Writing performance reviews is one of the most important things a manager does, and one of the most dreaded. A manager with eight direct reports can easily spend two full working days turning a year of notes, goals and feedback into fair, specific written reviews. Most of that time goes on finding evidence and getting the words right, not on judgement.

AI can take most of that searching and drafting off the manager’s plate. Used well, it gives the time back to the part only a person can do: deciding what the year really meant and having an honest conversation about it. Used badly, it produces polished, generic text that nobody trusts. This guide shows the difference.

What AI can do in a review

Think of AI as a very fast research assistant with a good vocabulary. It is strongest at three jobs:

• Summarising the year. Pulling goal progress, one-to-one notes, peer feedback and recognition into one view, so recent events don’t crowd out the rest of the year.
• Writing a first draft. Turning bullet points into clear, specific sentences that reference real outcomes.
• Checking the language. Flagging vague phrases, gendered wording and comments about personality rather than work.

AI writes the draft. The manager writes the verdict.

Where AI should never decide

Some parts of a review carry consequences for someone’s pay, career and livelihood. These should always stay with a person who can explain and defend them:

A five-step workflow that stays fair

This is the workflow we recommend to Bench customers. It keeps the speed of AI and the accountability of a human review.

1. Collect the year. Goals, check-ins, peer feedback and recognition, pulled automatically for the full review period.
2. Let the manager rate first. The manager chooses a rating before reading any AI draft, so the draft can’t anchor the judgement.
3. Generate the draft. AI writes strengths, growth areas and examples, each linked back to its source.
4. Edit and own it. The manager rewrites in their own voice, removes anything they can’t stand behind, and signs.
5. Calibrate, then talk. HR reviews ratings across teams before the one-to-one conversation.

Prompts that actually work

If your team uses a general AI assistant, the prompt matters more than the tool. Give it facts, a structure and limits:

Then paste the facts: goals with their final status, two or three pieces of feedback, and notable wins. Never paste national ID numbers, salaries or health information into a tool your company hasn’t approved.

Bias, privacy and the PDPL

AI learns from text written by people, so it can repeat their blind spots. Two habits protect against that. First, read drafts for people in the same role side by side and look for differences in tone. Second, remove anything that describes personality or attitude instead of results and behaviour.

Reviews also contain personal data. Under Saudi Arabia’s Personal Data Protection Law (PDPL), you should know where that data is processed, limit it to what the task needs, and be able to tell employees how it’s used. Check this with your legal team before rolling out any AI tool.

Frequently asked questions

Should we tell employees AI helped write their review?

Yes. Explain that AI drafts from documented facts and that their manager decides and signs everything. Most people are comfortable with that; what they dislike is finding out later.

Will AI make reviews fairer?

It can, by using the whole year instead of the last few weeks and by catching biased language. It won’t fix unclear goals or an unfair rating, so calibration still matters.

What if a manager has few notes?

Then the draft will be thin, and that is useful information. It’s a sign to start regular check-ins now, so next year’s review has real evidence behind it.

End-of-service awards are one of the questions HR teams in Saudi Arabia get asked most, usually at the worst moment: the day someone resigns. The rules are clear once you see them laid out, and a worked example removes most of the anxiety for both sides.

The basic calculation

When a contract ends, the employee is entitled to half a month’s wage for each of the first five years of service, and a full month’s wage for each year after that. Partial years count in proportion to the time worked.

When the employee resigns

• Less than 2 years: no award.
• 2 to 5 years: one third of the award.
• 5 to 10 years: two thirds of the award.
• 10 years or more: the full award.

A worked example

Layla earns 10,000 SAR a month and leaves after 7 years. The full award is 5 × 5,000 for the first five years plus 2 × 10,000 for the next two, which is 45,000 SAR. If her contract ends, she receives 45,000 SAR. If she resigns, she receives two thirds of that: 30,000 SAR.

A clear calculation shared early turns a tense exit into a respectful one.

Special cases, such as exits for force majeure or certain resignations by women after marriage or childbirth, follow their own rules. Confirm these with your legal adviser before you finalise a payment.

Geofencing lets employees clock in only when they are at a work location. For field and multi-site teams it ends buddy punching, but rolled out carelessly it feels like surveillance. These are the habits that make it work.

Draw zones people recognise

Start with the gate, reception or site office, where people actually begin their shift. A radius of 100 to 200 metres covers most buildings and car parks without letting someone clock in from home.

Plan for GPS drift

Phones inside tall buildings or basements can report a location tens of metres away. Rather than blocking the stamp, record it with a flag so a manager can review it. People stay on time, and genuine problems still surface.

Earn trust before launch

• Location is checked only at the moment of clocking in and out.
• Nobody tracks movement during the day.
• Employees can see every stamp and its location themselves.

Say what you record before you record it.

Most engagement surveys fail for the same reason: they ask too much, too rarely, and nothing visible happens afterwards. Pulse check-ins fix that when they follow a few simple rules.

Ask one thing at a time

“How manageable is your workload today?” takes two seconds to answer. Twenty questions take ten minutes and get abandoned halfway. A short question asked often gives you a trend, which is far more useful than one big snapshot a year.

Make privacy obvious

People answer honestly when they know a manager can’t see their individual reply. Report results by team, and only when enough people have answered to keep everyone anonymous.

Close the loop

The fastest way to kill a pulse survey is silence. Each month, share one thing the results showed and one thing that changed because of it. Response rates follow.

People keep answering when they see someone is listening.

A mid-year review doesn’t need a month of meetings. With a clear calendar and one calibration session, most teams can finish in two weeks and come out with ratings people trust.

The two-week calendar

1. Days 1–4: employees complete a short self-review against their goals.
2. Days 3–6: managers draft reviews and propose ratings.
3. Day 7: calibration meeting for each department.
4. Days 8–12: one-to-one conversations.
5. Days 13–14: sign-off and goal updates.

Run calibration well

Calibration exists to make sure a “meets expectations” means the same thing in every team. Ask each manager to explain their top and bottom ratings with evidence, and look for teams where everyone landed in the same box.

Mid-year is for course correction, not a verdict.

Prepare managers before the calendar starts

What sinks a mid-year review is rarely the calendar; it is a manager walking in unprepared. Before the two weeks begin, run one short session with all reviewing managers and agree on three things: the rating scale with a concrete example for each level, the classic traps (recency bias toward the last two months, rewarding visible effort over actual impact), and the rule that any unusually high or low rating needs a documented example behind it. Then hand every manager their team list with the goals as written at the start of the year, not as remembered. One hour of preparation lifts the quality of dozens of conversations and cuts calibration arguments in half.

After the two weeks: close the loop fast

The value of a review evaporates if its output arrives late. Within one week of the cycle closing, every employee should have their summary in writing: the rating, the evidence behind it, their goals for the second half, and any commitment the manager made — training, support, an obstacle to remove. At company level, pull just three readings from the cycle: the rating distribution per manager (it exposes the lenient and the harsh), the skill gaps that repeat across teams and deserve one shared program instead of ad-hoc fixes, and a short list of top performers who need a retention plan before the market finds them. Then book a light monthly check-in — the second half of the year is steered with small corrections, not by waiting for the year-end review.

One standard, every team

Fairness across teams is harder than fairness within one. A lenient manager sitting next to a harsh one means the same rating carries two different meanings, and any raise or promotion decision built on it will wrong someone. Fix it with a short calibration session after the cycle: each manager shows their rating distribution with one example of their highest and lowest rating, and visible outliers get questioned. The goal is not a forced curve that pushes deserving people down, but exposing systematic drift and aligning what the scale means — so every employee can trust that their rating reflects their performance, not their manager’s generosity or severity.

Finally, protect the calendar with one managerial rule: the two review weeks are shielded from postponable meetings, and any slipped session must be rebooked within forty-eight hours or it escalates automatically. A calendar that may be broken once becomes optional for everyone, and a review cycle that stretches into two months loses its fairness — the late-reviewed are judged with different information and a different mood than those who went first.

Employee of the Month can lift a whole team, or quietly annoy it. The difference is whether people believe the winner deserved it. Here is how to design one that earns respect.

Start with criteria

Tie the award to your company values and describe what each looks like in practice. “Helped another team hit a deadline” is clearer than “team player”.

Let peers decide

Colleagues see the work managers miss: the person who trains newcomers or fixes problems nobody asked them to. Open nominations, a short voting window and visible results make the outcome feel fair.

Spread the spotlight

If the same department wins every month, rotate categories or cap repeat wins within a year. Keep a visible record of past winners so every award adds to a story.

Recognition only works when people believe it.

Make the announcement a moment

How the winner is announced matters as much as who wins. Skip the silent email and give it a real moment: two minutes at the monthly all-hands, a short story of what the person actually did and who it helped, and a thank-you in their own team’s words. Put it where people already look, on the office screens and in the company feed, and keep a simple wall of past winners so the award builds a visible history. A thirty-second, specific story about real work teaches the team more about what the company values than any policy document, and it gives every future nominee something concrete to aim at.

The three failure modes to design against

Employee-of-the-month programs usually die in one of three ways, and all three can be designed against. First, rotation-by-fairness: once the team senses the award simply takes turns, it means nothing — so allow repeat winners when deserved and a month with no winner when nobody qualifies. Second, visibility bias: sales and front-line wins get noticed while quiet background excellence never surfaces — so balance categories, or rotate the month’s theme between customer service, quality and behind-the-scenes support. Third, a prize with no felt value: a paper certificate alone sends a lukewarm message, while a mix of public recognition, a modest meaningful reward and a small privilege like a flexible day makes winning something people actually aim for.

Measure whether the award works

An award is a management tool, and tools get measured. Track three simple signals each quarter: the number and spread of nominations, because a dry month or nominations coming from one department only is an early warning; participation and retention of past winners, because an award whose winners quickly leave is rewarding the wrong thing; and its echo in pulse surveys on feeling appreciated. After a full year, review it bravely: adjust criteria that produced winners nobody found convincing, trial new categories, and do not fear pausing and relaunching — better that than carrying on a monthly ritual that lost its meaning while everyone politely plays along.

Remember too that Employee of the Month is one recognition tool among many, not a substitute for them: instant documented thanks when something lands, public peer shout-outs across teams, a sincere word in the weekly meeting — these build the everyday culture of appreciation that a monthly award sits on top of. Without that ground the award feels like an isolated ceremony; with it, it becomes the natural crowning of a culture the team lives daily.

Payroll day goes smoothly when the compliance work happens before it. This monthly checklist puts the most common checks in a sensible order.

Before payroll

• Confirm new joiners, leavers and salary changes.
• Check bank details (IBAN) for anyone who changed them.
• Approve overtime, deductions and unpaid leave.

Government platforms

• Mudad: upload the wage protection file and confirm it matches the bank transfer.
• GOSI: make sure registered wages match current salaries.
• Qiwa: check that contracts are documented and up to date.

Look ahead

List every contract and document expiring in the next 60 days, and check your Saudization position before any hiring decision.

Compliance is easier as a habit than as a rescue.

New graduates bring energy and fresh ideas, but their first job is also their first workplace. A clear 90-day plan helps them contribute sooner and decide to stay.

Before day one

Send a welcome message with the start time, location, dress code and who will meet them. Make sure their accounts work and that a buddy outside their reporting line is ready.

The first 30 days

Give three small goals they can finish, such as shadowing a process, fixing one real problem and presenting what they learned. Early wins build confidence faster than training sessions.

Day 90

Ask what has been harder than expected, what they’d change, and where they want to grow. Then agree on goals for the rest of the year.

The first 90 days decide whether someone builds a career with you.

AI is arriving in every HR tool. A simple test helps you decide where it belongs: does this feature inform a person, or replace one?

Where AI helps

• Summarising a year of feedback before a review.
• Explaining why attendance dipped in one team.
• Drafting policies and job descriptions for a person to edit.

Where it shouldn’t decide

Rejecting candidates, setting pay or choosing who leaves are decisions with real consequences. A person must be able to explain them, and the employee must be able to challenge them.

Say it openly

Publish a short note explaining which tools use AI, what data they see and who makes the final call. Clarity builds more trust than any feature.

AI should make people better at deciding, not decide for them.

Guardrails to put in writing

Before any AI tool touches HR decisions, write its guardrails into a short policy everyone knows. Name the decisions where the tool may only ever assist — hiring, termination, promotion, disciplinary action — with the final call and the accountability resting with a named human. Define what data may be fed to it and what may not, especially sensitive personal data, in line with data-protection rules. Require explainability: any recommendation or alert must arrive with its reasons, and an unexplained suggestion is not actionable. Finally, record for every significant decision what the tool proposed and what the human decided — that trail is your protection when a decision is ever challenged.

Where to start: low-risk, high-tedium work

The best entry point for AI in HR is not the riskiest decision but the most repetitive, least sensitive work: summarising CVs against written requirements, drafting letters and job ads, answering employees’ recurring questions from your approved policies, and surfacing patterns in attendance and turnover data before they become problems. In that space the model’s mistakes are cheap, and your team learns when to trust it and when to double-check. After a quarter or two of disciplined use you will have genuine in-house judgement about where it helps, and can widen the scope with confidence — instead of leaping straight to consequential decisions and regretting it at the first public mistake.

Watch for bias before it scales

The biggest danger of AI in HR decisions is not an individual mistake but its power to scale a bias that already lives in your data. A model trained on a hiring history that favoured one group will reproduce that preference efficiently, at greater scale, under a veneer of objectivity. So review the outputs of any screening or alerting tool periodically, broken down by sensitive dimensions such as gender, nationality and age, and look for systematic gaps with no professional explanation. When you find drift, fix the data and the criteria, not just the interface. Automated bias spreads faster than the human kind and hides better — and responsibility for it is fully yours, whatever its technical source.

Lastly, give employees an explicit right to know when an AI tool was used in a decision about them, and to request a human review of that decision. This is not regulatory luxury; it is what preserves the team’s trust in fair decisions as technology enters them — and it protects you from the fallout of the first mistake-story that spreads internally without context.