Amazon starts using AI to summarise reviews and save shoppers time
How much time do you spend reading all those reviews? Well, now AI can summarise them all for you.
Cite or link to this article
Griffin, M. (2023) 'Amazon starts using AI to summarise reviews and save shoppers time', 311 Institute, 29 July. Available at: https://www.311institute.com/amazon-starts-using-ai-to-summarise-reviews-and-save-shoppers-time/ (Accessed: 25 October 2026).
Amazon, that giant E-Tailer we all use, has announced that they’re hoping to make product choice easier for customers by using Artificial Intelligence (AI) to summarise all the reviews a product has had to save customers time so they don’t need to read all the comments. This AI review consolidation has been rolled out to select customers in the US, and is expected to be expanded. And as we see AI abused and individuals finding new ways to con and twist it to their own ends what can go wrong …
“We obsess over helping customers feel confident in their purchase decisions, even if that decision is not to make the purchase,” said Amazon. “Last year alone, 125 million customers contributed nearly 1.5 billion reviews and ratings to Amazon stores, that’s 45 reviews every second, making reviews at Amazon an incredible resource for customers.”
It also makes Amazon an incredible source to build the vocabulary of the company’s large language model.
The response to the new rollout has been positive, however, particularly regarding Amazon’s new click-feedback set of attributes such as “Stability”, “Performance” and “Ease of Use,” which will be displayed below the summarized customer review. The online shopping giant, which has previously come under fire for its business practices, believes the AI-generated review feature will be able to bypass bots and other “unauthentic”, read fake, customer commentary.
“We welcome authentic reviews, whether positive or negative, but strictly prohibit fake reviews that intentionally mislead customers by providing information that is not impartial, authentic, or intended for that product or service,” Amazon stated. “We continue to invest significant resources to proactively stop fake reviews. This includes machine learning models that analyze thousands of data points to detect risk, including relations to other accounts, sign-in activity, review history, and other indications of unusual behavior, as well as expert investigators that use sophisticated fraud-detection tools to analyze and prevent fake reviews from ever appearing in our store.
“The new AI-generated review highlights use only our trusted review corpus from verified purchases, ensuring that customers can easily understand the community’s opinions at a glance.”
Amazon CEO Andy Jassy told shareholders earlier this year that generative AI will be a “big deal” for the company, with much more to come. And despite concerns, Amazon believes that the “authentic” way AI will gather reviews negates the concerns about how trustworthy the new model is.
“Our Community Guidelines help both our machine learning models and our human moderators keep the community safe and the reviews relevant, while allowing customers to express themselves and their opinions with as much personal expression as possible,” Amazon said in a statement. “We believe this leads to a richer, better, and more trustworthy set of reviews. Customers who share their opinions appreciate this, and those that read them do too.”
The response to the new model has been largely positive, and fortunately nothing like the company’s 2018 move into artificial intelligence.
Source: Amazon
What this means for your organisation
The summary becomes the shop window.
Shoppers who read one summary instead of hundreds of reviews will judge products on a few lines. Whoever writes that summary shapes demand. Brands should monitor how AI describes their products on major platforms.
Review data is a strategic asset.
A huge store of customer reviews feeds both the summaries and the platform's language model. Firms with rich customer feedback hold training data rivals lack. Retailers should organise and use their own review data.
Fake reviews remain a risk.
The feature is meant to resist bots and inauthentic reviews, but people keep finding ways to game AI. A distorted summary misleads far more shoppers than one fake review. Trust and safety teams should test summaries for manipulation.
Three questions for your next leadership meeting
- How does AI currently describe our products on major marketplaces?
- Are we using our own customer feedback to improve products?
- How would we spot manipulation in summarised reviews?
The trends and technologies behind this story
From the 311 Starburst, the 311 Strategic Intelligence Unit’s map of the technologies and trends shaping the next 50 years.
Analysis by the 311 Strategic Intelligence Unit, from Matthew Griffin’s article and 311 data. First published on Fanatical Futurist.
FAQ
Why does this matter?
How much time do you spend reading all those reviews? Well, now AI can summarise them all for you.
About the author
Matthew Griffin Founder, President and Futurist, 311 Institute
Matthew Griffin is a multi-award winning Futurist and expert in Disruption and Innovation, Geopolitics, Leadership, and Technology, who NASA have described as a "walking encyclopaedia of the future" and a "futurist Polymath."
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Matthew Griffin is a multi-award winning Futurist and expert in Disruption and Innovation, Geopolitics, Leadership, and Technology, who NASA have described as a "walking encyclopaedia of the future" and a "futurist Polymath." 17-time best selling author of the "Codex of the Future" series, Matthew is the Founder, President and Futurist of the 311 Institute, a global Futures and Deep Futures advisory firm working with royal households, world leaders, G7, G20, and G77 governments, NGOs, and multi-national mid and mega cap firms to help them explore, shape, and lead the next 50 years of business and society.
An award-winning YouTube creator with over a million followers, with an unrivalled global reach and impact, Matthew is a highly sought-after international keynote speaker, lecturer, and mentor who collaborates with global leaders through the United Nations Alliance of Civilizations (UNAOC) and United Nations General Assembly (UNGA) to shape pivotal initiatives such as the UN’s AI for Humanity program, the United Nations Conference of the Parties (UN COP), and the World Economic Forum in Davos.
As the former Global Head of Cloud, National Security, and Enterprise Sales for companies including Atos, Dell-EMC, and IBM, Matthew has a proven track record of building multi-billion dollar business units and turning failing divisions into market leaders. His ability to identify, analyse, and communicate the implications of hundreds of emerging technologies and trends is unparalleled, and his insights are trusted by many of the world’s most respected organisations, including ABB, Accenture, Adidas, AON, ARM, BCG, Centrica, Citi, Coca-Cola, Dentons, Deloitte, Dow Jones, EY, Google, KPMG, Lego, Legal & General, LinkedIn, Microsoft, PepsiCo, Qualcomm, RWE, Samsung, Siemens AG and Siemens Energy, T-Mobile, UBS, VISA, Walmart, Workday, Worldpay and many others.
Regularly featured in the global media including the AP, BBC, Bloomberg, CNBC, Discovery, Forbes, Khaleej Times, Telegraph, TIME, ViacomCBS, WIRED, and the WSJ, Matthews mission is to help organisations create a fair and sustainable future whose benefits are shared by everyone irrespective of their ability, background, or circumstances.
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Sources and further reading
- company’s 2018 move reuters.com
- Amazon improves customer reviews with generative ai aboutamazon.com