For online shopping, consumers are increasingly receiving answers and purchase suggestions through chatbots and AI-powered search, without needing to click through to traditional review sites. On major shopping days, referral traffic from AI-driven tools to retailers has skyrocketed, highlighting a shift in how people make buying decisions.

For decades, review sites have served as trusted guides: Which robot vacuum cleans best? What’s the easiest soundbar to set up? AI can answer all of these questions instantly, often cutting out the need to visit a publisher’s site. That leaves professional reviewers scrambling: if AI can summarize, compare, and recommend, what role do review sites really play?

The reality is nuanced. Consumers still crave depth, credibility, and context. While AI can deliver quick answers, it can’t yet replicate comprehensive side-by-side testing, long-form analysis, or editorial expertise that signals trust. Review sites that provide unique insights, such as proprietary testing, data-driven scoring, and in-depth comparisons, can still earn a spot in the buying journey. In fact, AI often cites journalistic content as its source, showing there’s value in well-researched reviews.

But the business model is under pressure. Where affiliate links once drove revenue from clicks, AI’s “Buy Now” widgets bypass traditional referral paths, offering direct purchase options. 

Observers say review sites may need to rethink their partnerships, considering licensing their content to AI platforms or offering branding recommendations to maintain relevance and revenue.

Kurt Muehmel, Head of AI Strategy at Dataiku, says AI can sidestep some human biases that are nearly impossible to eliminate. “There’s a famous study of parole boards where approval rates dropped from 65% at the start of a session to nearly zero right before lunch. Same cases, completely different outcomes, based entirely on when they happened to be heard. AI doesn’t get tired or hungry. It processes the hundredth application with the same attention as the first.”

AI can inherit biases from its training data. Amazon’s recruiting tool, trained on predominantly male hires, penalized resumes containing the word “women’s” and was ultimately scrapped.

WHERE HUMAN JUDGEMENT STILL MATTERS 

When stakes are high and decisions are irreversible, human oversight is still critical. Muehmel points to the UK Post Office scandal as a cautionary tale: faulty accounting software led to over 900 wrongful convictions of postal workers for theft. “The pattern that actually works is using AI to handle volume and flag exceptions while humans make the final call on consequential decisions,” he says. He notes that lower-stakes processes, like spam filtering, can be fully automated, but decisions involving hiring, loans, or medical diagnoses require human judgment at the end, not just at the start.

On transparency, Muehmel emphasizes that AI doesn’t have to be fully explainable to be trustworthy. “The model itself may be a black box, but that’s not necessarily the problem people think it is. What matters is whether the AI can point to its sources and explain its reasoning in ways that an expert can validate.” 

Good AI review tools include benchmark datasets, human spot-checks, and agreement scores across multiple reviewers, ensuring that conclusions can be verified even if the inner workings remain opaque.

There are ethical and practical limits. Context is everything: AI performs best where the criteria are clear, and decisions are reversible. “It struggles where the situation calls for nuance, empathy, or ethical reasoning that depends on understanding the full picture of someone’s circumstances,” Muehmel says. The real risk, he adds, is mission creep—organizations expanding AI’s role beyond what it was validated to do, like letting a resume-sorting tool influence hiring decisions or risk scores affect sentencing across states.

Despite the risks, Muehmel says the tangible benefits of AI reviews are numerous. “None of this is a reason to avoid AI review. The benefits in coverage and consistency are real and measurable. It’s a reason to match the level of oversight to the stakes involved, and to be intentional about where you draw the line.”

AI VS. HUMAN REVIEWERS

Meanwhile, Marisa Kamall, founder of Dubai-based women’s leadership community GAIA, says that AI won’t replace human reviewers entirely. Her initiative, ChatGPShe, is a movement where women professionals demystify AI, learn collaboratively, and shape technology with inclusivity and ethics at its core. She sees AI replacing aggregation and surface-level summarizing, not lived experiences. “It can scan thousands of reviews, spot patterns, and optimize for speed. What it cannot do is sit in the room, read the power dynamics, sense what’s missing, or challenge a dominant narrative. Reviewers who add original thinking, context, and courage will become more valuable, not less,” Kamall says .

Moreover, Kamall says the role of journalists and reviewers is now at a crossroads. Those who simply repackage information will struggle. Those who bring judgment, investigation, and perspective will thrive. The shift moves from reporting what happened to explaining why it matters, transforming access into insight and speed into trust.

ETHICS AND LIMITATIONS 

AI reflects the data it is trained on, Kamall warns. If historical reviews have privileged certain voices, AI risks reinforcing those biases. It can also carry false authority, sounding confident even when wrong. “Accountability becomes blurred when errors occur, and over-reliance on the same tools risks flattening opinion and originality. AI should amplify the human voice, not replace it, especially those of people who have historically been underrepresented. The future belongs to people who can think critically, challenge systems, and use AI consciously rather than blindly. If you are not asking these questions, then you’re already behind,” she says.

Despite the risks, both Muehmel and Kamall emphasize AI’s tangible benefits: speed, consistency, and the ability to handle large volumes of work. The key is pairing AI with human judgment, insight, and ethics. 

AI reviewers are reshaping industries and consumer experiences, but the most effective systems integrate humans where nuance, context, and judgment matter, ensuring technology amplifies fairness rather than replaces it.