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Circle K gets serious about retail media

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By Mitchell Parton

Circle K is more openly promoting and expanding its retail media network, Full Circle Media, as a way for advertisers to reach shoppers across its convenience stores, fuel pumps and digital channels in the U.S., Canada and Europe.

Last year, Circle K named Joell Robinson director of retail media for Circle K. She was previously senior director of retail media at Giant Eagle, where she helped build the Leap retail media network. The company began using the Full Circle Media branding in February. Before then, the company had been relatively quiet about its retail media business. Full Circle Media reaches more than 6,600 locations in the U.S. and delivers up to 200 million monthly impressions, according to Circle K.

“Circle K has had a network; it just hasn’t been out there,” Robinson told Modern Retail. “We’ve had suppliers participating, but [we are] really building up our capabilities, and our measurement, and the scalability of the whole thing, and making sure it’s built to last.”

Robinson said that previously, the company had been focused on retail media through its proprietary upsell screens, called Lift, in stores. The company has 13,200 screens installed at 6,600 locations, in addition to in-store digital walls. She describes the Lift screens as working similarly to sponsored searches online: When a customer purchases one candy bar, for example, it may tell them there’s a “two for $3” deal, indicating they should pick up another one.

“But there’s so much more,” Robinson said, adding that the retailer also has screens at the fuel pumps as well as placements on its app and emails. “We have a lot of great properties. In the past, we had kind of played in those areas, but [had] not necessarily put a formal offering around it,” she said.

Circle K’s approach to building out its retail media network is a bit of an anomaly in the industry. Other retailers, including Giant Eagle, are only just beginning to add in-store screens for retail media and have instead focused more on building out more advertising functionality on mobile and the web. Robinson said Circle K instead has had a heavy in-store media presence and is now building more digital components. Circle K does not have an e-commerce website. “I had to shift my thinking a bit, because I was used to having sponsored search and a lot of online placements.”

Robinson said her team’s focus has been to create a retail media program that spans many different channels instead of just mainly in-store media. In May, the media network started offering social and off-site placements. “Focusing on the full journey is really, really important to us,” Robinson said. “What we’ve been doing is better utilizing some of the channels we have.”

Her focus has also been on working more with Circle K’s merchandising team. “When the organization is supporting, say, a certain promotion, we really try to work with our suppliers to make sure that we’re the megaphone on that promotion, and that people know about it,” Robinson said.

She said the company is also experimenting with closed-loop measurement, as well as incrementality measurement, in email. “The idea is to get to this all-encompassing, omnichannel, closed-loop measurement,” Robinson said. “It’s really aligning the things underneath to make sure that that is transparent and defensible.”

Robinson said her team has been trying to bring more measurement to the fuel pump, and has been testing ways to bring those customers into the store via the screen or through SMS or push notifications on their phones. Full Circle Media includes screens at more than 3,800 fuel pumps.

“We have so much traffic at the pump, and I think even if we could get a small percentage of that, it would be material,” she said. “That gives them just the opportunity to engage with us and engage with our customer service folks, which I think is where we win them.”

Robinson also plans to leverage Circle K’s loyalty program, Inner Circle, which offers discounts on fuel and beverages, among other perks. The loyalty program has grown to more than 5,000 stores in 34 states, and has 14.7 million members. Circle K just redesigned the program to reward customers based on how often they visit the store or the pump.

“Especially in retail media, having a strong loyalty program is really the foundation of what we’re trying to do,” Robinson said. “Our willingness and our enthusiasm around our loyalty program has been incredibly refreshing for me, and great to see from a retail media standpoint, because who I know I can measure. Who we know, we can engage with.”

Convenience-store retail media is still in the early innings in the U.S. Retail media analyst Andrew Lipsman, in his Media, Ads and Commerce newsletter, said C-store retail media networks are often an afterthought in the U.S. retail media conversation because the market is fragmented between too many different chains.

“In the quest for CPG brand dollars, they take a back seat to Amazon, Walmart, Kroger, Instacart and nearly a dozen others,” he wrote. He said outside of 7-Eleven’s Gulp Media Network — which reaches more than 13,000 stores across the country — he believes “for other C-store RMNs to achieve viability, the market will ultimately need to consolidate into an aggregated media network.”

Frank Beard, principal of Konbini Strategy, a marketing and PR firm for companies that serve the fuel and convenience-retail industries, said it’s a “no-brainer” for Circle K to want to invest more into retail media. “Convenience stores have a lot of traffic flow,” he said. “It’s a natural place to put an advertisement, especially when it comes to products that are sold in a convenience store.”

But he said retailers can’t lose sight of investing in the customer experience in favour of functions like retail media that are aimed at attracting advertisers rather than customers. “Retail media works when you have a compelling enough retail offer to drive people into the store in the first place.”

By Mitchell Parton

Sourced from ModernRetail

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From Tinder and Reddit to Amazon, unwanted AI images are freaking people out

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Users complain of ‘fake photos’ being created without their consent.

Amazon, Reddit and Tinder might not seem to have a huge amount in common, but all three are jumping on AI trend that many users find unfathomable. They’ve been pushing AI imagery on users without being asked for it.

This week, the dating Tinder app said it had suspended a controversial experimental feature that used AI to automatically edit some users’ profile photos. Meanwhile, sellers on Amazon and advertisers on Reddit complain of automatically generated fake product images.

It seems that whether you’re trying to sell groceries, video games or yourself, these three very different tech companies think their fake imagery will help you do it. But with consumers so wary of AI, why are they pushing it into use cases where it doesn’t belong?

In Tinder’s case, the latest controversy involved a ‘Photo Enhance’ feature. Some users found that the app automatically edited their profile photos to make them look clearer, brighter, or even to change their own physical features.

In the TikTok above, Tinder user Jeni Rubin describes her confusion when she saw her profile picture had been given “someone else’s teeth”. It’s like posting a Photo to social media and the platform automatically applying one of those controversial glamour filters . On an app whose whole purpose is to help people meet up with strangers, it felt a bit like they wanted to take catfishing in house.

Users were told their photo had been “enhanced” and they could undo the action if they wanted, but only after the photos were already live. They weren’t asked if they wanted to use the feature in the first place, and there appeared to be no way to opt out.

Tinder’s not the only app that’s been pushing unexpected AI image generation on users. One of the first examples that made headlines was Uber Eats way back in 2023 when it started adding fake images of business’s menu items. It was so bad that a New York pizzeria’s margheritas were depicted as pastry tarts.

That hasn’t put tech companies off. Over on Reddit this week (above), an indie game developer was surprised by their experience when they decided to pay for adverts for the first time. Reddit used their gameplay images to generate fake screenshots.

Adding the fake images to ads is optional, but the developer wonders why Reddit would generate them in the first place and whether some users might accept them unintentionally. Could some of those games people are dismissing as AI slop actually be decent titles whose marketing has been scuppered by Reddit’s ad platform?

“I didn’t prompt these. Reddit just generates a bunch of images for you without asking. I don’t know how Reddit thought this would provide value to advertisers,” the developer writes.

“As far as I can tell, they don’t seem to be automatically added to the media list the ad uses, but it still feeds your promotional artwork into the AI without your clear consent,” they add. “Then when going through ads setup pressing ‘ok’ on everything, [you’re] one accidental ‘add all’ misclick away from being used in your official ad campaign.”

Amazon Shop by Style AI

Amazon’s AI ‘shop by style’ feature (Image credit: Amazon)

On Wednesday, Amazon announced a new feature that will display AI-generated images of products based on users’ search queries. The idea is that fake photos will help consumers find what they’re looking for by helping to narrow down searches to real products that might resemble them.

I can see an argument for that. You might be looking for something made from a certain material and not know what it’s called, for example. But the feature could also add more confusion to a marketplace where it’s becoming harder to tell what’s real. Some Amazon sellers complain of AI-generated images being automatically added to their product listings.

“Am I able to opt out? I don’t like the pictures they generated, and they don’t even show the right appearance of my products. It may cause the confusion,” one person writing on Reddit fears.

They’re right to be concerned. Various online marketplaces are being taken over by AI-generated images. For some listings, fantasy AI ‘lifestyle images’ might be the only images provided. They might draw attention and increase initial click through, but are people going to buy a product if they can’t see what it really looks like? If they do, isn’t there more chance of negative reviews and returns?

Why companies are doing it

It’s common for tech companies to make new features opt out. Opt-in features tend to fail when they’re things that nobody asked for. But are these AI features intended to create value for the user or for the platforms themselves?

The precise reasons for the implementations might vary among platforms, but reducing user churn and disillusionment is probably one of the main objectives. People stop using Tinder and won’t buy a subscription if they get few matches. Blurry or poorly lit photos make that more likely, so by editing images, Tinder hopes people get more response and stay hooked on the app for longer.

On platforms like Reddit, AI-generated images make it easier for small businesses and everyday users with limited resources to create ads and spend money on the platform. Repeating the same images can lead to ad fatigue, causing users to scroll past, while AI-generated variations allow for more A/B rotation to see which gets the more clicks. More clicks mean more money for Reddit if you’re on Cost-Per-Click payment.

Amazon also sells per click advertising, but it also sells AI image generation as a product for advertisers. Even if it gives products the feel of dodgy knockoffs, the company has an interest in normalising use of the technology.

In every case, there could be benefits for the user too if they get more visualisations from prospective customers (or matches). The risk for the tech platforms is the continued negative public sentiment regarding AI, which probably isn’t being helped by companies pushing unrequested features that also drive up the cost of hardware.

By 

Joe is a regular freelance journalist and editor at Creative Bloq. He writes news, features and buying guides and keeps track of the best equipment and software for creatives, from video editing programs to monitors and accessories. A veteran news writer and photographer, he now works as a project manager at the London and Buenos Aires-based design, production and branding agency Hermana Creatives. There he manages a team of designers, photographers and video editors who specialise in producing visual content and design assets for the hospitality sector. He also dances Argentine tango.

Sourced from CREATIVE BLOQ

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Your billboard is becoming a search query. What happens when OOH triggers AI?

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For many years, OOH has been measured by what happens after someone sees a billboard: search, clicks and website traffic. But AI is changing what happens next. A billboard can now trigger a question in an answer engine, making the digital footprint behind a campaign just as important as the physical media itself. Here’s why the OOH-to-AEO journey matters.

For years, one of the clearest signs that an out-of-home advertising campaign had worked was a spike in search.

A billboard went live. People saw it. Someone searched the brand name. Branded search increased, website traffic followed and, suddenly, there was a digital trail showing that something happening in the physical world had created an online response.

The billboard is no longer the end of the journey

That relationship between OOH and search still matters.

But the way people find information is changing. Instead of typing a few words into Google, someone can ask ChatGPT, Gemini, Perplexity or another AI-powered search tool a much more complicated question and expect an answer immediately.

The journey can now look very different:

See billboard → ask AI → receive an answer → investigate brand → visit website → take action.

That creates a new question for OOH planners. What happens after someone notices the campaign?

What information will they look for and what will an answer engine tell them when they ask?

OOH creates the question. The digital ecosystem has to answer it

Outdoor advertising has traditionally been treated as a broadcast medium.

You buy the location, create the artwork, put the campaign live and measure reach, impressions, recall and perhaps a lift in search.

But a strong piece of OOH can create something more valuable than an impression: a question.

If a billboard makes a surprising claim, people may want to verify it. If it introduces a not-so-familiar brand, they may want to understand it.

If it shows an unusual price, statistic or comparison, they may want to know whether it is true.

That means the campaign does not necessarily finish when somebody walks past it. The physical creative can become the trigger for a digital information journey.

A York Street campaign offers a useful example

Our recent campaign on York Street in Leeds illustrates the principle. Two 48-sheet billboards were placed next to each other, contrasting an existing quoted price for the same site.

The message was deliberately simple enough to understand from the road, but provocative enough to create a second life online. People photographed the boards, publications picked up the story and the physical campaign became digital content in its own right.

It is the mechanism: a physical message created curiosity, curiosity created discussion and that discussion created a searchable information trail.

AEO is about being understandable

There is a temptation to treat answer engine optimization as simply the latest acronym marketers need to add to a presentation. That misses the more important point.

AEO is fundamentally about making an organization understandable to systems that retrieve and synthesize information.

Who are you? What do you actually do? Where do you operate? What do your services cost? What evidence supports your claims? Who has written about you? Are independent sources talking about you? Can an AI system find consistent information about your business across your website and the wider web?

These are not questions that can be solved simply by changing a meta title or adding an FAQ section. They require a broader digital footprint and clear, useful information.

The question becomes part of the campaign strategy

This creates an interesting opportunity for OOH. Instead of planning only for what the audience will see, planners can start thinking about what the audience might ask afterward.

A campaign could be designed backward: creative → likely question → search behavior → available information → AI answer → action.

That does not mean every billboard needs to contain a question or every campaign needs an elaborate digital strategy.

It means the likely second step in the consumer journey deserves consideration alongside location, reach, frequency and format.

Design the question, not just the impression

OOH planning already asks important questions: Where is the audience? How much reach do we need? What frequency are we looking for? Which formats fit the budget?

The next layer could be just as simple: What question might this campaign create? What will somebody search after seeing it? What might they ask an AI system? Where will they find the answer? Is that answer accurate, useful and credible?

That is a different way of thinking about outdoor advertising. It treats attention not as the end metric, but as the beginning of a wider journey.

The OOH-to-AEO pipeline

Physical attention: A person sees the billboard, bus, digital screen, taxi, airport advert or Underground campaign.

Curiosity: The creative creates a question, emotion or reason to investigate.

Search or AI prompt: The consumer searches the brand or asks an answer engine for more information.

Digital ecosystem: The consumer finds the company’s website, content, reviews, press coverage and other authoritative information.

AI-generated answer: An answer engine can now use that wider context when responding to the consumer.

Action: The consumer visits the website, makes an inquiry, buys something, visits a location or shares the campaign.

The billboard may actually be the first prompt

I do not think the billboard is becoming less valuable because people are spending more time with AI. There is a case for the opposite.

The more powerful AI search becomes, the more valuable real-world triggers may become. AI can answer questions, but it cannot put your brand physically in front of somebody walking down a high street.

A billboard can create the question that sends somebody to search or AI in the first place.

That is the opportunity.

The next generation of OOH campaigns will not just be designed to be seen. They will be designed with what happens afterward in mind: attention, curiosity, search, AI discovery, discussion and credibility.

The billboard may actually be the first prompt. What happens next is up to the rest of your digital ecosystem.

Sourced from The Drum

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Salesforce agents gain a runtime that pursues goals over weeks, not chats

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By Luis Rijo

Seven prebuilt agents cover sales, service, commerce and back office, with six generally available and outbound seller Hunter held in pilot until November.

In Short

Salesforce packaged seven ready-made AI agents, each built for one job such as customer support, IT help desk, online shopping, outbound sales, supply chain, inbound lead qualification and customer experience workflows. Six can be bought and switched on immediately; the outbound sales agent, Hunter, stays in pilot until November 2026 and is the first to run on new plumbing that keeps an agent working toward an objective over weeks instead of ending when the chat window closes. For marketing and revenue teams, the practical shift is that pipeline generation, chat commerce and service deflection now arrive as configured products rather than as components to assemble.

Seven agents, six of them available immediately

The announcement, issued from San Francisco, names each agent and assigns it a function rather than a capability. Casey handles customer service resolution across voice, SMS, WhatsApp and web chat, shipping with prebuilt handling for frequently asked questions, returns, account management and escalation to a human. Paige covers IT and human resources requests through Slack, internal portals and existing employee tools. Carter works the shopper path, helping buyers find and compare products, answering questions and closing the transaction through in-chat checkout. Marshall sits in the back office, orchestrating end-to-end processes with what Salesforce describes as deterministic execution and an audit record of every action taken.

Piper works websites and inboxes to engage, qualify and convert inbound leads into pipeline for business-to-business sales and marketing teams. Fin resolves customer experience workflows across channels, running on a customer operations agent called Operator and on Fin Apex, a set of models the company says are custom-trained for customer experience work. Those six are generally available.

Hunter is the exception. Described as an outbound sales agent that works a pipeline from research through outreach and collaborates with sellers over weeks and months, it is in pilot now with general availability set for November 2026.

Every agent connects to Customer 360 and inherits the customer records and business processes already held there. Customers can rename an agent, and each operates inside the buyer’s own business rules, permissions and security model. Behaviour can be specified through Agent Script, the open-source language Salesforce published for agent behaviour, which mixes model reasoning with deterministic rules so that certain decisions follow fixed logic rather than inference. That combination is the quiet part of the release: a system that reasons freely is hard to audit, and the back-office agent in particular is sold on its audit record.

The runtime underneath Hunter

The second half of the announcement concerns infrastructure rather than packaging. Salesforce built a long-horizon runtime for Agentforce, and Hunter is the first agent to run on it. The company says more agents in the portfolio will move onto the runtime over time, and that customers will eventually build long-horizon agents themselves.

Three capabilities sit underneath. Memory carries context and progress between sessions, so a plan survives the end of an interaction. Durable execution keeps that plan running and allows the agent to resume or correct course when circumstances change. Dynamic steering adjusts behaviour in response to an individual user’s feedback and direction.

The worked example Salesforce gives is a seller asking Hunter to rescue at-risk deals before quarter end. The agent converts that instruction into a measurable goal, builds a plan, and determines which tasks to complete, which tools and context are required, and where the guardrails sit between acting autonomously and requesting seller approval. Nothing in the announcement specifies how those guardrails are configured, how approval thresholds are set, or what happens when a long-running plan conflicts with a change in the underlying record.

The distinction matters more than the vocabulary suggests. The OECD, in a 34-page working paper published in February 2026, separated a single AI agent that acts with some autonomy from agentic AI, meaning multiple coordinated agents pursuing complex objectives over extended periods with minimal supervision. Sustained goal pursuit over weeks is the second category, and it is the category where oversight, liability and specification questions have been least settled.

Two of these agents arrived by acquisition

Piper and Fin did not originate inside Salesforce. Piper is the inbound sales development product built by Qualified, the San Francisco company founded in 2018 by former Salesforce executives Kraig Swensrud and Sean Whiteley. According to Salesforce’s quarterly filing with the Securities and Exchange Commission, the company acquired Qualified in April 2026 for consideration valued at approximately $1.2 billion, of which roughly $1.1 billion was cash, recording $954 million of goodwill and about $290 million of intangible assets.

Fin is Intercom. The same filing records that Salesforce entered into an agreement in June 2026 to acquire Intercom, Inc., listed under the name Fin. A separate filing by Hercules Capital, an Intercom lender that committed $250 million in March 2026, put the transaction at approximately $3.6 billion.

Presenting both as members of a single portfolio is a product decision rather than a technical statement. Neither the announcement nor the filings describe how deeply either system has been rebuilt on Salesforce’s own stack, and the release attributes Fin’s performance to its own models rather than to Agentforce reasoning.

The customer figures, and what they leave out

Salesforce published six deployment statistics. Engine resolves half of its chat inquiries through a help agent named Eva. Perk builds 60% of its sales pipeline through Hunter. Autism Queensland resolves 70% of administrative requests through Paige. Hibbett AI covers 90% of core shopper journeys and went live in six weeks. Asana’s website agent, Piper, now drives four times the conversation volume, with Piper deployments averaging 45 days. Anthropic resolves 79% of the conversations Fin sees without human involvement.

All six are vendor-supplied, and none carries a denominator, a measurement window or a definition of resolution. A resolution rate depends entirely on which contacts enter the funnel: an agent that handles password resets and order status will post a higher figure than one exposed to billing disputes. Four times the conversation volume describes activity, not outcome. Sixty per cent of pipeline built by an agent says nothing about what share of that pipeline closes.

One figure also carries a naming discrepancy worth flagging. Salesforce’s April 2026 material on Engine, covered when the company opened its platform to external coding agents through Headless 360, named the customer service agent Ava, built in 12 days and handling 50% of customer cases autonomously. The September 11 release names the same agent Eva and gives the same 50% figure. The release does not explain the change, and it is not clear whether the earlier spelling, the later one, or a rename inside Engine accounts for it.

Work units as the headline metric

Salesforce framed the release with volume rather than revenue. Over the past two years, it says, 7 billion Agentic Work Units have been delivered across Agentforce and Slack, including 3.2 billion in the second quarter alone.

An Agentic Work Unit is Salesforce’s own metric, first disclosed at its fourth-quarter fiscal 2026 results in February 2026, and defined as one discrete task accomplished by an agent: a prompt processed, a reasoning chain completed, or a tool invoked. The company positioned it explicitly against token counts, arguing that tokens measure consumption rather than completed work. At that February disclosure, the cumulative figure stood at 2.4 billion units, with 771 million recorded in the fourth quarter, up 57% quarter over quarter.

Set against that baseline, the arithmetic is the story. Cumulative units moved from 2.4 billion to 7 billion in roughly two quarters, and a single quarter now accounts for 3.2 billion, more than four times the quarterly figure disclosed seven months earlier. The metric remains defined and counted by the vendor, with no external audit and no published breakdown by agent type in the September release, so it measures platform activity rather than customer outcome. A tool invocation that fails still counts as work performed.

The platform layer around the agents

Three additions accompany the portfolio. AI Skills inside Agentforce Coworker lets an employee teach the agent how to complete a task once, then reuse that method across the workforce and across interfaces; it is in pilot now with general availability in October 2026. Multi-Agent Orchestration routes work between specialised agents so that a job crossing roles, systems or stages of a customer journey is handled as one coordinated sequence, and it is generally available. Agent Optimizer assists teams through the agent lifecycle, covering construction and refinement of agents, subagents and actions, performance testing, and analysis of session traces to identify what to change; general availability is set for October 2026.

Multi-Agent Orchestration is the component with the widest implications, because coordination between agents is where interoperability questions surface. Routing inside one vendor’s platform is a solved problem in a way that routing between vendors is not, and the announcement describes the former.

Salesforce closed the release with a standard disclaimer that it may reference services or features still in development and unreleased, and that customers are directed to base purchase decisions on currently available functionality. Three items in the announcement carry future dates.

Measured against Salesforce’s own research

The most useful counterweight to a long-horizon runtime comes from Salesforce AI Research. Its CRMArena-Pro benchmark, published on June 10, 2025, found that leading language model agents succeeded in 58% of single-turn business tasks and 35% of multi-turn ones across 19 business tasks and 4,280 query instances. Workflow execution proved the most tractable skill, exceeding 83% in single-turn conditions, while confidentiality awareness was a consistent weakness across every model tested.

That study measured agents completing tasks inside a conversation. The runtime announced on September 11 extends the horizon to days and weeks, which multiplies the number of turns, tool calls and state transitions between instruction and outcome. Salesforce has not published an updated benchmark measuring long-horizon performance, and the release offers no error rate, no intervention rate and no figure for how often a plan is abandoned or corrected.

The wider evidence base is mixed in the same direction. Google Cloud’s survey of 3,466 senior business leaders found 88% of early adopters reporting positive return on agent deployments, a figure drawn from self-assessment rather than audited accounts. Marc Benioff said in July 2025 that agents were performing 30% to 50% of work inside Salesforce and resolving 85% of customer service inquiries, and the company has used its own operations as a proof site since.

What this changes for marketing and revenue teams

Two of the seven agents sit directly in marketing workflows. Piper occupies the inbound path, engaging traffic on a website and in an inbox, qualifying and converting it into pipeline. Carter occupies the commerce path, running product discovery and comparison and completing the purchase inside the conversation.

Carter is the more structurally interesting of the two, because in-chat checkout moves the conversion event off the product page and into a dialogue. That direction is not specific to Salesforce. Google set out the Universal Commerce Protocol at the National Retail Federation conference in January 2026, defining how agents discover a merchant’s catalogue, build carts and complete payment; Salesforce joined its Tech Council in April 2026 alongside Amazon, Meta, Microsoft and Stripe. Adoption of that standard has lagged its endorsement list, with a scan in May 2026 finding 26 public sites carrying the required files out of more than three million checked. A conversion completed inside an agent conversation does not fire the same events as a checkout completed on a page, which leaves attribution and measurement to be rebuilt rather than reconfigured.

For service and support functions, the packaging argument is simple: prebuilt agents shorten the distance between purchase and deployment, and Salesforce cites deployment windows of six weeks for Hibbett and an average of 45 days for Piper. Those windows describe implementation, not payback.

The governance question is the one the release addresses least. Across the wider agentic advertising market, guardrails have become the current product cycle rather than an afterthought, with PubMatic shipping a five-component governance layer on August 5, 2026 that constrains what autonomous buying agents may do at the point of execution. Salesforce’s answer is Agent Script plus per-agent permission inheritance, which places control at the level of business rules rather than at the level of spend.

The timing is not incidental. Dreamforce 2026 runs from September 15 to 17 at the Moscone Centre in San Francisco under the theme of becoming an agentic enterprise, four days after this release. Salesforce has already announced Claudeforce with Anthropic, an arrangement disclosed on August 26, 2026 that put 37 prebuilt sales skills inside Claude for pilot customers and named Claude the default model across several Salesforce surfaces. The September 11 portfolio arrives as the applications layer of that architecture, and the pricing, packaging and edition structure attached to it were not part of the announcement.

Timeline

Summary

Who. Salesforce, with named customer deployments at Engine, Perk, Autism Queensland, Hibbett, Asana and Anthropic. Piper originated at Qualified, acquired in April 2026; Fin is Intercom, subject to a June 2026 acquisition agreement.

What. Seven prebuilt agents covering customer service (Casey), IT and HR service (Paige), shopping (Carter), outbound sales (Hunter), supply chain (Marshall), inbound pipeline generation (Piper) and customer experience workflows (Fin), plus a long-horizon runtime built on memory, durable execution and dynamic steering, and three platform additions: AI Skills, Multi-Agent Orchestration and Agent Optimizer.

When. Released September 11, 2026. Six agents generally available immediately; Hunter in pilot with general availability in November 2026; AI Skills and Agent Optimizer generally available in October 2026. Dreamforce follows on September 15 to 17, 2026.

Where. San Francisco, with agents operating across voice, SMS, WhatsApp, web chat, Slack, internal portals, websites and inboxes, connected to Customer 360.

Why. Salesforce is moving from selling an agent-building platform to selling configured agents for named jobs, backed by a volume metric it defines itself: 7 billion Agentic Work Units over two years and 3.2 billion in the second quarter, against 2.4 billion cumulative units disclosed in February 2026. The deployment statistics attached to each agent are vendor-supplied and carry no denominators, and the company’s own benchmark research still records a sharp drop in agent reliability once tasks run past a single turn.

By Luis Rijo

Luís Rijo has written PPC Land daily since founding it in 2016. Over 10,000 articles, funded by readers, no sponsored coverage. Tips and corrections: [email protected]

Sourced from PPC Land

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AI doesn’t reward scale. That changes everything for multicultural agencies.

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By Nita Song

For agencies that have always had to do more with less, the level playing field is finally here, writes Nita Song, president and chief momentum officer of IW Group.

For the first time in my 30-plus years in the agency space, a major technological shift is not rewarding scale.

A 10-person boutique agency and a 1,000-person holding company shop can access the same artificial intelligence platforms, generate the same research summaries, automate the same workflows and produce the same first drafts. The tools don’t know the difference.

That single fact should be reshaping how this industry thinks about where innovation, strategy and creative leadership will come from next.

For decades, advertising operated on a simple, unspoken rule: size wins. Bigger agencies had bigger production budgets, larger research teams, deeper infrastructure and more resources to absorb risk. Everyone else operated with a fraction of those advantages and were expected to deliver comparable — and often better — results.

AI changes that equation.

AI makes many things easier. Culture isn’t one of them

As AI adoption accelerates, many people assume the technology itself will become the competitive advantage. I think the opposite is true.The tools will become increasingly accessible. The differentiator will be what organizations know that the tools don’t.

Here’s what most people miss: AI is exceptionally good at looking backward. It learns from what already happened — what people clicked on, purchased, shared or responded to. Culture doesn’t move that way.

Culture is happening right now, in conversations, communities and experiences that haven’t made it into a dataset yet.

Multicultural agencies have never had the luxury of waiting for the data to catch up. They’ve always had to understand communities before they become trend reports. They’ve had to recognize shifts in behavior before they appear in dashboards.

A large-language model can summarize what Asian-American consumers purchased last quarter. It cannot tell you why a message resonates differently with a second-generation Chinese-American household in Los Angeles than it does with a first-generation Vietnamese-American household in Houston. AI cannot replace years of community relationships, cultural fluency and lived understanding.

That distinction matters because multicultural consumers don’t simply consume culture — they create it. Many of the trends, conversations and behaviors that eventually shape mainstream marketing begin in multicultural communities long before they reach the broader market.

Understanding those signals requires something technology alone cannot provide: proximity. That remains one of the greatest strengths multicultural agencies possess.

AI finally solves the economics problem

For years, multicultural agencies faced a frustrating challenge.

They knew the value they created, but the difficulty was proving that value in ways traditional marketing organizations, procurement teams and budget models could easily quantify. AI has the potential to change that.

Research that once required months of lead time can now happen in days. Campaign adaptation that previously required extensive production resources can now be scaled efficiently. Multi-versioning can deliver culturally nuanced creative across multiple audiences at a fraction of historical costs. Most importantly, measurement is evolving.

Advanced attribution models and AI-powered analytics are making it increasingly possible to isolate audience behavior, understand performance by segment and connect multicultural marketing efforts to business outcomes with greater precision than ever before.

For decades, multicultural agencies often had to argue for investment using incomplete measurement systems.

Now the measurement systems themselves are becoming smarter.

The result is not simply greater efficiency. It is greater visibility into the value multicultural marketing has always generated.

The opportunity is bigger than multicultural marketing

The future of marketing is becoming more fragmented, more personalized and more culturally nuanced. Brands increasingly need to speak to multiple audiences simultaneously, adapt messages across communities and create experiences that feel relevant at a deeply individual level.

That environment looks remarkably familiar to multicultural agencies.

We’ve been navigating multiple cultures, languages, identities and generations for years. We’ve been translating between communities, helping brands understand nuance and identifying emerging cultural shifts before they become mainstream.

What was once considered a specialty capability is becoming a core business requirement.

The real opportunity isn’t simply that multicultural agencies can compete more effectively.

It’s that the skills developed in multicultural marketing are increasingly becoming the skills required for all marketing.

The industry has spent years asking whether multicultural agencies are keeping up.

That has always been the wrong question.

The better question — and the one the AI era is now forcing everyone to confront — is whether the rest of the industry has been paying attention.

Because AI didn’t create this advantage.

It revealed one that was there all along

Feature image credit: Getty Images

By Nita Song

Sourced from MARKETINGDIVE

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‘Not testing will break you’ in commerce media’s agentic shopping age

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By Maria Greaves

Circana’s Cara Pratt and Lindsay Pullins unpack how AI, social commerce and agentic shopping are compressing the path to purchase, and why brands that stop experimenting risk disappearing from the buying journey completely.

“There’s an incredible amount of experimentation, and the cost of experimenting is decelerating. But the cost of not experimenting is significant,” says Cara Pratt, president of global retail and media at Circana, speaking alongside Lindsay Pullins, Circana’s senior vice president of retail media.

As AI transforms how products are discovered, evaluated, and bought, Pratt argues that brands can no longer afford to wait for the perfect strategy. The priority is understanding how machines interpret brands today, before AI agents begin making more purchasing decisions on consumers’ behalf.

Marketing to machines

As commerce becomes increasingly agentic, brands will need to persuade algorithms, not just people.

“The brand needs to be thinking about, how am I showing up, how is that AI system evaluating my product, what kind of reviews do I have, what kind of claims do I have, and is it crystal clear what the brand stands for?” says Pullins.

This means that clear product data, accurate taxonomies, trusted reviews and consistent brand signals will become as important as creative campaigns in determining which products AI recommends.

Experimentation alone isn’t enough

Pratt argues that success also depends on combining strong data foundations with speed, precision, and scale.

That means building feedback loops where brands can test, learn, and optimize continuously as AI reshapes consumer behaviour in real time.

The conversation also signalled a broader shift for retail media.

Rather than operating as another advertising channel, commerce media is evolving into a connected ecosystem where retailers, publishers, brands, and AI platforms share richer signals to drive both brand building and performance.

Watch the full conversation to learn how agentic commerce will reshape retail faster than many brands expect, how social commerce is rewriting the path to purchase, and what to do now.

By Maria Greaves

Sourced from The Drum

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AI Agents Could Rewrite E-Commerce—Here’s How Brands Can Prepare

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By Haozhuang Dai

In the days after Google rolled out AI Overviews, many merchants watched search traffic drop almost overnight. The data soon confirmed what they were seeing. According to a Pew Research Center study, users clicked a traditional search result in only 8% of searches that showed an AI summary, roughly half the rate of searches without one.

Here is the part worth sitting with: The introduction of AI Overviews was a gentle change. Everything that existed before was still there; one small region of the page became an AI answer. If a soft interface tweak can rewire traffic in days, imagine what happens when the interface itself is replaced.

Having spent years building e-commerce infrastructure for independent brands, I believe each coming change will be more fundamental than the last and that commerce is approaching its iPhone moment—a complete interface shift.

Content, products and ads are becoming the same thing.

E-commerce has long treated content, product and advertising as three separate disciplines with three separate teams and budgets. But the categories were always artificial. A shoppable product card in a social feed is simultaneously content, an ad and a transaction. The lines are dissolving.

What the open web never had was a recommendation system built around products, transactions and fulfillment—the way social platforms built one around engagement. That absence is precisely why customer acquisition costs for direct-to-consumer brands have become punishing, and why so many have retreated into retention marketing, communities and physical retail.

AI agents change this equation. When a shopping agent handles discovery, evaluation and checkout in a single conversation, the funnel collapses into one step. Content, product and ad finally converge—inside the agent’s answer.

The agent-to-agent future may be closer than it appears.

This is not speculative. Adobe Analytics reported that traffic to U.S. retail sites from generative AI sources jumped 1,200% in under a year, and during the 2025 holiday season, AI-referred retail traffic grew 693% year over year—with those shoppers converting 31% more than visitors from traditional search.

Meanwhile, agentic checkout protocols backed by major AI and payments companies are turning “an agent buys on your behalf” from a demo into infrastructure. The logical endpoint is agent-to-agent commerce: a buyer’s agent negotiating with a seller’s agent. In that world, ranking is no longer won by ad budgets and search engine optimization tricks. It is won by structured, verifiable, machine-readable context—the facts about your products that an agent can check, cite and trust.

‘Wait and see’ is the riskiest strategy.

A discipline I recommend to every founder: Be optimistic about your own execution timelines, but assume technology itself will move two to three times faster than you expect. Text-to-image AI went from research curiosity to production-grade in a few years. Nobody’s roadmap priced that in.

For merchants, the uncomfortable implication is that starting today is already late—the right time to start preparing for agentic commerce was months ago. The AI Overviews release showed that these transitions do not come with a grace period. A two-day feature rollout can permanently erase a channel you spent five years optimizing.

What can merchants do now?

1. Treat product data as your new storefront. Specifications, materials, provenance, policies and reviews need to be structured and verifiable, not buried in marketing copy. Agents buy what they can verify.

2. Measure your agentic surfaces. You track your Google rankings; start tracking whether and how AI assistants recommend you, and what they get wrong.

3. Audit your dependence on human eyeballs. Any channel that assumes a person is scrolling—paid social, SEO landing pages, email capture pop-ups—deserves a stress test against a future where an agent visits instead.

4. If you sell technology to merchants, sell the foundation, not the quick win. The question I ask about every feature is: Is this something the customer will still rely on in three years, or something we merely managed to sell them? Short-term revenue is easy to manufacture; becoming infrastructure is not.

The endgame: Production is sales, and design is brand.

Selling to businesses is not the end state. If agents come to mediate both demand and supply, most intermediaries—arbitrage sellers, dropshippers, layers of markup—can simply compress away. Factories connect to demand directly; pricing becomes dynamic and transparent.

What survives is what was always scarce: brand, taste and trust. Hence my two-line summary of the endgame: Production becomes sales, and design becomes the brand.

In my view, the iPhone’s lesson was never that phones got better. It was that a new interface quietly rewrote every industry it touched. The agent could be that interface for commerce—and the merchants who treat it as infrastructure to build on, rather than a feature to react to, could be the ones still standing when the funnel finally collapses into a conversation.​

Feature image credit: Getty

By Haozhuang Dai

Find Haozhuang Dai on LinkedIn. Visit Haozhuang’s website.

COUNCIL POST | Membership (fee-based)

Haozhuang (Tony) Dai is the founder of Nile.app, an agent-native commerce platform. Read Haozhuang Dai’s full executive profile here.

 

Sourced from Forbes

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Firefox for iPhone Now Blocks Ads: How to Use the New Ad-Blocking Tool

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By James Peckham

The feature is optional, but it won’t work for search engine results or Mozilla-powered ads.

Firefox for iOS now comes with a built-in ad blocker to hide select online advertisements when browsing the web on your iPhone or iPad. The feature was introduced on Aug. 15, but Mozilla says it remains an “experimental” tool, meaning it’s gradually rolling out to all users.

The ad blocker is optional and remains off unless you enable it. Mozilla says the blocker helps with privacy, such as limiting third-party tracking, and makes it easier to visit websites with intrusive ads, such as pop-ups or overlays.

Mozilla is using the EasyList filter, originally designed for AdBlock tools, to determine what types of ads to remove. However, it won’t block everything, as Mozilla is intentionally making some exceptions. For example, ads shown in search engines won’t be blocked, with Mozilla specifically referring to ads provided by Bing, DuckDuckGo, and Google, among other search providers.

Other sponsored content powered by Mozilla through its Firefox Home or New Tab pages will also continue to appear even when the tool is enabled. There’s no way to remove either of these elements from Mozilla’s web browser.

How to Use Firefox’s Built-In Ad Blocker on iPhone and iPad

When available, you’ll find the ad blocker in the settings. You won’t need to sign up for the tool to appear, but Mozilla says it’s happening on a gradual rollout, so it’s unclear how long it will take to appear on your device.

To find it, head to Firefox for iOS and open the menu, then go to Settings > Browsing. You’ll find a section called Content, where you can toggle Ad Blocker to turn it on. Mozilla’s description says it “reduces ads and ad-related trackers” alongside a warning that “if a site looks broken, try turning this off.”

If you visit a website with bad ads and the blocker is off, you can also activate it from the menu in the bottom-right corner of Firefox. You’ll see a toggle under Content called Ad Blocker to switch it on.

To tell if you have Firefox’s blocker turned on, look for two icons at the top of the site menu labelled Protections and Ad Blocker. If it’s turned on, these will appear with small green shield icons with ticks; when turned off, they’ll appear orange with crosses.

Feature image credit: Timon Schneider/SOPA Images/LightRocket via Getty Images)

By James Peckham

Sourced from PC Mag

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Ikea’s latest clever billboard adverts work because they reveal a common truth about every home

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The furniture giant is coming clean.

Ikea recently caused furor with its racy Wicker furniture ad. Now it’s shown that it can also clean up after the mess.

The furniture giant surprised shoppers and commuters in its native Sweden with a series of ordinary-looking billboards that had a surprise hidden behind them. The clever OOH installation was designed to reveal the secret of every neat and tidy home: plastic packaging crates, and lots of them.

Ikea’s billboards sought to reveal “the other side” of tidy homes by hiding is storage solutions in plain sight. “Behind every tidy room, there’s a Samla,” reads the copy on the front of the billboard posters.

Behind the street vitrines, the creative agency NoA Åkestam Holst stacked piles of said Samla boxes filled with everything from toys to clothes, vintage records, half-finished drawings, and, yes, that Ikea monkey toy.
“The front of the billboards like any typical ad for home furnishing. But when you look again, there’s an honest twist. Everyday life is messy, then tidy, and then messy again. We think it’s nice to show both sides of the story,” says Tiago Pinho, Art Director at NoA Åkestam Holst.

The ad was inspired by Ikea’s own research that found that 83% of people in Sweden feel they have at least one room at home that’s hard to keep tidy. The aim was to show that the mess can be contained.

“Keeping your home clean is always a battle, regardless of if it’s a small flat, a castle, or something in between. We wanted to remind people of our great storage boxes in an out-of-the-box way – hopefully we can stop people in their tracks,” Ikea Sweden’s marketing comms leader Jonas Westberg said.

The best billboard ads have to grab attention on a busy high street. Surprise is one of the best ways to do that, but Ikea shows that communication is most effective when this isn’t surprise for its own sake. Its ads often use unexpected juxtapositions to reveal a common truth and to provide a practical demonstration of how it can solve an everyday problem.

Feature image credit: Ikea / NoA Åkestam Holst

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Joe is a regular freelance journalist and editor at Creative Bloq. He writes news, features and buying guides and keeps track of the best equipment and software for creatives, from video editing programs to monitors and accessories. A veteran news writer and photographer, he now works as a project manager at the London and Buenos Aires-based design, production and branding agency Hermana Creatives. There he manages a team of designers, photographers and video editors who specialise in producing visual content and design assets for the hospitality sector. He also dances Argentine tango.

Sourced from CREATIVE BLOQ

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ChatGPT Is an Advertising Platform Now. Here’s What That Means for Your Business

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By Al Sefati|Edited by Chelsea Brown

OpenAI covertly turned ChatGPT into an advertising tool this year. Here’s how it works.

The script for finding customers when they were searching for something to buy was once simple: Google Ads, Meta Ads (Facebook, Instagram, WhatsApp), LinkedIn Ads, and perhaps TikTok and X (Twitter) Ads.

Thanks to ChatGPT and AI, today, you can add a new name to that list.

Earlier in 2026, OpenAI, the parent company of ChatGPT, covertly turned ChatGPT into an advertising tool. This happened much sooner than most expected and evolved in a matter of months.

If you are an entrepreneur or are responsible for marketing a small business, the insights below are vital.

Why this matters more than you think

For many users today, ChatGPT is no different than what Google was — weighing options and asking “what is the best X for Y” before choosing between a select few without visiting any website. That’s something that has been going on for quite some time already.  What’s new this year is that OpenAI made room for companies in this process.

Early signals show this cannot be siloed as a niche experiment. According to industry news, the ad test generated $100 million annually after only six weeks, with many advertisers already experimenting with it.

Top advertising agencies, including Omnicom and Publicis, have created ChatGPT-enabled advertising strategies for their clients. We also ran some ads for a couple of clients as well and have gained our own share of insights.

What the sponsored messages really look like

It’s not what you think. The new ChatGPT feature enables the system to show you a sponsor message card whenever it generates an answer to your question. It’s similar to a business card containing a headline, copy, image and link.

OpenAI has been firm that ads will never change what ChatGPT actually tells you. The answer to your prompt is not impacted by the ads. The ad is just an additional option below it.

Ads are shown to whom?

This can shock the user. They show up to ChatGPT’s free plan users and also users using the cheaper version known as Go.

The paying customers with the Plus, PRO or better plans don’t see any ads, and OpenAI says just a small portion of free users see the ads.

Simply put, this is not about millions of impressions. It is about a few motivated users.

Starting without an exorbitant investment

According to reports, when OpenAI initially started, it had asked for a minimum investment of $200,000 just to use its testing facility, thereby excluding most small businesses.

Larger businesses are usually slower in new technology adoption; hence, in May, OpenAI’s Ads Manager was available to pretty much all businesses, where they could decide how much they were willing to spend per click or impression.

This was a huge change. A local business or even a startup can now consider this mode of advertising without spending an arm and a leg, for as little as $25 a day.

One major distinction from Google and Facebook advertising

If you are used to using age, salary, profession and past activity as criteria for your ads, ChatGPT Ads will seem strange to you. Currently, the platform allows targeting either by country or by “context,” which refers to the general subject or need, but not by particular keywords and not by demographics.

Advertisers have very limited opportunities for optimization. You may upload first-party data, such as a customer list upload, to target them via ads or exclude them from seeing ads, but that is pretty much it.

The conversations themselves are not provided to advertisers by OpenAI, meaning this marketing channel works best with a clear offer rather than a vague one because of its inability to be targeted precisely.

Where it fits (and where it doesn’t) right now

Currently, the most appropriate use of ChatGPT Ads appears to be where the individual is evaluating different options or conducting research for making a particular decision. In simple terms, ChatGPT Ads is best for purchases, which include software, education, traveling and other service products.

From our experience, the traffic is also not converting the way the client desires. But we have seen growth of other channels, which begs the question: if users are actually using ChatGPT to research, but then go to the website directly or perform more branded search or look to social media.

What this means for entrepreneurs

The platform is still relatively new and, as earlier stated, has limited features, but it is growing very quickly.

The ad formats, targeting tools and category policies have been revised at least a couple of times this year, and there will certainly be more changes coming.

Rather than trying to completely revamp your marketing budget in a single day, marketers and entrepreneurs should try running a smaller experiment first and see how it does.

By Al Sefati

Al Sefati is CEO of Clarity Digital Agency and an omnichannel marketing strategist and AI-driven digital transformation consultant. With 20+ years of experience, he helps brands grow through smart strategy, performance marketing, and data.

Edited by Chelsea Brown

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