Target is moving image search, review summaries and conversational assistants from experiments into mainstream shopping flows. These changes matter because they shift where discovery happens — from text search and category pages to photos, summarized review themes and assistant-built carts — and that alters priorities for brands, measurement and product teams.
What Target launched and where it appears
Over the past few months Target has introduced several customer-facing AI features across its app and services. Photo Search, launched in August, lets shoppers tap the app’s search camera to upload or snap an image and find visually similar products without typing a description. The feature is live in the Target app.
In June, Target added AI Review Insights, which parses customer reviews and groups commonly discussed attributes — for apparel, examples include comfort, fit and breathability — so shoppers can see the themes most relevant to their decision. Target says the tool helps reduce what it calls “decision fatigue” while improving conversion and add-to-cart activity, though specific performance figures were not disclosed.
Target has also upgraded personalization for repeat and unfinished purchases. Buy Again, accessible from the app’s main navigation, surfaces past purchases and frequently ordered items; Target reports that improvements to recommendations and deal selection have driven “double-digit conversion growth year-over-year.” Continue Shopping, introduced last fall, brings shoppers back to recently viewed items and suggests alternatives and offers. Target says both experiences are producing double-digit conversion rates and incremental add-to-carts.
Organization and external partnerships
These product moves sit alongside organizational shifts: Target appointed Chandhu Nair as its first chief AI officer, effective Aug. 24. CEO Michael Fiddelke framed the hire as a way to “accelerate how we harness the power of AI to create better guest experiences and unlock new capabilities across our business,” signaling a formal, cross-functional AI mandate.
Target is also testing touchpoints outside its properties. The company said it has partnerships with OpenAI and Google Gemini as it explores what it calls “agentic commerce” — commerce experiences where external AI platforms can act as intermediaries between shoppers and retailers. Traffic from those external AI platforms remains a small share of visits today, but Target reports growth from those sources at more than 3.5 times the industry rate year over year.
Shipt’s conversational play
Target-owned Shipt launched Ask Shipt on Sept. 9, an AI assistant in the Shipt app and on Shipt.com that can turn prompts, recipes and meal photos into a shopping cart. Members can review, swap or edit suggested items before checkout. Shipt says Ask Shipt works across a marketplace of more than 100 retailer partners and has also experimented with shopping through ChatGPT and Anthropic’s Claude.
Shipt is preparing Shared Lists as well — collaborative lists multiple users can edit in real time, then convert into a cart with one click. Shipt’s chief growth and strategy officer, Katie Stratton, described Ask Shipt as a way to shorten the path from inspiration to purchase by bringing “people‑centric AI directly into the shopping journey.”
Practical implications for brands, agencies and product teams
Target’s feature set changes the discovery stack. Visual search elevates the importance of product imagery and consistent visual attributes; review summarization shifts some emphasis from raw review counts to clearly surfaced themes; conversational assistants create new entry points that bypass traditional category and search pages.
Concretely, teams should prioritize three areas. First, optimize imagery: supply high-quality, taggable photos and consider multiple angles and contextual shots so visual-search models can match products reliably. Second, shape review signals: monitor review themes and address common concerns in product copy and Q&A so summary outputs present favorable, accurate themes. Third, rethink measurement and attribution: instrument assistant-driven flows and image-origin referrals separately so you can detect and credit conversions coming from those new paths.
What to watch next
Expect competitors to test similar features and watch how consumer behavior evolves on external AI platforms. Key indicators that will determine whether these capabilities become table stakes: sustained conversion lift with verifiable metrics, broader traffic volumes from AI assistants and standardized ways to attribute purchases that originate from image searches or conversational prompts. For now, the immediate task for brands and commerce teams is practical: make assets discoverable by image and theme, and update analytics to capture assistant-driven discovery.
