The Most Common AI Mistakes HR Managers Make

When LinkedIn Recruiters ranks candidates by fit score, managers often assume the top ten are genuinely the best matches and stop looking further. The algorithm optimises for candidates similar to your past hires, which means it replicates old hiring patterns rather than finding new talent.

The fix

Always review the bottom third of the algorithm's rankings for candidates who have the skills you need but a lower fit score, then assess them yourself before deciding.

Automated video assessment tools score delivery, word choice, and facial expressions, which are poor predictors of job performance and often penalise candidates with accents, speech differences, or interview anxiety. Managers skip phone screens to save time, which means candidates with strong abilities but weaker interview performance never get a chance.

The fix

Use HireVue only to flag candidates for your own review, then conduct a live screening call with every candidate who passes before making any judgement about their suitability.

Workday's skills matching scans for keyword overlaps and can flag candidates as unqualified because they call a skill by a different name or use industry terminology that the system does not recognise. A candidate with the right experience gets rejected because the algorithm did not understand the context.

The fix

Read every résumé that scores below your threshold on skills match if the role is hard to fill, because the algorithm often misses transferable skills and alternative terminology.

Managers implement algorithmic screening believing it removes human bias, but if the training data came from ten years of your past hiring decisions, the AI simply automates that bias at speed. You feel more confident the process is fair because a machine made the cut, not because the bias is gone.

The fix

Compare the demographic makeup of candidates your AI system passes through with the demographic makeup of candidates it rejects, then compare both to your local labour market to see whether the algorithm genuinely widens or narrows your pool.

When a candidate ranks highly on LinkedIn Recruiters and Workday, managers often assume the vetting is complete and move straight to offer. References are where you hear about whether someone actually listens, takes feedback, and works well with others. The algorithm cannot measure these things.

The fix

Make references non-negotiable regardless of how well a candidate scores, and ask referees specifically about the candidate's response to criticism and collaboration with colleagues who disagreed with them.

When you paste an employee's performance issue into ChatGPT and use its output for feedback, the message loses the specific examples and the tone of someone who actually manages that person. Employees feel like they are receiving a template rather than genuine feedback, which damages trust and makes improvement harder.

The fix

Write your own first draft of performance feedback with specific examples from your direct observation, then use ChatGPT only to check your tone and clarity, not to generate the content.

HR managers use ChatGPT to draft policies quickly, but overly formal AI language can make policies feel punitive rather than protective. Employees read policies written in corporate jargon and assume the company does not trust them, even when the policy is meant to support them.

The fix

After ChatGPT generates policy language, rewrite it in the way you would explain the rule to an employee sitting in front of you, using concrete examples instead of legal terminology.

When you use Rippling to broadcast a message about a policy change to all employees, the same words land differently depending on someone's situation. A new parent reads a policy change about flexibility differently than someone who does not have caring responsibilities. Mass communication from AI makes you feel efficient but can make employees feel unseen.

The fix

Write a core message in Rippling, then add a line at the end inviting employees to talk to you directly if their situation is different, and actually be available for those conversations.

When you get an alert from Workday that an employee's productivity metrics have dropped, you rely on that signal instead of asking the employee directly what is happening. The metrics might show a real problem, but they could also reflect someone managing a health issue, a difficult project phase, or a system failure that has nothing to do with performance.

The fix

When Workday flags a performance concern, use it as a prompt to have a real conversation with the employee and ask what is going on, rather than relying on the metric to explain the situation.

When every email exchange with an employee goes into Workday and you review it through the analytics lens, you can miss the human context. The system shows what was written but not the tone of voice, the relationship you have built, or the informal moments where you actually solved problems together.

The fix

Review communication analytics as one input to performance management, but also keep a separate note of important conversations you had in person or by phone, so your record includes the full picture of how you actually work together.

You can see in your Workday dashboard that your workforce became 3 percent more diverse last year and assume your recruitment is working. But if that diversity came through referral hiring and algorithmic screening still favours candidates who match your existing team, you have not actually changed the process that created the original imbalance.

The fix

Check whether the diversity improvement came from specific channels (referrals, job boards, recruiter outreach) and whether those same channels are now being filtered by your AI screening tools.

When you use Workday to run reports on performance ratings and exit interview data, you can identify trends weeks after people have already decided to leave or checked out. By the time the analytics show someone is struggling, they may have already been unhappy for months without you knowing.

The fix

Use Workday analytics to identify patterns, then follow up with direct one-to-one conversations before any formal action, because data about a problem is not the same as understanding why it happened.

LinkedIn Recruiters suggests you should hire for skills that are currently popular among candidates you have passed on, but that does not mean those skills match what your business actually needs. The algorithm optimises for candidate availability, not for your strategic priorities.

The fix

Use LinkedIn Recruiters's skills suggestions as market context only, then decide what skills matter for your business by talking to your hiring managers and team leads about what they actually need.

Workday measures attendance, productivity metrics, and time to hire easily, so HR managers sometimes build initiatives around those things even when they are not your biggest problems. You might focus on reducing time to hire by 10 percent when retention is your real crisis, just because the data is available.

The fix

Before running a report in Workday, write down what business problem you are trying to solve, then check whether the available data actually measures that problem or just something convenient.

When you present Workday analytics to executives showing that certain teams have higher turnover, it is easy to let them assume the data explains why it is happening. You have numbers but not context. Leadership then makes decisions based on incomplete information, and you end up executing solutions to the wrong problem.

The fix

Always include in your report what the data shows, what it does not show, and what you would need to learn through conversation or investigation to understand the full picture.