Authored by Sara Hillenmeyer, VP of AI and Data Science at Payscale. Sara has 16 years of AI experience, including 5 years in compensation, and leads the teams that build Payscale’s data offerings and AI-powered software. Before joining Payscale, she was an AI scientist and led AI research and engineering teams at Amazon. She holds a BS from Brown University and a PhD from Stanford University. Her AI research has more than 1,500 citations, and she holds six patents.
Your hiring manager wants a unicorn. Your compensation team has salary data for horses. And you’re the recruiter expected to make an offer everyone can agree on.
Does this sound familiar?
As artificial intelligence (AI) changes the skills organizations need, this scenario is becoming increasingly common. Hiring managers want candidates with machine learning, generative AI, or agentic AI experience, but the approved salary range doesn’t always reflect those requirements.
Imagine you’ve found a great candidate with machine learning expertise. The hiring manager is thrilled, but there’s just one problem: The candidate wants 15% above the approved range.
You turn to compensation for guidance, but the team can’t confidently say whether the additional pay is justified.
So what do you do now?
I’ve spent 15 years developing machine learning and AI-powered tools, and at Payscale, my team works on skills-based pay questions like this every day. One of the most important lessons is that having a valuable skill doesn’t automatically mean someone should earn more for it.
What matters is how valuable that skill is for the job you’re hiring for.
Here are four rules for making smarter skills-based pay decisions.
Rule 1: Don’t pay extra for skills the job already requires
Let’s say you’re hiring a Machine Learning Engineer II.
You might be impressed by a candidate’s experience with programming languages or running model inference using TensorFlow or PyTorch. Those are important capabilities, but they’re also standard expectations for the role.
Paying an additional premium for them would mean compensating someone twice for skills already reflected in the job’s market rate.
Now consider three more specialized skills:
- AI Research: 4.1% pay premium
- Fraud Detection: 3.0% pay premium
- Machine Learning Model Training: 2.0% pay premium
According to Payscale’s skills pricing data, these skills command additional pay for a Machine Learning Engineer II.
The difference is that they’re less common among organizations hiring for this role and carry measurable market value.
Before increasing an offer, ask whether you’re paying for something genuinely specialized or something every qualified candidate should already bring to the table.
Rule 2: Pay for what the skill contributes to the job
Here’s where skills-based compensation gets interesting.
Consider machine learning itself.
For a Back-End Developer II, that expertise can be worth an additional 2.8%.
For an AI Engineer II or a Machine Learning Engineer II, it's pretty expected; there’s no additional premium.
And for an aircraft mechanic? Building machine learning models probably won't make someone better at maintaining an airplane.
The same skill can have completely different values depending on the job.
I once worked at a logistics company that offered employees extra pay for holding a commercial driver’s license (CDL). It sounded reasonable. After all, trucking and logistics go hand in hand. But then employees in roles unrelated to driving, including administrative staff, started earning their CDLs to qualify for the additional pay.
The company was spending more money without necessarily getting more value from those skills. That also meant less budget available for the positions where a CDL actually mattered.
Case in point: Reward the skills your organization needs, not simply the skills people happen to have.
Rule 3: Don’t assume a skill has the same value at every career level
Experience changes what employers expect from a candidate.
For someone relatively early in their career, machine learning expertise might be a differentiator worth paying extra for.
But by the time you’re hiring a Principal Engineer, that same expertise may already be expected.
The market rate for the senior role reflects higher technical knowledge and experience.
This is why applying a standard AI skills premium across every level can become expensive.
A skill that deserves additional compensation at Level II might not warrant any premium at the principal level.
For recruiters, the question isn’t simply whether a candidate has a particular skill. It’s whether that skill adds value beyond what the role and level already expect.
Rule 4: Think carefully about how you reward specialized skills
Some skills and certifications have persistent value to your organization and should be incentivized in base pay. For skills and certifications that have more transient value, consider one-time payouts like sign-on bonuses. Here are a couple of examples:
Persistent: A security clearance for a software developer working at an organization that handles government contracts. If your organization will always need it, paying extra for it indefinitely makes sense. In this case, adding a certification premium to the role's base pay is a good option. If some employees work on projects that require the clearance and others do not, split the work into two roles, with the certification required in one of them. The premium then attaches to the job rather than to an individual.
Transient: Take a hot new capability like agentic AI. Its market premium appears when demand outpaces supply, and it fades as more people build the skill. Eventually it becomes a standard expectation of the role and is built into market rates. For skills like these, a sign-on bonus or a one-time payment after an employee demonstrates the skill on a project lets you recognize the expertise without locking in a permanent increase.
Whether a skill or certification is transient or persistent matters, but so do the role, retention needs, internal equity, and your compensation philosophy. The key is to decide on purpose rather than adding every premium to base pay by default.
Bringing compensation intelligence into the recruiting process
Much of the conversation about AI in talent acquisition has focused on screening resumes, sourcing candidates, and automating communications.
But what about using AI to make better pay decisions?
Consider three stages of recruiting where compensation intelligence could make a meaningful difference.
Writing the job description: Recruiters and hiring managers could identify which requested skills are uncommon and whether they command additional pay.
Evaluating candidates: Recruiters could assess how particular capabilities influence market pay for the position, rather than treating every technical skill on a resume as equally valuable.
Building the offer: When a candidate requests more than the approved range, recruiters and compensation teams could evaluate that request using documented, role-specific market data.
Better access to compensation intelligence can improve these conversations. In Payscale’s ROI of strategic compensation research, 85% of surveyed customers said they responded faster to ad hoc job pricing requests, and 70% said they could make more confident, defensible compensation decisions.
Your next move: Start with three difficult requisitions
Find three open requisitions where compensation is creating friction. Maybe candidates keep asking for more than the approved range, or hiring managers want specialized skills they can’t confidently price.
Then, for each position, ask these three questions:
- Is the skill rare for this specific role and level?
- Does market data support paying a premium for it?
- Can recruiters access that information when making an offer?
If you can’t answer those questions, you’ve found a gap worth addressing.
That’s where Payscale Ascent comes in. By bringing market benchmarking, skills and certifications data, and compensation analysis together, Ascent helps teams understand how specific skills influence pay within a role. That gives compensation professionals better information to share with recruiters and hiring managers before they extend an offer.
A candidate asking for 15% more likely knows what other employers are willing to pay. Your hiring team should have the information to evaluate that request, too.
And with better skills intelligence, the next conversation about hiring a unicorn doesn’t have to end in a debate about what horses cost.
