Cloudflare CEO Matthew Prince has clarified that its recent global outage was caused by an internal configuration error and a latent software flaw rather than any form of cyber attack.

A Major Disruption Across Large Parts Of The Internet

The outage of internet infrastructure company Cloudflare began at around 11:20 UTC on 18 November 2025 and lasted until shortly after 17:00, disrupting access to many of the world’s most visited platforms. For example, services including X, ChatGPT, Spotify, Shopify, Etsy, Bet365, Canva and multiple gaming platforms experienced periods of failure as Cloudflare’s edge network returned widespread 5xx errors. Cloudflare itself described the disruption as its most serious since 2019, with a significant portion of its global traffic unable to route correctly for several hours.

Symptoms

The symptoms were varied, ranging from slow-loading pages to outright downtime. For example, some users saw error pages stating that Cloudflare could not complete the request and needed the user to “unblock challenges.cloudflare.com”. For businesses that rely on Cloudflare’s CDN, security filtering and DDoS protection, even short periods of failure can stall revenue, block logins, and create customer support backlogs.

Given Cloudflare’s reach (serving a substantial share of global web traffic), the effect was not confined to one sector or region. In fact, millions of individuals and businesses were affected, even if they had no direct relationship with Cloudflare. That level of impact meant early scrutiny was intense and immediate.

Why Many Suspected A Major Cyber Attack

In the early stages, the pattern of failures resembled those of a large-scale DDoS campaign. Cloudflare was already dealing with unusually high-volume attacks from the Aisuru botnet in recent weeks, raising the possibility that this latest incident might have been another escalation. Internal teams initially feared that the sudden spike in errors and fluctuating recovery cycles could reflect a sophisticated threat actor pushing new attack techniques.

The confusion deepened when Cloudflare’s independent status page also went offline. Since it is hosted outside of Cloudflare’s own infrastructure, this coincidence created an impression, inside and outside the company, that a skilled attacker could be targeting both Cloudflare’s infrastructure and the third-party service used for its status platform.

Commentary on social media, as well as early industry analysis, reflected that uncertainty. With so many services dropping offline at once, it seemed easy to assume the incident must have been caused by malicious activity or a previously unseen DDoS vector. Prince has acknowledged that even within Cloudflare, the team initially viewed the outage through that lens.

Prince’s Explanation Of What Actually Happened

Once the situation stabilised, Prince published an unusually detailed account explaining that the outage originated from Cloudflare’s bot management system and the internal processes that feed it. In his statement, he says the root of the problem lay in a configuration change to the permissions in a ClickHouse database cluster that generates a “feature file” used by Cloudflare’s machine learning model for evaluating bot behaviour.

What??

It seems that, according to Mr Prince, the bot management system assigns a “bot score” to every inbound request and to do that, it relies on a regularly refreshed feature file that lists the traits used by the model to classify traffic. This file is updated roughly every five minutes and pushed rapidly across Cloudflare’s entire network.

It seems that, during a planned update to database permissions, the query responsible for generating the feature file began returning duplicate rows from an additional schema. This caused the file to grow significantly. Cloudflare’s proxy software includes a strict limit on how many features can be loaded for performance reasons. When the oversized file arrived, the system attempted to load it, exceeded the limit, and immediately panicked. That panic cascaded into Cloudflare’s core proxy layer, triggering 5xx errors across key services.

Stuck In A Cycle

Not all ClickHouse nodes received the permissions update at the same moment, meaning that Cloudflare’s network then entered a cycle of partial recovery and renewed failure. For example, every five minutes, depending on which node generated the file, the network loaded either a valid configuration or a broken one. That pattern created the unusual “flapping” behaviours seen in error logs and made diagnosis harder.

However, once engineers identified the malformed feature file as the cause, they stopped the automated distribution process, injected a known-good file, and began restarting affected services. Traffic began returning to normal around 14:30 UTC, with full stability achieved by 17:06.

Why The Framing Matters To Cloudflare

Prince’s post was clear and emphatic on one point i.e., that this event did not involve a cyber attack of any kind. The language used in the post, e.g., phrases such as “not caused, directly or indirectly, by a cyber attack”, signalled an intent to remove any ambiguity.

There may be several reasons for this emphasis. For example, Cloudflare operates as a core piece of internet security infrastructure. Any suggestion that the company suffered a breach could have wide-ranging consequences for customer confidence, regulatory compliance, and Cloudflare’s standing as a provider trusted to mitigate threats rather than succumb to them.

Also, transparency is a competitive factor in the infrastructure market. By releasing a highly granular breakdown early, Cloudflare is signalling to customers and regulators that the incident, though serious, stemmed from internal engineering assumptions and can be addressed with engineering changes rather than indicating a persistent security failure.

It’s also the case that many customers, particularly in financial services, government, and regulated sectors, must report cyber incidents to authorities. Establishing that no malicious actor was involved avoids triggering those processes for thousands of Cloudflare customers.

The Wider Impact On Businesses

The outage arrived at a time when the technology sector is already dealing with the operational fallout of several major incidents this year. For example, recent failures at major cloud providers, including AWS and Azure, have contributed to rising concerns about “concentration risk”, i.e., the danger created when many businesses depend on a small number of providers for critical digital infrastructure.

Analysts have estimated that the direct and indirect costs of the Cloudflare outage could actually reach into the hundreds of millions of dollars once downstream impacts on online retailers, payment providers and services built on Shopify, Etsy and other platforms are included. For small and medium-sized UK businesses, downtime during working hours can lead to missed orders, halted support systems, and reduced customer trust.

For regulators, this incident looks like being part of a trend of high-profile disruptions at large providers. Sectors such as financial services already face strict operational resilience requirements, and there is growing speculation that similar expectations may extend to more industries if incidents continue.

How Cloudflare Is Responding

Prince outlined several steps that Cloudflare is now working on to avoid similar scenarios in future. These include:

– Hardening ingestion of internal configuration files so they are subject to the same safety checks as customer-generated inputs.

– Adding stronger global kill switches to stop faulty files before they propagate.

– Improving how the system handles crashes and error reporting.

– Reviewing failure modes across core proxy modules so that a non-essential feature cannot cause critical traffic to fail.

It seems that Cloudflare’s engineering community has welcomed the transparency, though some external practitioners have questioned why a single configuration file was able to impact so much of the network, and why existing safeguards did not prevent it from propagating globally.

Prince has acknowledged the severity of the incident, describing the outage as “deeply painful” for the team and reiterating that Cloudflare views any interruption to its core traffic delivery as unacceptable.

What Does This Mean For Your Business?

Cloudflare’s account of the incident seems to leave little doubt that this was a preventable internal failure rather than an external threat, and that distinction matters for every organisation that relies on it. The explanation shows how a single flawed process can expose structural weaknesses when so much of the internet depends on centralised infrastructure. For UK businesses, the lesson is that operational resilience cannot be outsourced entirely, even to a provider with Cloudflare’s reach and engineering reputation. The incident reinforces the need for realistic contingency planning, multi-vendor architectures where feasible, and a clear understanding of how a supplier’s internal workings can affect day-to-day operations.

There is also a broader industry point here. For example, outages at Cloudflare, AWS, Azure and other major players are now becoming too significant to dismiss as isolated events. They actually highlight weaknesses in how complex cloud ecosystems are built and maintained, as well as the limits of automation when oversight relies on assumptions that may not be tested until something breaks at scale. Prince’s emphasis on transparency is helpful, but it also raises questions about how often configuration-driven risks are being overlooked across the industry and how reliably safeguards are enforced inside systems that evolve at speed.

Stakeholders from regulators to hosting providers will surely be watching how quickly Cloudflare implements its promised changes and how effective those measures prove to be. Investors and enterprise customers may also be looking for signs that the underlying engineering and operational processes are becoming more robust, not just patched in response to this incident. Prince’s framing makes clear that this was not a compromise of Cloudflare’s security perimeter, but the reliance on a single configuration mechanism that could bring down so many services is likely to remain a point of scrutiny.

The most immediate implication for customers is probably a renewed focus on the practical realities of dependency. Even organisations that never interact with Cloudflare directly were affected, which shows how embedded its infrastructure is in the modern web. UK businesses, in particular, may need to reassess where their digital supply chains concentrate risk and how disruption at a provider they do not contract with can still reach them. The outage serves as a reminder that resilience is not just about defending against attackers but preparing for internal faults in external systems that sit far beyond a company’s control.

Researchers have uncovered a privacy weakness in WhatsApp that allowed the confirmation of 3.5 billion active accounts simply by checking phone numbers.

A team from the University of Vienna and SBA Research found that WhatsApp’s contact discovery system could be queried at high speed, letting them generate and test 63 billion numbers and confirm more than 100 million accounts per hour. When a number was recognised, the app returned publicly visible details such as profile photos, about texts, and timestamps, with 57 per cent of users showing a profile picture and nearly 30 per cent displaying an about message.

Meta said only public information was accessible, no message content was exposed, and the researchers deleted all data after the study. It added that new rate-limiting and anti-scraping protections are now in place and that there is no evidence of malicious exploitation.

Security experts warned that the incident shows how phone numbers remain a weak form of identity, making large-scale scraping and profiling possible. They stressed that metadata, even without message content, can still be valuable to scammers or organised cyber groups.

Businesses can reduce risk by limiting the personal information staff make visible on messaging apps, reviewing privacy settings, and ensuring employees understand how scraped contact details may be used in targeted attacks.

Exowatt, a Sam Altman-backed energy startup, plans to revolutionise AI data centre energy consumption by harnessing the power of concentrated solar energy stored in high-temperature hot rocks to provide round-the-clock, dispatchable electricity.

A Viable Alternative to Traditional Grid-Based Power?

Co-founded by Hannan Happi, who has a background in energy innovation and technology development, Exowatt aims to address the AI industry’s growing demand for sustainable and reliable power. With this in mind, the company’s flagship product, the Exowatt P3 system, is designed to solve the solar energy industry’s most significant challenge, i.e., providing consistent, 24-hour electricity. By capturing solar energy, storing it as heat, and converting it into electricity when required, Exowatt aims to deliver a viable alternative to traditional grid-based power, which is not always reliable or sustainable for energy-hungry industries like AI.

How Exowatt’s P3 System Works

The Exowatt P3 is a modular system that functions differently from conventional solar panels. Instead of converting sunlight directly into electricity, the system uses concentrated solar power (CSP) technology, a method that has been around for decades but has yet to achieve widespread commercial success.

Heats A Brick And Blows Air Over It

As the company says on its website, “Exowatt delivers power on demand by capturing and storing solar energy in the form of high-temperature heat and converting it into dispatchable electricity as needed.”

In order to do this, the system works by using fresnel lenses (a type of light-focusing lens) to concentrate sunlight into a tight beam. This beam heats a special brick inside a box, which serves as a thermal battery. A fan blows air over the brick, carrying the heat to a Stirling engine, a heat engine that converts thermal energy into mechanical energy, which is then used to generate electricity. The P3’s thermal storage capacity allows it to provide dispatchable power, meaning it can supply electricity whenever needed, even when the sun isn’t shining. This addresses the intermittent nature of traditional solar energy, which can only generate power when there is direct sunlight.

Can Store Heat For 5 Days

The P3 units can store heat for up to five days, ensuring continuous operation. Also, the units are modular, meaning they can be scaled depending on the energy requirements of the user. Exowatt has designed the system to be easy to deploy, requiring minimal maintenance and a small physical footprint compared to other renewable energy solutions.

Why It Matters for the AI Industry

The AI sector is growing at an unprecedented rate, with increasing energy demands driven by the need to train complex models and power massive data centres. For example, according to estimates, data centre energy consumption will increase by 150 per cent by 2030, with AI models expected to be one of the largest contributors to this demand. Traditional energy grids, however, are not equipped to handle this surge in consumption, especially as the need for clean and reliable energy grows.

Exowatt’s approach could, therefore, significantly reduce reliance on fossil-fuel-powered backup generators, which many data centres currently use to ensure uptime during power shortages. These backup systems, often powered by gas, are not only expensive but contribute to carbon emissions, directly contradicting the industry’s shift towards more sustainable practices.

The Exowatt P3 promises a cleaner, more sustainable alternative by providing a reliable power source that does not depend on the grid. This is particularly important for companies building data centres in remote areas, where access to stable grid power may be limited or non-existent. By positioning itself as a dispatchable energy solution, Exowatt gives AI companies a way to meet their energy needs while maintaining their commitment to sustainability.

What Makes Exowatt So Different?

Unlike traditional solar power systems, which require battery storage to hold electricity until it is needed, Exowatt’s thermal storage system offers a number of advantages. For example, the P3 system’s reliance on heat storage rather than electric battery storage avoids many of the issues associated with lithium-ion batteries, such as their reliance on rare-earth minerals, the environmental impact of battery disposal, and the rapid cost reductions in solar panel production which have outpaced improvements in battery technology.

Exowatt’s system is designed to work in sunnier regions where traditional solar systems are most effective. Happi notes that Exowatt’s P3 units can be deployed near new data centre developments, often located in sunny areas, thus overcoming grid limitations. The modular nature of the system means that power capacity can be increased simply by adding more P3 units, making it a scalable solution.

Pricing and Availability

Exowatt appears to be aggressively scaling production, having raised a total of $140 million in funding to date, including a recent $50 million extension to its Series A round. The company has set a target price of $0.01 per kWh, which would position its energy cost below current prices for many types of renewable power. To achieve this, Exowatt hopes to manufacture 1 million units per year, which would bring production costs down and make it competitive with other forms of renewable energy.

While the technology is still in its early stages, Exowatt has already secured a backlog of 90 GWh in demand, with customers in the AI data centre and energy developer sectors. As production ramps up, Exowatt plans to roll out the P3 system to large-scale data centre projects in regions that are sun-rich, making it an ideal fit for AI companies seeking reliable, sustainable power solutions.

Other Companies in the Space

It should be noted here that Exowatt is not the only company exploring the potential of thermal storage and concentrated solar power. Several other firms are pursuing similar solutions, though each has its own approach and focus. These include:

– Vast Energy, which is developing modular concentrated solar thermal power systems designed to deliver clean, dispatchable energy for utility-scale and industrial applications. Their CSP v3.0 technology captures the sun’s energy and stores it as heat, allowing for efficient and reliable power delivery when needed, similar to Exowatt’s P3 system.

– Heliogen, which focuses on solar thermal technologies and aims to replace fossil fuels in industrial applications. Their systems use concentrated solar power to generate high-temperature heat, which can be used to produce electricity or replace gas in manufacturing processes.

– SolarReserve and eSolar, which are earlier players in the CSP field, though their commercial activities have slowed in recent years. These companies have contributed to the development of solar thermal technology, but they are less active or have shifted their focus due to challenges with scalability and cost.

While Exowatt’s approach is similar to these companies, its focus on modular, scalable systems tailored for AI and high-density computing environments could set it apart, particularly if it can prove its technology is both cost-effective and adaptable to different locations and energy demands.

Broader Implications and Challenges

Exowatt’s technology looks as though it has the potential to disrupt the renewable energy and data centre industries, offering a way to tackle AI’s increasing energy demands sustainably. For example, for data centre operators, the system presents an opportunity to reduce their carbon footprint while ensuring that power is always available, even during peak demand periods or at night.

However, Exowatt faces some stiff competition. Photovoltaic solar panels and lithium-ion batteries have come down in price rapidly in recent years, making them more attractive options for many companies. Also, concentrated solar power projects have faced challenges in the past due to high upfront costs and the need for specific geographical conditions. Exowatt will need to prove that its system can scale effectively and remain cost-competitive as production increases.

One of the key challenges for Exowatt’s system is land use. For example, while the P3’s efficiency is comparable to traditional photovoltaic solar panels, the system requires a significant amount of land to scale up production, particularly in regions with less sunlight. This may limit the system’s appeal in areas where land is scarce or where sunlight is insufficient. The large land footprint required to deploy large numbers of P3 units could also pose logistical challenges, especially in urban areas where space is at a premium. These factors are likely to be crucial for Exowatt to overcome if it aims to scale effectively and meet the growing demand for sustainable AI infrastructure power.

Looking Ahead

As Exowatt continues to scale its operations, it could well become a leading player in the transition to sustainable energy for AI data centres. For example, with major backers like Andreessen Horowitz and Sam Altman, the company has the resources to expand rapidly, and its innovative approach to solar energy storage could set a new benchmark for the energy demands of AI.

However, its success looks likely to depend on whether it can overcome the inherent challenges of large-scale deployment and prove that its technology can compete with existing energy solutions. If Exowatt can deliver on its promises, it could reshape the way data centres, and indeed, entire industries, think about their energy needs in the age of artificial intelligence.

What Does This Mean For Your Organisation?

Exowatt’s P3 system seems to offer a compelling vision for how AI data centres can meet their energy needs sustainably, addressing the increasing demand for 24/7 power in an industry heavily reliant on high-performance computing. The system’s ability to store solar energy as heat and convert it into dispatchable electricity sets it apart from traditional solar and battery solutions, offering a reliable and cleaner alternative to fossil-fuel-powered backup systems.

However, while the P3 system presents a promising solution for reducing data centre emissions, its success could hinge on overcoming several challenges. Scaling production efficiently and managing the land footprint required for deployment are two critical obstacles. Although Exowatt has the potential to deliver energy at an exceptionally low cost, competing technologies, such as photovoltaic solar and lithium-ion batteries, have quickly become more cost-competitive. Exowatt will need to demonstrate that its system can meet these challenges, particularly in less sunny regions where land availability and sunlight are limited.

Looking to the future, Exowatt’s modular, scalable approach could make it an attractive option for AI companies looking to ensure reliable power while maintaining sustainability goals. For UK businesses, particularly those involved in AI, data centres, and energy-intensive industries, the success of Exowatt could signal a new era of energy independence and sustainability. If Exowatt can continue to scale and prove its technology’s viability, it could reshape the energy landscape for data centres globally, offering UK companies a reliable and affordable path to meet the growing demands of the digital age.

Despite the hurdles, Exowatt’s ambition and innovative approach may be precisely what’s needed to meet the unique energy challenges of the AI sector, paving the way for a more sustainable and resilient energy future.

You can now take your TEAMS chats to the next level by inviting Microsoft Copilot to the chat. You now have all the power of the AI at your fingertips, without having to leave a TEAMS chat and you can collaborate like never before … fascinating stuff !

[Note – To Watch This Video without glitches/interruptions, It may be best to download it first]

Did you know you can turn any message into a dyslexia‑friendly view that reads it aloud and even translates it on the fly? It’s perfect for listening to long emails while you’re busy with something else.

To read an email with Immersive Reader:

– Open the email you want to view.
– Click the three‑dot “More Actions” menu (⋯) at the top of the message and choose Show in Immersive Reader (or go to View → Immersive Reader).
– Use the toolbar that appears to adjust line spacing, pick a theme, or hit the Read Aloud button to have Outlook read the text to you.

Why it helps – It lets you speed‑read or listen to lengthy emails without leaving Outlook, and the dyslexia‑friendly layout makes reading easier for everyone.

Give it a try next time you have a long thread to catch up on!

Two new launches from Hero and Google show how everyday digital tasks are moving towards full automation, with prompt writing and online shopping now handled largely by AI rather than users.

A Clear Move Towards Automated Digital Tasks

AI tools have become familiar, yet many still require people to know how to phrase prompts or navigate long product pages. Now, Hero, a rising productivity startup, and Google, are both targeting these pain points with new systems designed to remove the need for manual prompting and repetitive shopping tasks altogether. It seems their latest releases aim to streamline everyday digital admin using context, automation and conversational interactions.

Who Is Hero And What Does The App Do?

Hero is a consumer productivity platform built by former engineers who have previously worked on augmented reality interfaces. The company has grown rapidly, reporting more than 300,000 users and a 4.9 rating on the Apple App Store. Its core idea is to replace multiple apps with a single daily assistant. For example, the Hero app brings together calendars, reminders, events, to-dos, notes, habit tracking, shared lists and weather updates in one continuous feed.

Users can create tasks, organise schedules, coordinate with partners or colleagues, and receive “Can’t Miss” notifications that can sound even when a phone is in silent mode. There is also a built-in grocery system that categorises items automatically and connects to Instacart ordering. Hero promotes itself as a tool to “run your life in one place”, aiming to simplify the routines and small decisions that tend to fragment across apps.

Hero’s Autocomplete SDK Now Writes Prompts For You!

It seems that Hero is now extending this philosophy to AI prompts. The company has introduced a new autocomplete SDK (Software Development Kit) that predicts and fills in the parameters an AI system will need to complete a task. This means users can begin with a short instruction, and the SDK will fill in all the other relevant fields and details, allowing the user to complete as much or as little as they like before submitting the request.

For example, starting a prompt with “Book a flight” can automatically produce fields such as departure and destination airports, dates, times and airline choices. The same applies to creative tools, where the SDK can suggest common parameters such as style, location or camera angle for image or video generation.

Uses Multiple Models Together

Hero says that the SDK uses multiple models working together to understand user intent and assemble the information the system needs. The company says the autocomplete experience reduces the number of messages required to complete an action, cutting time and effort for users and reducing computing costs for businesses that run AI-powered services.

Background In AR

It seems this idea most likely comes from the founders’ background in augmented reality, where screen space is limited and long free-form prompts are impractical. Building clear, structured actions from short starting phrases became part of their design thinking, and the new SDK continues that approach by making prompts more like guided workflows.

Funding

Hero recently secured 3 million dollars in additional funding and is already testing the autocomplete technology inside its own app, where users will be able to rely on the assistant to propose structured prompts for tasks such as finding meeting times, organising shared plans or identifying key details from photos and screenshots.

Google Redesigns Online Shopping With Agentic AI

While Hero is automating prompt writing, Google is now automating shopping. For example, the company has just announced a major upgrade to its AI shopping features across Search and the Gemini app, aimed at simplifying product discovery, comparison, stock checking and purchase.

In Google’s own announcement about the features, the company said shopping should “feel a lot more natural and easy”, noting that browsing can be enjoyable but the administrative steps often are not. The new tools are designed to let people describe what they want in everyday language while the AI organises the information needed to make decisions.

For example, through AI Mode in Search, users can now ask conversational questions such as “cosy jumpers in warm autumn colours” and receive a visual selection of products, prices, reviews and inventory information. If they are comparing items such as skincare products, AI Mode can switch to structured comparison views that highlight key differences and insights from reviews.

Google says these features are powered by Google’s Shopping Graph, which contains over 50 billion product listings, with around 2 billion refreshed every hour. This gives Google’s AI near real-time awareness of stock levels and pricing across retailers.

Shopping Inside Gemini And Automated Purchasing

Google is also making the same capabilities available inside the Gemini app. For example, instead of brief suggestions, Gemini can now respond with complete lists of ideas, curated recommendations, comparison tables and links to buy. All of this is driven by Shopping Graph data, and it is designed to help users move from brainstorming to browsing in a single conversational thread.

One of the most significant additions is agentic checkout. With its help, users can track the price of an item they want, set a maximum budget and ask Google to buy it automatically using Google Pay if the price drops within their range. Google says the system will always request confirmation before completing a purchase and will only use payment details the user has already authorised.

Early rollout partners include retailers such as Wayfair, Quince, Chewy and selected Shopify stores.

Google’s AI Can Call Shops For You

Google has also introduced a tool that uses AI to call physical shops directly. For example, when people search for certain items “near me”, they may see an option marked “Let Google Call”. Selecting this enables Google’s AI to call local stores, check availability, ask about pricing and confirm whether any offers are available. The results are summarised in a follow-up message.

This feature is built on Google’s Duplex calling technology. Merchants who receive calls hear a clear disclosure that the caller is an AI acting on behalf of a customer. Google says shops can opt out at any time, and calls are limited to avoid unnecessary disruption.

Benefits

These developments highlight several benefits for consumers, business users and retailers. For example, for individuals, Hero’s autocomplete SDK removes the need to learn how to write prompts, lowering the barrier to using AI tools. Google’s agentic shopping features reduce time spent checking prices, comparing products or phoning shops, which can support faster decision-making during busy periods such as the holiday season.

For businesses, the real appeal is efficiency and cost reduction. Hero’s SDK shortens user interactions, which reduces the number of model calls required, lowering server costs. Google’s automated shopping tools can bring back hesitant buyers, help retailers reach local customers and streamline the customer journey from discovery to purchase.

There are also broader implications for professionals and business users. For example, automated prompts can speed up research tasks, planning, scheduling and customer support workflows. Automated shopping and stock checking can simplify procurement, reduce manual checks and help teams stay within budgets more easily.

Challenges And Criticisms

Despite all the obvious benefits, it should be noted that there are some also important considerations. For example, the influence of automated suggestions raises questions about visibility and fairness. If autocomplete systems prioritise certain parameters or products, users may only see a narrow band of options. This is particularly sensitive where sponsored listings appear alongside AI-generated recommendations.

Also, privacy is a central concern. Hero brings together large volumes of personal information, including calendars, notes, reminders, grocery lists and shared tasks. Google’s agentic shopping tools collect signals about purchase timing, price sensitivity and product intent. Both companies provide assurances about data handling, yet users may still question how much insight these systems can gain over daily routines and buying habits.

There are also challenges for smaller businesses. Retailers that do not integrate with larger shopping ecosystems may become less visible inside AI-driven recommendations, placing pressure on them to engage with platforms they might otherwise avoid.

It’s also worth noting that this shift from advisory to agentic AI means systems are not only suggesting options but taking actions on behalf of users. This means that the level of comfort people feel with automated purchasing, prompt completion and real-world calling is likely to shape how widely these features are adopted and how deeply automated digital life becomes in the years ahead.

What Does This Mean For Your Business?

The combined direction of these developments suggests that everyday digital tasks are becoming less about active decision making and more about approving actions that AI systems have already prepared. Hero’s approach shows how this can simplify workflows that would normally require careful prompt writing, while Google’s agentic shopping tools reveal how much of the purchase journey can be handled without the user having to search, compare or chase information themselves. The result is a growing expectation that these systems will assemble the context, gather the details and present the decisions in a form that requires minimal input.

This transformation has particular relevance for UK businesses. For example, teams that once spent time on procurement checks, research tasks or repetitive customer queries may find that agentic systems remove much of the manual effort, freeing staff to focus on higher value work. The same applies to smaller organisations that struggle with capacity peaks during busy seasons. Automated comparison, stock checking and structured prompting could help these companies stay responsive even with limited resources, although they will need to weigh this against concerns about visibility and reliance on third party platforms.

There is also a wider shift for retailers, service providers and other stakeholders who will now find themselves interacting not only with customers but with AI agents acting on their behalf. Features such as automated shop calls or price triggered purchases may change how demand appears, how stock is managed and how customer expectations evolve. This presents opportunities to reach customers more consistently, though it also places new pressure on businesses to ensure their information remains accurate across the systems that feed these AI tools.

It’s likely, therefore, that the next stage of adoption is really going to depend on trust. For example, users will need confidence that the suggestions offered are balanced, that privacy safeguards work as intended and that automated actions remain transparent. Businesses will want reassurance that they are not disadvantaged if they choose not to integrate with large ecosystems. What is clear from both launches is that AI is moving steadily from a tool that responds to instructions to one that anticipates what users want and prepares the steps in advance. How people and organisations respond to this will determine how quickly these ideas actually become part of everyday life or not.