey says,” that is data slinging. Conversely, when the response walks through the source, the methodology, the cross-checks, the assumptions tested, and the limits acknowledged, that is analysis. Most pay decisions in most organizations fall on the slinging side of that line, not because comp pros are lazy, but because the cycle is fast and the pressure to produce a number is real.

Now add AI to that environment. The output gets cleaner. Narrative tightens. Slides get prettier. Underneath, the assumptions remain untested. Furthermore, the polish creates a confidence effect that makes the analyst less likely to question the result. The committee accepts the recommendation because the presentation is good. As SHRM’s 2025 Compensation Trends to Watch notes, pay transparency expectations and board scrutiny of compensation philosophy are accelerating in parallel. Data slinging cannot meet that standard, no matter how fluent the wrapper.

When AI Laminates Weak Analysis

AI is a multiplier. Whatever you bring to it, AI amplifies. Bring deep expertise, careful assumptions, and tested data, and AI helps you communicate that with clarity and speed. Conversely, when compensation data slinging is the input, AI produces polished compensation data slinging at scale. The output looks identical. Risk profiles do not match, however.

Here is the failure mode in practice. An analyst asks AI to help interpret a survey cut. AI obliges with a clean explanation. The analyst feels educated. Subsequently, the analyst presents the conclusion as their own informed judgment. Nobody in the room knows whether the analyst would catch an error in the AI output, because the analyst lacks the underlying expertise to catch it. The conversation moves forward. A decision gets made. Risk gets booked.

This is the new Dunning-Kruger trap. The Dunning-Kruger effect describes how people with limited expertise often overestimate their own competence. AI accelerates this dramatically because fluency masquerades as understanding. When AI explains something to you in confident, organized prose, your brain reads that as expertise transferred. It is not. It is expertise borrowed from a model that cannot tell you what it does not know about your specific situation.

In my friend’s case, the AI did exactly what it was asked to do. The model made her data look compelling. However, it did not ask her about Section 4958 because she did not prompt it to. It did not flag the rebuttable presumption framework because that was outside the conversation. Moreover, it did not raise the auditor’s perspective on contemporaneous documentation, because nobody put the auditor’s perspective in the room. AI cannot tell you what you forgot to ask. That is the analyst’s job, and no amount of AI fluency will substitute for the question the analyst never thought to raise.

Did You Know?

You argue with AI more when you know a topic than when you do not. Think about that for a moment. When you have deep expertise, you catch the hedge, the missing nuance, the edge case the model glossed over. You push back, correct, and make the AI work harder. Conversely, when you do not know the topic well, the output sounds confident, the prose is clean, and you have no internal frame to challenge it. So, you accept. You copy. You present.

The friction between you and the AI is the evidence of your expertise. Conversely, the absence of friction is the warning sign. If you walked out of your last AI session having argued and refined, you brought knowledge to it. Walking out feeling impressed and validated, on the other hand, may mean you just got data-slung by a chatbot. Run this self-diagnostic before your next committee meeting: how often did you push back on the AI’s first answer? When the honest answer is “rarely,” the silence is not because the AI is always right. It is because you are not catching what it is getting wrong.

The Skills-Based Pay Example: Expert Consensus or Echo Chamber?

Consider how compensation thought leaders have lined up behind skills-based pay over the past several years. Conference panels endorse it. Vendor white papers promote it. Consulting firms publish frameworks. The discipline appears to have reached consensus. Most comp pros now nod along when skills-based pay comes up, as if the case were settled. Adoption data backs the momentum: WorldatWork’s 2025 research on skills-based rewards reports that 45% of HR leaders now reward skill acquisition, with 23% of organizations running some form of skills-based rewards program, up from 17% in 2023.

Where is the proof? Not the testimony. Certainly not the panel quotes. Furthermore, not the vendor case study sponsored by the vendor selling the solution. Adoption rates measure popularity, not validity. Specifically, where is the data on retention, internal equity, pay compression, legal defensibility under audit, and total cost of administration over a five-year horizon? The honest answer is that this evidence is thin. Industry consensus formed because thought leaders said so, and the discipline absorbed it as testimony rather than testing it as a hypothesis.

This is not an argument against skills-based pay. It may turn out to be valuable in specific contexts. The argument is against accepting it as a settled best practice when the proof is still being gathered. A comp pro who adopts skills-based pay because the experts say so is engaging in compensation data slinging at the strategy level. They are pulling a fashionable conclusion off the shelf and presenting it as their own informed recommendation. When their CFO or board asks why, the honest answer is “because the industry is moving that way,” which is not a defense any auditor will accept.

The deeper problem is that pay should reflect the actual depth and breadth of knowledge a job requires and how that knowledge gets demonstrated in the work. Knowing how to collect data and shape it into a presentation is not the same as demonstrating expertise. Specifically, it is putting a wrapper on data collection. The question for any pay model, including skills-based pay, is whether it captures real demonstrated capability or whether it captures the appearance of capability. Most skills inventories I have reviewed capture the latter.

What Real Validation Looks Like in Compensation Decisions

Validation is not a step that happens after the recommendation. It is the recommendation. A pay decision that has not been validated is not a recommendation; it is a guess presented in business attire. Real validation involves four practices that compensation data slinging routinely skips.

First, source the source. When a survey reports a median, ask how the sample was constructed, how jobs were matched, and what aging factor was applied. If the answer is opaque, the median is opaque. Second, stress-test the conclusion. Ask what evidence would change the recommendation, then go look for that evidence before presenting. Third, layer independent perspectives. Run the conclusion past someone who does not work in compensation, including legal, finance, or operations. If the recommendation falls apart under their questions, it was not ready. Fourth, document the reasoning, not just the outcome. The auditable trail must show how you got to the number, not just the number itself.

In my friend’s nonprofit situation, real validation would have looked like this. She would have started with the IRS §4958 rebuttable presumption framework. Building from there, she would have assembled appropriate comparability data, often drawn from multiple sources for larger organizations, that a reasonable person with relevant expertise would rely on. Furthermore, she would have documented the committee’s review process contemporaneously. Then she would have used AI to communicate the analysis cleanly to the committee, because AI is genuinely good at that final translation step. The work would have been hers. Polish would have been the tool’s contribution. That is the right division of labor.

The current regulatory environment makes validation non-negotiable. Pay transparency laws across California, Colorado, New York, Washington, and Illinois are increasing the need for employers to explain, document, and consistently apply the reasoning behind pay decisions. The EU Pay Transparency Directive, with member-state transposition required by June 7, 2026, raises the bar further. For nonprofit executives specifically, IRS §4958 intermediate sanctions remain in force and apply personally to organization managers who approve excess benefit transactions. None of these frameworks accepts “the AI helped me put it together” as a defense.

AI as Multiplier of Real Expertise

The hopeful frame is straightforward. AI is genuinely useful when it sits on top of real expertise. A comp pro who knows their regulatory environment, understands their data sources, has tested their assumptions, and can articulate the limits of their analysis will get tremendous leverage from AI. The model will help them communicate faster, explore alternative scenarios, draft narratives around defensible conclusions, and stress-test their own thinking. That is AI used well.

The same comp pro without the underlying expertise gets nothing useful from AI, even when the output looks identical. They cannot tell when the model is confidently wrong. Furthermore, these analysts cannot prompt for the considerations they do not know exist. Additionally, they cannot defend the recommendation when challenged, because the recommendation was never really theirs. The output belongs to the model. Risk belongs to the analyst, the committee, and the organization.

This is where compensation technology has to mature. The next generation of tools cannot simply collect recommendations or generate polished summaries. They need to preserve the reasoning trail behind every pay decision so that the work survives audit, leadership transition, and the next regulatory shift. That is the design principle behind SimplyMerit, MorganHR’s compensation administration platform for merit planning, manager rollup, and total rewards statements. Every recommendation carries its inputs. Each manager override carries its rationale. The cycle produces documentation that holds up under external scrutiny. AI fluency does not replace that trail; it sits on top of it.

The standard for the next generation of compensation work is not “did AI help me?” The standard is “did I know enough to know whether AI helped me?” That second question separates the comp pros who will thrive from the ones who will eventually find themselves unable to explain a decision that landed them in front of an auditor, a board, or a regulator.

A Self-Audit Challenge for Compensation Professionals

Before your next pay decision goes to a committee, run this six-question audit on your own work. The full audit takes five to ten minutes and may save months of cleanup later. None of it requires AI.

• Where did this data point come from, and would I bet my reputation on the methodology behind it?
• What would prove this recommendation wrong, and have I looked for that evidence?
• Who outside my comp function has reviewed this, and what did they push back on?
• What regulatory framework applies here, and have I documented compliance with it?
• If AI helped me produce this, did it amplify expertise I actually have, or did it create the appearance of expertise I do not?
• How often did I push back on the AI during this analysis? If I never argued with it, did I bring enough expertise to catch what it got wrong?

If any answer is uncomfortable, the recommendation is not ready. That discomfort is the signal that compensation data slinging is happening. Sit with it. Go back to the work. The hour spent validating is cheaper than the year spent explaining.

Key Takeaways

• Compensation data slinging is the discipline-wide habit of presenting data points as analysis without testing the assumptions underneath. AI does not fix it. AI amplifies it.
• Fluency is not expertise. When AI produces polished output, the analyst’s brain reads that as understanding transferred. It is not. The underlying knowledge gap remains, and the risk profile grows because nobody in the room can see it.
• Expert consensus is not proof. Skills-based pay is the current example. Comp pros adopting it because thought leaders endorse it are slinging strategy, not validating it. Demand evidence, not testimony.
• Real validation is the recommendation itself. Source the source, stress-test the conclusion, layer independent review, and document the reasoning. Anything less is a guess in business attire.
• The friction between you and AI is the evidence of your expertise. You argue with AI more when you know a topic than when you do not. If you never push back, that silence is not because the AI was right; it is because you are not catching what it got wrong.
• AI as a multiplier requires expertise to multiply. Comp pros who bring deep knowledge get leverage. Conversely, comp pros who bring half-baked analysis get polished half-baked analysis. The output looks the same. Risk does not.

Quick Implementation Checklist

1. Inventory your last five major pay recommendations. For each, write the source, the methodology, the validation step, and the regulatory framework you applied.
2. Identify which recommendations you could defend confidently under audit, and which you could not.
3. For the ones you could not, document what was missing and what you would do differently next cycle.
4. Build the six-question self-audit into your pre-committee review process.
5. Separate AI tasks into two categories: communicating analysis you already validated, and exploring questions you have not yet answered. Use AI freely for the first. For the second, apply caution and never present AI output as your conclusion without independent verification.
6. Establish a documented decision trail for every consequential pay decision, capturing inputs, reasoning, overrides, reviewers, and dates.
7. Schedule a quarterly review of your decision documentation against the regulatory standard in your jurisdiction.

Frequently Asked Questions About Compensation Data Slinging

For Compensation Professionals

What is compensation data slinging?

Compensation data slinging is the practice of pulling a data point, applying minimal judgment, and presenting it as analysis. The analyst cannot articulate whether the number feels right or wrong, has not validated the methodology behind the source, and has not stress-tested the conclusion. Furthermore, the result is presented with confidence that the underlying work does not support.

How do I know if I am slinging data instead of analyzing it?

Ask yourself whether you could walk a skeptical CFO through your reasoning step by step. If the answer is “the survey says” or “the consultant recommended,” that is slinging. Conversely, if you can name the methodology, the cross-checks, the assumptions you tested, and the evidence that would change your mind, that is analysis. The line is sharp once you start looking for it.

Why is AI making this problem worse instead of better?

AI amplifies whatever you bring to it. When you bring tested expertise, AI helps you communicate clearly and quickly. However, when you bring unexamined assumptions, AI produces polished versions of those assumptions. Additionally, the polish creates a confidence effect that makes the analyst less likely to question the result. The wrapper looks expert. Yet underneath, the thinking was never tested.

For Executives and HR Leaders on Pay Decisions

Why should leadership care about how comp recommendations are validated, not just what they produce?

Boards, regulators, and auditors increasingly ask the second question. A pay decision that produced a defensible outcome through indefensible reasoning is a governance failure waiting to surface. Consequently, leaders who demand documented validation behind compensation recommendations reduce regulatory exposure and build credibility with the workforce, the board, and external stakeholders.

How do I tell whether my comp team is slinging data?

Ask the team to walk you through the reasoning behind a recent recommendation. Listen for sources, methodology, cross-checks, and acknowledged limits. Moreover, ask what would change their mind. If the answer is thin in any of those areas, you have a slinging problem. The fix is process discipline, not more sophisticated tools.

Is there a quick way to gauge whether someone really knows the topic they used AI to analyze?

Yes. Ask how often they argued with the AI during the analysis. Genuine experts push back, correct, and refine because they catch what the model gets wrong. Conversely, non-experts accept the output because they have no internal frame to challenge it. The friction is the signal. If your team member sailed through an AI session without disagreement, the analysis is borrowed, not owned.

Skills-Based Pay and Expert Consensus

Is skills-based pay actually proven, or is it the latest example of expert consensus without evidence?

Skills-based pay may turn out to be valuable in specific contexts. Yet the broad consensus around it is built primarily on testimony from thought leaders and vendors selling related solutions, not on longitudinal evidence across diverse industries and organization sizes. Furthermore, the rigorous data on retention, internal equity, compression, and audit defensibility is still being collected. Comp pros should treat skills-based pay as a hypothesis worth testing in their context, not a settled best practice to adopt because the industry is moving that way.

What should I demand before adopting skills-based pay?

Ask for evidence specific to your industry, organization size, and workforce composition. Additionally, ask what would prove the model wrong, and what the exit path looks like if it does not perform. If the vendor or consultant cannot answer those questions clearly, you are being sold testimony, not validated practice.

Nonprofit Compliance and IRS Section 4958

What does IRS Section 4958 require for nonprofit executive compensation?

IRS §4958 establishes intermediate sanctions on excess benefit transactions between tax-exempt organizations and disqualified persons. Organizations can establish a rebuttable presumption of reasonableness by following three steps: approval by an independent body, use of appropriate comparability data that a reasonable person with relevant expertise would rely on, and contemporaneous documentation of the basis for the determination. Without these steps, both the executive and approving managers face personal tax exposure. AI does not satisfy any of these requirements.

Pay Transparency and Regulatory Validation

How do pay transparency laws change the validation burden?

Transparency regulations shift the standard of proof from outcome to reasoning. Where employers previously needed to defend results, they now must defend how those results were reached. Consequently, the EU Pay Transparency Directive, with transposition required by June 7, 2026, and active United States state pay transparency laws require objective criteria behind pay differences. Organizations whose pay decisions rest on AI-polished assumptions face material legal risk.

The MorganHR Point of View

After four decades in compensation, I will say it plainly. The profession’s greatest risk right now is not getting replaced by AI. Rather, the greatest risk is getting comfortable with AI-polished work that the profession never validated. Compensation data slinging existed before AI. Furthermore, AI just made it harder to spot, both for the analyst doing it and for the committee receiving it. The fluency is the camouflage.

Comp pros who will lead this profession over the next decade are the ones who treat AI as a multiplier of their expertise, not a substitute for it. They will know their regulations, source their data, test their assumptions, and document their reasoning. Subsequently, they will use AI to communicate that work more clearly and explore it more broadly. The work will be theirs. Leverage will be the tool’s contribution. That is the right division of labor, and it is the standard the next generation of compensation decisions needs to meet.

Disclaimer: This article is for educational purposes and should not be interpreted as legal, tax, or regulatory advice. Organizations should consult qualified legal counsel when evaluating compensation governance, pay transparency obligations, or nonprofit executive compensation requirements.