AI for Performance Reviews: Guide for Small Business
AI for performance reviews: 8 use cases (what works, what doesn't), 7-step responsible process, prompt examples, risks, and common mistakes managers make.
AI for Performance Reviews
An honest guide for small business managers
The first time I used AI to help write a performance review, I made the mistake almost every manager makes the first time. I gave it three vague observations, asked for a complete review, and got back four paragraphs that sounded plausible but contained two completely invented metrics, three generic phrases that could have applied to anyone, and one line of subtly gendered language I almost missed. The output looked finished. It was useless. The version I eventually sent took the same 90 minutes to produce as if I had not used AI at all, because I had to fact-check, rewrite, and rebuild from scratch.
Most articles on AI for performance reviews fall into one of two camps. The vendor-written ones tell you AI is going to revolutionize performance management and you should buy their AI feature. The skeptical ones tell you AI is dangerous and you should avoid it entirely. Both are wrong about what AI actually does well and where it actually fails. The reality is more nuanced and more useful: AI is genuinely helpful for the mechanical writing work, useless for the evaluation work, and risky if you do not understand the difference.
This guide is different. It is written for small business owners and operators who are deciding whether and how to use AI in their actual review process, without an HR team to manage the rollout. You will get the 8 specific use cases where AI works (and where it does not), the 7-step responsible process, prompt examples you can adapt, the legal and ethical risks, and the common mistakes that turn AI from a productivity tool into a problem. I built FirstHR for this audience because most performance management content assumes either enterprise sophistication or a level of AI hype that does not match what AI actually does well.
What AI Can Actually Do for Performance Reviews
The simple working description: AI is a writing assistant that takes your observations and turns them into structured prose. It is not a manager. It cannot tell whether the employee performed well; it can only restructure or rephrase what you tell it. The quality of AI output is determined by the quality of your input. Garbage in, polished garbage out.
Three things AI does genuinely well for performance reviews. First, structural drafting: turning your bullet-point observations into well-organized prose with consistent sections (summary, strengths, development areas, goals). Second, rephrasing: converting vague phrases to specific ones, harsh language to neutral, technical observations to accessible language. Third, pattern analysis across multiple reviews: identifying whether you are consistently softer or harsher with certain employees, recycling generic phrases, or varying specificity. These three use cases save real time without creating risks if managed properly.
Three things AI does poorly or dangerously. First, evaluation: AI cannot judge whether the employee performed well; the manager must. Second, hallucinated specifics: AI tools sometimes invent plausible-sounding metrics, customer names, or project details that become legal exposure if disputed. Third, bias amplification: AI training data includes biased language patterns that can creep into output if the manager does not actively edit for them. Each of these failures has produced documented problems in actual workplace deployments.
Why This Looks Different at Small Business Scale
Most articles on AI for performance reviews are written for enterprise HR teams with formal AI policies, legal review of every tool, and dedicated People Operations specialists managing the rollout. None of that applies at small business scale. The owner-operator running a 12-person company is the manager, the HR function, the AI policy, and the legal reviewer, all in one role.
Three implications for small business AI use. First, the time savings matter more. A founder doing reviews for 8-10 direct reports while running the rest of the business needs the productivity gains AI provides. The 60-80% time savings on writing is real and meaningful at small scale, where every hour matters. Skipping AI entirely on principle ignores genuine productivity gains.
Second, the risks are also real and personal. At enterprise scale, an AI policy gone wrong creates a compliance issue. At small business scale, an AI policy gone wrong is the founder's personal liability, since the founder is usually the decision-maker, the writer, and the legal exposure all at once. Small businesses get less protection from organizational structure, so the discipline matters more.
Third, transparency is easier. A founder telling a 12-person team how they use AI is a single 5-minute conversation. An enterprise rolling out AI usage policy across 5,000 employees is a quarterly initiative. Small businesses can move faster on disclosure and policy, which is exactly the right move because trust scales poorly when discovered after the fact. SHRM's performance management toolkit covers the broader context for how AI fits into responsible performance management at any scale.
8 Specific Use Cases: What Works, What Does Not
AI is not uniformly good or bad for performance reviews; specific use cases work well, others fail consistently. Below are the 8 most common applications, with honest assessment of where each fits.
The pattern across these use cases: AI is good at mechanical work (drafting, rephrasing, translating, analyzing), neutral at preparation work (self-review drafts, examples), and bad at decision work (compensation, ratings, evaluation). The line that matters: anything where the AI is restructuring or rephrasing your input is generally safe; anything where the AI is making or substantially informing decisions about employees is legally and ethically problematic.
For the broader practice of writing performance reviews well, with or without AI assistance, the performance review writing guide covers the 7-step process that produces reviews driving behavior change.
A 7-Step Responsible Process for Using AI in Reviews
The process below produces AI-assisted reviews that save time without creating risks. The total time investment is 30-45 minutes per review when done right, compared to 90-120 minutes without AI. The steps are not optional; skipping any one introduces a specific risk that the others do not address.
Two failure modes to avoid. First, do not skip Step 1 (evidence collection). The temptation is to ask AI to write a review based on vague impressions, hoping it will fill in the specifics. AI cannot create specifics it does not have; it will either invent them (Step 3 violation) or produce generic content. Collect evidence first, always. Second, do not skip Step 4 (bias check). AI training data includes biased language patterns from the entire internet; output without bias review can include phrases that create legal exposure even if the manager would never use them in their own writing.
Specific Prompts You Can Adapt
The prompts below are templates you can adapt to your specific situation. Each one includes the structural elements (role, observations, output format, constraint) that produce useful AI output rather than generic content.
Three rules for prompt writing. First, always include the constraint "do not invent any details I have not provided." AI tools have a tendency to hallucinate plausible-sounding specifics; explicitly constraining this reduces the rate. Second, give specific structural requirements (length, sections, format) rather than asking AI to choose them; the output is more predictable and easier to edit. Third, include observations as bullet points rather than narrative paragraphs; AI processes structured input more reliably and is less likely to alter your facts.
For the broader practice of building specific phrases throughout reviews (which AI can help generate but the manager must verify), the employee review keywords guide covers categorized phrase banks that managers can use as starting points.
Tool Categories: Which AI to Use
The right AI tool depends on your context: privacy needs, integration with existing systems, budget, and team policies. Below are the main categories with honest assessment of each.
| Tool category | Best for | Caution |
|---|---|---|
| General AI assistants (ChatGPT, Claude, Gemini) | Drafting from notes, rephrasing, bias checks, pattern analysis | Public versions log conversations. Use anonymized inputs or paid privacy-protected tiers |
| Built-in AI in performance management platforms | Teams already on a performance platform; integration with existing review workflow | Locked into vendor; data lives in vendor systems; quality varies widely between platforms |
| AI review generators (free tools) | Single review draft when starting from scratch | Often produce generic templates; content may resemble other users' reviews; data privacy varies |
| Privacy-focused AI tools | Sensitive reviews where data must not be logged | Often more expensive; verify data handling claims with vendor; check enterprise data agreements |
| Custom prompts on company-approved AI | Standardized review process across team with consistent prompts | Requires upfront prompt development; train managers on consistent use |
The pattern: there is no universal best tool. Public general AI assistants are flexible and accessible but have privacy trade-offs. Built-in performance platform AI offers integration but vendor lock-in. Privacy-focused tools cost more but protect sensitive content. The right choice depends on what you optimize for. For most small businesses, a privacy-protected tier of a general AI assistant with anonymized inputs (Employee A, Project X) provides the best balance of flexibility and privacy.
The Risks of AI-Assisted Reviews
Honest discussion of the risks is essential. Most articles on AI for performance reviews either ignore the risks or treat them as so significant that AI use is irresponsible. Both framings are wrong. The risks are real but mitigatable; understanding them is the first step to managing them.
| Risk | What it means | Mitigation |
|---|---|---|
| Bias amplification | AI training data includes existing biased language patterns. AI-generated reviews can perpetuate stereotypes (gendered descriptors, age-coded language, 'cultural fit' phrasing) that managers might not notice if they accept output without editing | Always read AI output specifically for protected-characteristic adjacent language. Replace any flagged phrases with observable behavior |
| Hallucinated specifics | AI tools invent plausible-sounding specifics: invented metrics, fictional customer names, fabricated quotes. These can appear authoritative and become legal exposure if disputed | Verify every metric, name, date, and quote against your actual evidence before saving the review |
| Generic content masking poor evaluation | AI produces structurally correct reviews even when the manager has not done the actual evaluation work. The review looks complete but contains no real assessment | If you cannot describe specifics in your own words, you are not ready to write the review yet, regardless of what AI produces |
| Privacy and data leakage | Public AI tools log conversations. Pasting employee names, sensitive feedback, and performance issues into public tools creates data exposure | Use anonymous descriptors (Employee A, Project X). Use privacy-focused or company-approved AI tools. Document your AI use policy |
| Legal exposure under disparate impact analysis | EEOC has indicated that algorithmic decision-making tools used to make or inform employment decisions are subject to disparate impact analysis. Heavy AI reliance in reviews could expose the employer to claims if patterns emerge | Document your independent judgment and editing. AI assists; the manager decides. Retain notes showing human review of every AI output |
| Loss of manager learning | Writing reviews is one of the highest-leverage management practices. Managers who delegate to AI miss the development that comes from writing carefully about each direct report | Use AI to reduce mechanical writing time, not to skip the thinking work. The thinking is the development; the writing is just the artifact |
| Employee perception issues | Employees increasingly recognize AI-generated content. A review that feels AI-written damages trust, even if the manager had genuine input | Personalize aggressively. Add specifics only you would know. Read aloud and edit until it sounds like you, not like a template |
The pattern across these risks: each one is mitigated by deliberate process, not by avoiding AI entirely. Bias is mitigated by editing for protected-characteristic adjacent language. Hallucinations are mitigated by verifying specifics. Privacy is mitigated by anonymized inputs or privacy-focused tools. Legal exposure is mitigated by documented human judgment. Manager learning is preserved by using AI for mechanical work, not thinking work. Employee perception is managed by personalization and disclosure. The risks are real; the mitigations are also real.
Legal Considerations Small Business Owners Miss
The legal landscape around AI in employment decisions is evolving rapidly. The EEOC has indicated that algorithmic decision-making tools used to make or inform employment decisions are subject to disparate impact analysis under Title VII of the Civil Rights Act. State and local laws (such as New York City's automated employment decision tool law) add additional requirements. Small business owners using AI for reviews need to understand the basic compliance landscape.
| Legal area | What it means | Practical action |
|---|---|---|
| Disparate impact analysis | If AI-assisted reviews produce different outcomes for protected groups, the employer can be liable under Title VII even without intentional discrimination | Document independent human judgment. Audit review patterns periodically. Apply consistent standards across all employees |
| Algorithmic decision-making | EEOC treats AI tools that make or inform employment decisions as 'selection procedures' under Title VII. Heavy AI reliance in compensation, promotion, or termination decisions creates exposure | Use AI for writing assistance only. Documentation, decisions, and judgment stay with the manager |
| Vendor liability | Employers can be liable for AI tools developed by third parties. 'The vendor said it was safe' is not a defense | Ask vendors specifically what they have done to test for adverse impact. Document the answer. Audit periodically |
| Data privacy | Performance content typically includes sensitive personal information. Public AI tools that log conversations create data privacy exposure | Use anonymized inputs in public tools. Use privacy-focused tools for sensitive content. Document your data handling policy |
| Disclosure to employees | Some jurisdictions (NYC, Illinois Biometric Privacy Act, EU AI Act) require disclosure to employees when AI is used in employment decisions | Check applicable state and local laws. Default to transparency with employees regardless of legal requirement |
| Reasonable accommodation | AI tools may inadvertently disadvantage employees with disabilities. ADA requires reasonable accommodation regardless of AI use | Audit AI output for adverse effects on employees with known accommodations. Provide alternatives where AI creates barriers |
Three rules for legal protection when using AI. First, document your independent judgment. The strongest defense in any algorithmic decision claim is showing that the manager exercised judgment and the AI assisted writing only. Second, apply standards consistently across all employees regardless of AI use. The strongest defense in disparate impact claims is showing that everyone was treated the same way. Third, audit periodically. Look at review patterns across your team to identify whether AI use is producing different outcomes for different groups.
Detecting Bias in AI-Generated Content
AI training data includes biased language patterns from the entire internet, including stereotyped descriptions of different demographic groups. AI-generated reviews can perpetuate these biases without the manager noticing if output is accepted without editing. Bias detection is one of the highest-leverage editing practices.
| Biased language pattern | Common AI output | Replace with |
|---|---|---|
| Gendered descriptors for similar behavior | 'Bossy,' 'aggressive,' 'soft-spoken,' 'emotional,' 'mature' | 'Direct,' 'assertive,' 'measured,' 'thoughtful,' 'experienced' |
| Age-coded language | 'Energetic,' 'mature,' 'fresh perspective,' 'set in their ways' | 'Maintains consistent productivity,' 'demonstrates sound judgment,' 'brings new approaches,' 'consistent in their approach' |
| Family-status references | 'Despite family commitments,' 'works late despite obligations' | Remove family references entirely. Focus on work output and quality |
| 'Cultural fit' phrasing | 'Strong cultural fit,' 'fits well with our culture' | 'Collaborates effectively with team,' 'contributes to team norms,' 'supports team goals' |
| National origin/accent references | 'Speaks well for someone with their background' | 'Communicates clearly in written and verbal formats' |
| Disability adjacent | 'Reliable despite their condition,' 'overcomes their challenges' | 'Delivers on commitments consistently,' 'achieves results' |
The rule: read every AI-generated review specifically for these patterns before saving. The 60 seconds of bias review per review is the difference between AI-assisted reviews that protect against legal exposure and AI-assisted reviews that create it. If you find these patterns appearing in AI output, the AI tool is doing what AI tools do: surfacing biases from training data. The fix is editing, not avoiding AI; the same patterns appear in human writing that has not been bias-checked.
Gallup research on managers consistently finds that the manager-employee relationship is the strongest predictor of engagement; how managers handle AI in the relationship matters as much as the underlying tools.
Common Mistakes Small Business Owners Make
The mistakes below appear consistently across small businesses adopting AI for performance reviews. All are avoidable once you understand the underlying patterns.
The pattern across these mistakes: treating AI as a substitute for management work rather than as an assistant for the writing portion of management work. AI cannot create context, judgment, or relationship; it can only restructure what the manager has already developed. Managers who use AI to skip the development part of management produce thin reviews regardless of how polished the output looks. The fix for most AI mistakes is reasserting the manager's primary role: collect evidence, evaluate, edit, decide. AI accelerates the writing; humans do everything else. Work Institute research on retention consistently finds that the quality of feedback drives retention; AI-polished but generic reviews fail this test even when the documents look complete.
What Stays Human in the AI-Assisted Review
The clearest framework for AI use in performance reviews: identify what AI does and what humans do, and never confuse the two. Below is the practical division.
| What AI does | What humans do |
|---|---|
| Structures bullet points into prose | Collects bullet-point observations from the period |
| Rephrases vague language to specific | Determines whether observations are accurate and complete |
| Translates technical to accessible language | Decides what observations matter and which to include |
| Suggests alternative framings for difficult feedback | Decides what feedback to give and how to deliver it |
| Analyzes patterns across multiple reviews | Decides what the patterns mean and what to do about them |
| Drafts goals based on stated development areas | Decides which development areas matter and which goals to pursue |
| Flags potentially biased language | Decides whether language is biased in this specific context |
| Translates to other languages | Decides what should be communicated in the conversation |
The pattern: AI handles transformation and analysis of existing material; humans handle judgment, evaluation, and decision. Reviews that respect this division are AI-assisted. Reviews that violate it are AI-substituted, and they produce the problems described throughout this article: generic content, hallucinated specifics, biased language, legal exposure, and broken trust with the team.
For the conversation that follows the written review (which AI cannot conduct), the performance review guide covers the 9-stage delivery script that turns written feedback into actual behavior change.
For the broader practice of feedback that lands across performance situations, the employee feedback guide covers feedback delivery techniques that complement AI-assisted writing in 1:1s and informal conversations.
Telling Your Team You Use AI
Disclosure is one of the most underrated practices in AI use for performance reviews. Most managers either skip it (assuming no one will notice) or over-engineer it (writing formal AI policies before they are needed). The right approach is direct conversation: tell the team you use AI to assist with structure and writing, that the substance and judgment are yours.
| Approach | How it lands | When to use |
|---|---|---|
| No disclosure | Damages trust if discovered (which is increasingly likely as AI fingerprints become recognizable) | Never. The risk of discovery exceeds any benefit |
| Brief verbal disclosure | 'I use AI to help structure first drafts; the observations and decisions are mine' | Most small businesses; covers the essential ethical ground |
| Written AI policy | Formal document covering allowed use, privacy, decision-making boundaries | Teams over 25 people; companies in regulated industries; teams that have raised concerns |
| Policy plus training | Document plus session on responsible AI use across the team | Teams scaling AI use to multiple managers |
| Periodic transparency updates | Quarterly note on what AI use is producing, what is not working, what is changing | Teams adopting AI broadly; useful for building trust over time |
The single most important thing about disclosure: do it before AI use becomes invisible. Teams that learn after the fact that their manager used AI without telling them experience the discovery as a trust violation, regardless of how reasonable the AI use was. Teams that are told upfront usually accept AI use as a normal management practice. The disclosure is essentially free; the secrecy is genuinely expensive.
Where AI for Performance Reviews Is Going
The AI landscape for performance management is evolving rapidly. Three trends matter for small business owners thinking about AI use over the next 1-3 years.
First, AI capabilities are improving fast but not uniformly. Drafting and rephrasing have improved dramatically over 2024-2026; evaluation and judgment have not. The pattern is likely to continue: AI gets better at the mechanical work, stays bad at the judgment work. The framework in this article (AI assists writing, humans make decisions) should remain stable even as the tools improve.
Second, regulatory pressure is increasing. EEOC guidance, state laws (NYC AEDT, California, Illinois), and emerging federal frameworks are creating compliance requirements around AI in employment decisions. Small businesses that adopt deliberate AI processes now have less retrofitting work later. The investment in process discipline pays back as regulation tightens. OPM's performance management framework provides a federal-government reference for how structured practices integrate with technology.
Third, employee expectations are shifting. Employees increasingly recognize AI-generated content and have opinions about its use in evaluating their work. Disclosure norms are tightening; teams that handle AI use transparently build trust, teams that handle it opaquely lose it. The trajectory favors the small businesses that establish good practices now over the ones that defer the conversation. SHRM's research on organizational employee development consistently finds that perception of fair treatment drives engagement; how AI factors into that perception will increasingly matter.
How FirstHR Fits
The honest disclosure: FirstHR is not a dedicated AI performance review platform. We do not have a built-in AI review generator, AI scoring system, or AI-driven decision tools. What we do have is an AI Onboarding Wizard and the operational HR foundations (employee profiles, document management, org charts, onboarding workflows) that most small businesses need. AI use in performance reviews, when you adopt it, will live in your AI tool of choice (ChatGPT, Claude, Gemini, or built-in AI in performance platforms), not in dedicated FirstHR software.
That said, AI use for performance reviews works better when the underlying people operations are working. A manager using AI to write reviews on top of broken onboarding will spend most of the AI-assisted writing time compensating for unclear expectations the new hire never had. A manager using AI on top of consistent onboarding, clear documented roles, and structured employee profiles will produce AI-assisted reviews that actually drive growth. FirstHR exists to handle the operational HR foundation at flat-fee pricing ($98/month for up to 10 employees, $198/month for up to 50), so that owners can focus on the higher-impact work of evaluating employees well and writing reviews that matter.
For the foundation that makes performance reviews possible, the onboarding best practices guide covers what determines whether new hires are set up to be reviewed meaningfully.
For the broader management foundation that AI sits on top of, the people management guide covers running a small team without enterprise overhead.
Frequently Asked Questions
Can AI write performance reviews?
AI can produce structured first drafts of performance reviews when given specific observations as input, but it cannot evaluate performance independently or replace the manager's judgment. Used well, AI saves 60-80% of the writing time on the structural work while the manager focuses on substance: adding specifics, removing generic phrases, and ensuring the review reflects actual performance. Used poorly, AI produces generic template content that fails to drive behavior change. The manager's editing is what turns AI output from template to feedback.
Is it ethical to use AI for performance reviews?
Yes, with three boundaries. First, transparency with the team: tell them you use AI to assist with structure, that the substance and judgment are yours. Second, the manager makes evaluation decisions, not the AI: ratings, promotions, terminations stay human. Third, privacy: do not paste employee names and sensitive details into public AI tools that log conversations. AI as a writing assistant is similar to using a grammar checker or template; AI as the actual evaluator is ethically and legally problematic.
What are the best AI tools for performance reviews?
There is no single best tool; the right choice depends on your context. General AI assistants (ChatGPT, Claude, Gemini) are flexible for drafting, rephrasing, and pattern analysis but their public versions log conversations. Built-in AI in performance management platforms offers workflow integration but locks data into the vendor. Privacy-focused tools cost more but protect sensitive content. Free AI review generators produce generic content that often feels templated. For most small businesses, a privacy-protected tier of a general AI assistant with anonymized inputs works well.
Can ChatGPT write a performance review?
ChatGPT can write a structured first draft of a performance review when given specific observations as input. The output requires significant editing to add specifics ChatGPT could not know, remove generic phrases that creep in from training data, and verify any specific claims. ChatGPT-only reviews without manager editing produce template content that employees recognize and that fails to drive behavior change. Use ChatGPT for the mechanical writing; use your own judgment for the substance.
Should I tell my team I used AI to write their reviews?
Yes. Trust matters more than the time savings. If team members discover undisclosed AI use, the trust damage exceeds any efficiency gain. The honest framing works: 'I used AI to help structure first drafts; the observations, evaluation, and final editing are mine.' Most teams accept this. Few accept secrecy. Disclosure also creates accountability for managing AI well, which produces better reviews than secret use.
What are the risks of using AI for performance reviews?
Seven main risks. Bias amplification (AI training data includes biased language patterns). Hallucinated specifics (AI invents plausible-sounding details). Generic content masking poor evaluation. Privacy and data leakage (public AI tools log conversations). Legal exposure under EEOC disparate impact analysis. Loss of manager learning (writing reviews develops management capability). Employee perception issues (AI-written content damages trust if discovered). All risks are mitigatable, but they require deliberate process: editing, verification, privacy protection, and disclosure.
Can AI determine performance ratings or recommend promotions?
No, and using AI for these decisions creates legal exposure. EEOC guidance treats algorithmic decision-making tools used to make or inform employment decisions as subject to disparate impact analysis under Title VII. AI used to score employees, recommend promotions, or determine terminations could expose the employer to claims if patterns emerge. The legally and operationally safe use is AI assists writing; humans make decisions. Document your independent judgment to demonstrate human review.
How do I write a good prompt for performance review AI?
A good prompt has four parts. First, the role and review type ('annual review for a customer success manager'). Second, your specific observations as bullet points (8-15 examples with specifics). Third, the structural requirements ('2-3 sentence summary, 3-5 strengths with situation-behavior-impact, 2 development areas'). Fourth, the explicit constraint: 'Do not invent any details I have not provided.' The constraint is critical because AI tools sometimes hallucinate plausible-sounding specifics. Verify everything in the output against your actual evidence.
How much time does AI save on performance reviews?
Used well, 60-80% time savings on the writing portion of the review. A review that takes 90 minutes to write from scratch typically takes 15-25 minutes when AI handles the structural draft and the manager edits aggressively. The time savings comes from the mechanical writing work, not the thinking work. The manager still needs to collect evidence, evaluate the employee, verify specifics, and conduct the conversation. AI does not save time on the parts that actually matter; it saves time on the parts that are mechanical.
Can AI detect bias in my performance reviews?
Yes, partially. AI is reasonably good at flagging gendered descriptors (bossy, soft-spoken), age-coded language (mature, energetic), 'cultural fit' phrasing, and family-status references. AI can also analyze patterns across multiple reviews to identify whether you are consistently softer or harsher with certain employees. AI is less good at detecting subtler bias patterns (whose work gets praised vs. whose gets credited to circumstance). Use AI bias detection as a first pass, not as the final check; have a colleague review for nuanced patterns AI misses.
Should small business managers use AI for performance reviews?
Most should, with the boundaries described in this guide. Small businesses have less administrative bandwidth than enterprise HR teams; AI is a force multiplier for the writing portion. The constraints are the same as for any size: AI assists writing, manager makes decisions, edit aggressively, verify specifics, protect privacy, disclose to the team. Small businesses that follow these constraints get the time savings without the risks. Small businesses that skip the constraints get template content that fails to drive behavior change and may create legal exposure.
What is the difference between AI-assisted and AI-written reviews?
AI-assisted reviews use AI for drafting, rephrasing, or analysis while the manager retains evaluation, editing, and decision-making. AI-written reviews delegate the evaluation work to the AI, with the manager simply approving or lightly editing the output. The first approach is a productivity tool; the second is a delegation of judgment that AI is not capable of exercising. Reviews that read as AI-written damage trust with the team and may create legal exposure. Reviews that are AI-assisted save time without these trade-offs.