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A strategic guide to enterprise AI transformation.
Amazon has introduced Alexa for Shopping, combining its Rufus shopping chatbot with Alexa+ across its app, website, and Echo Show devices.
The assistant can answer product questions, compare items, track prices, and support shopping reminders. It can also handle scheduled shopping actions and eligible automated purchases.
The company said Alexa for Shopping combines Rufus’ product expertise with Alexa+’s personalised assistant context. Amazon said Rufus helped more than 300 million customers in 2025 research, compare, and buy products.
GeekWire reported that Amazon is retiring the Rufus name from its shopping interface, while Rufus will continue to power parts of the experience behind the scenes.
GeekWire also reported that Amazon CEO Andy Jassy said Rufus monthly active users rose more than 115%, while engagement increased nearly 400% year over year.
Alexa for Shopping is available through the Amazon Shopping app, Amazon’s website, and Echo Show devices. The feature is rolling out to US customers. Signed-in Amazon customers can use it for free, without a Prime membership, Echo device, or Alexa app.
Amazon reported US$426.3 billion in North America net sales and US$161.9 billion in international net sales in 2025. Amazon also reported online stores and third-party seller services as separate revenue categories in its 2025 annual report.
The assistant allows customers to ask shopping-related questions through Amazon’s main search bar instead of using a separate chatbot window. Users can ask for product recommendations or purchase history. They can also ask for advice related to specific shopping needs.
Examples shared by Amazon include questions such as “What’s a good skincare routine for men?” and “When did I last order AA batteries?” Amazon said the assistant uses information from its platform to answer these questions.
Amazon said Alexa for Shopping uses information from a customer’s Amazon activity and Alexa interactions. That includes shopping history, browsing, purchases, and conversations. Amazon said the information is used to recommend products and support shopping actions.
Alexa for Shopping can compare products side by side and provide AI-generated summaries on product pages. It can also show AI-generated overviews in search results with category information.
Alexa for Shopping can monitor price drops for selected items for up to one year. Customers can view a full year of price history on product detail pages or by asking the assistant.
The assistant can create shopping guides for larger purchases. These guides compare product features and prices. They also include reviews from Amazon and the web.
Amazon said customers can use the assistant to set scheduled shopping actions, including restocking household items. Amazon said the assistant can also handle birthday reminders and gift suggestions.
Scheduled actions can also be tied to conditions. For example, the assistant can add an item to the cart if it reaches a target price and has not been purchased within a set period.
The assistant can search past orders and add frequently purchased items to a customer’s cart through conversational prompts.
Amazon said customers can view and update personal details used by Alexa for Shopping. These details can include family members, pets, interests, and dietary needs.
Alexa for Shopping can also surface products from other online stores through Shop Direct. For eligible products, Amazon said its Buy for Me agentic AI feature can complete purchases using a customer’s primary address and payment method.
Amazon is also adding full-store shopping access to Echo Show. Users can browse, search, and shop using voice, touch, or both.
The Echo Show shopping experience is available for Alexa+ customers on Echo Show 15 and Echo Show 21, with support for other devices to follow.
Amazon also cited AI investments in its first-quarter 2026 results. The company said free cash flow fell to US$1.2 billion for the trailing 12 months. It attributed the decline mainly to a US$59.3 billion increase in property and equipment purchases, primarily reflecting AI investments.
Rajiv Mehta, Amazon’s vice president of conversational shopping, said the assistant can carry customer preferences, past purchases, and conversations across phones, laptops, and Echo devices.
Users can access the assistant by updating the Amazon Shopping app and selecting the Alexa icon in the bottom navigation bar. On the desktop, the feature appears at the top of the screen.
(Photo by Anirudh)
See also: Google tests Remy AI agent for Gemini as focus turns to user control
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The term “physical AI” is largely attributed to Jensen Huang, the CEO of NVIDIA, to refer to AI’s evolution away from the digital screen into the real world.You may have seen it: a humanoid robot moonwalking across a stage to Michael Jackson’s “Billie Jean” before slipping several times on a set of steps and lying motionless. The video, filmed in Shenzhen, China, has taken social media by storm this week.The scene may well augur a future in which robots imitate, perform and work alongside humans. That’s because it is an early example of what many experts are calling the next phase of the artificial intelligence boom: physical AI. What is Physical AI?The term “physical AI” is largely attributed to Jensen Huang, the CEO of NVIDIA, to refer to AI’s evolution away from the digital screen into the real world. In a recent company blog post, Huang suggested that the “ChatGPT moment for general robotics is just around the corner.”Physical AI refers to any AI system that is designed to interact with the environment, typically through specialized sensors, said Yanzhi Wang, professor of electrical and computer engineering.Examples of physical AI systems extend beyond robotics, and include medical devices, autonomous vehicles, smart manufacturing systems and AI-powered drones. What does it mean for AI to interact with its environment? Experts describe that interaction in terms of a system’s ability to “perceive, reason and learn” from the environment around it. These AI systems would be able to learn the laws of physics with some degree of autonomy and adaptability. Sarah Ostadabbas, associate professor of electrical and computer engineering, said that in addition to “sensing and learning from the world,” physical AI systems should, in theory, also be able to “act independently” based on the information they take in from their surroundings.But to bridge the gap between the simulated or virtual world and the real world, “You need to reason about what you have seen, or what you have perceived,” she added. “So this reasoning component is really important.”Ostadabbas explained that the reasoning model relies heavily on text, or an understanding of language. Language-based systems reason from descriptions and patterns rather than through direct interaction with the physical world. “We hope that this reasoning component in these systems eventually is derived from the actual physics of the world,” she said. At Northeastern University’s Physical AI Research Initiative, or PAIR, Ostadabbas and her colleagues are attempting to establish a framework that would help guide the development of physical AI systems. One emerging template for physical AI systems is the “vision-language-action” model, or VLA, Wang said. A vision-language-action model describes any system that unifies visual perception and language processing to act and make decisions. Early models, such as NVIDIA’s GR00T N1 and Google DeepMind’s RT-1, are designed with the aim of helping robots interpret their surroundings. What are some of Physical AI’s applications?Physical AI is already being deployed in some sectors, including manufacturing, Wang said. The most recognizable examples include robotic arms that assemble products on factory lines, and autonomous warehouse robots that help transport inventory, sort packages and perform other rote tasks with minimal human intervention. Unlike traditional industrial robots, which are typically programmed to repeat the same fixed motions, physical AI systems are designed so they can adapt to changing conditions, identify objects and independently navigate spaces. Wang noted that physical AI systems could transform manufacturing and other industries by allowing machines to operate in less predictable environments, where they can learn and adapt. A conversation with Northeastern’s President Joseph E. AounHow far does Physical AI still have to go?Physical AI is still largely theoretical in nature. Ostadabbas said there are many hurdles to actualizing the kind of physical AI systems she and her colleagues are attempting to define and conceptualize. One difficulty, she said, is the dynamic and unpredictable nature of the real world. The visual and physical data these systems would rely on is often “unclean” or “dirty,” referring to the ways environments shift or contain obstacles and other unexpected variables. Safety is also another concern. Physical AI systems operating around people must avoid causing harm and endangering trust, Ostadabbas said, which raises a whole slew of technical and legal questions. China’s dancing robot seemed harmless enough. But in other contexts, such systems could.“How can we make sure that this action is safe, trustworthy, verifiable and something that is robust?” Ostadabbas said. “That is the final pillar in our framework.”Wang thinks it may soon be implemented at scale, provided innovation and development continue at its current pace.“I think it will become more mainstream, but it is still a far way to go,” Wang said. “But … based on the current progress of this generation of tools, maybe after two or three years there will be a big breakthrough.”
Next-generation AI assistants being developed in the Apple ecosystem and by chipmakers like Qualcomm, but early reports suggest they are being designed with limits in place.Tom’s Guide has described early versions of these assistants as capable of navigating apps, carrying out bookings, and managing tasks in services. For instance a private beta agentic system completed tasks like booking services or posting content in apps. In one test, it moved through an app workflow and reached a payment screen before asking the user for confirmation.AI agents are being built with approval checkpoints. Sensitive actions, especially those tied to payments or account changes, require user confirmation before they are completed. The “human-in-the-loop” model lets the system prepare an action, but leaves approval to the user. Research linked to Apple’s AI work has explored ways to ensure systems pause before taking actions users did not explicitly request.Banking apps already require confirmation for transfers. The same idea is now being applied to AI-driven actions in multiple services.Limits and controlA control layer comes from restricting what the AI can access. Rather than providing the system full access to apps and data, businesses are establishing limits, such as which apps the AI can interact with and when actions can be triggered.In practice, this means the AI may be able to draft a purchase or prepare a booking, but not finalise it without approval. It also means the system cannot move freely in all services unless it has been granted permission.According to Tom’s Guide, the facility is for privacy. If data remains on the device, it eliminates the need to send sensitive information to external servers.In areas like payments, AI systems are expected to work with partners that already have strict rules in place. In one reported example, payment providers’ services are being integrated to provide secure authentication before transactions are completed, though such safeguards are still under development. The existing systems act as an additional layer of oversight. They can set transaction limits or require extra verification.Much of the discussion around AI governance has focused on enterprise use. That includes areas like cybersecurity and large-scale automation. The consumer side introduces a different challenge and companies must design controls that work for everyday users. That means clear approval steps and built-in privacy protections.Autonomy with boundariesAs AI gains the ability to carry out actions, the risks become greater as errors can lead to financial loss or data exposure.By placing controls at multiple points, including approval and infrastructure, companies are trying to manage those risks.The approach may shape how agentic AI develops in the near term. Rather than aiming for full independence, companies appear focused on controlled environments where the risks can be managed.(Photo by Junseong Lee)See also: Agentic AI’s governance challenges under the EU AI Act in 2026Want to learn more about AI and big data from industry leaders? Check out taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and co-located with other leading technology events. Click here for more information.AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here.