Google is kicking off its biggest marketing push for Gemini, its generative AI assistant, in a feel-good Super Bowl ad meant to humanize the tech. 

In the 60-second spot, set to air during the third quarter of the Feb. 8 game, a mother helps prepare her young son, Ben, for a big move to a new home with help from Gemini. She asks Gemini to pull up photos of the new house, then drops in a picture of Ben’s current room to show him how his bed, his toys, and even the dog’s bed can fit into his new room. Then, they take a look at the new backyard, and although Mom still won’t let Ben have a trampoline, together they imagine all the possibilities of their new life together with Gemini’s image-generation tools.

The campaign represents Google’s fifth consecutive—and 10th overall—Super Bowl appearance. From the company’s first foray in 2010 with “Parisian Love,”—a love story told through Google searches—the brand has consistently taken an emotional tack to its Super Bowl messaging. 

“Google is a product company, but really, we believe in products that help people,” said Marvin Chow, vp of consumer and AI marketing at Google. “We believe in that storytelling of connecting the benefit of a product to a value in someone’s life.”

The core idea of this year’s ad, “New Home,” is that Google is becoming more helpful than ever. “The platform that we’re developing is this idea of a new kind of help from Google,” Chow said. “We’ve always been the helpful company, but with new advances in technology and AI, the kind of help we can bring people is going to be new and improved. This really starts to set that tone [that] it’s not just another chatbot, but it’s a new kind of help…that we’re going to bring to you.”

This year’s ad, “New Home,” was created by the in-house Google Creative Lab team. Although Google maintains various agency relationships, it made sense to create this ad internally, Chow said, because the Creative Lab has direct insight to product development and research at Google, enabling a more informed depiction of Gemini’s capabilities. 

“New Home” is the keystone of an integrated campaign that spans TV, online video, social, out-of-home, and influencer partnerships. Much of the content will appear in the days leading up to the Big Game. Bay Area residents, for example, can expect to see ads for Gemini splashed across billboards and public transportation. Plus, through a partnership with Lyft, Google will offer a $20 ride credit through Feb. 8 for Bay Wheels bikes across the Bay Area. Gemini will also sponsor EA Sports’ Madden Bowl, starring Luke Combs and Teddy Swims, on Feb. 6. On social platforms, the brand will roll out a slate of play-by-play, sports commentary-style videos with announcers showcasing various use cases for Gemini, from trying a new hairstyle to identifying a car’s fuse box.

“We’re definitely going big,” Chow said. The forthcoming showdown between the Seattle Seahawks and New England Patriots will take place in Santa Clara, on Google’s “home turf,” Chow noted (Google’s global headquarters is just down the road in Mountain View). “You’ll start to see Gemini all over. It’ll be unmissable.”

In the months leading up to Super Bowl LX, the game’s broadcast host, NBC Sports, sold 30-second spots for upward of $10 million. Though Chow declined to specify Google’s total investment, he said it was “on par” with the brand’s previous Super Bowl buys.  

Google sees this moment as the start of a bigger promotional push, with Gemini occupying the pole position of CEO Sundar Pichai’s decade-long ambition to make Google an “AI-first” company (something he first spoke about in April 2016).

“At Google, we talk about [the idea that] a product has to be ready for marketing,” Chow said. “We have a lot of products, and some of them are early-stage, and they’re not quite ready for marketing. Nothing’s perfect, but we’re at that moment where the Super Bowl is the starting point” for a bigger Gemini push. 

Gemini is gaining steam on AI leader OpenAI. It now has around 650 million monthly active users, compared with ChatGPT’s 800 million, according to research from European bank BNP Paribas. Gemini’s share of website visits rose to 22.5% in December, up from 17.7% the previous month. The figures far outpace the tool’s 2024 usage numbers.

In the weeks following the Super Bowl, Chow’s team will look to link the “New Home” ad and the surrounding campaign to an uptick in Gemini adoption. Google will also do lift studies and assess changes in consideration, awareness, search trends, and more. 

“It is this pivotal moment, and we’re kind of kicking off this Gemini story,” Chow said. “When I look back, I would hope that this is the beginning of that moment where regular people are starting to learn about Gemini and be like, ‘Oh, that’s the helpful AI. That’s the thing that can do this for me.’ That’s where I think that cultural adoption and mindshare of Gemini starts to take off—in this starting moment.”

The meaning of home has changed. It is no longer just about maintenance or necessity; it reflects how people live, grow, and plan for the future. 

In this episode of The Speed of Culture podcast, Matt Britton sits down with Jennifer Wilson, senior vice president and CMO at Lowe’s, to examine how one of America’s largest retailers is executing a modern marketing strategy rooted in culture, experience, and long-term relevance. 

Jennifer breaks down Lowe’s brand transformation, from expanding beyond traditional home improvement into lifestyle and community, to using AI in home improvement retail, scaling creator partnerships, and building a powerful retail media network. 

The conversation offers a clear look at how brands evolve without losing trust, and what it takes to stay relevant as consumer behavior, technology, and culture continue to shift.

Jennifer has spent more than 20 years at Lowe’s, progressing through senior roles across integrated marketing, brand and customer marketing, and enterprise leadership before stepping into the CMO role.

Her experience across both marketing and business units shapes Lowe’s brand transformation today, informing decisions around retail marketing strategy, creator economy investment, loyalty growth, and the evolution of Lowe’s as a modern, experience-led retailer.

Key Takeaways:

[01:32] Home as Lifestyle, Not Just Shelter — Jennifer explains how the pandemic reshaped the emotional role of home. What once centered on safety and basic function now reflects lifestyle, growth, and long-term living. People think about how their homes evolve with family, work, pets, and aging. This shift anchors Lowe’s marketing strategy, positioning the brand as a partner across life stages, not just a place to buy supplies.

[04:01] Redefining What Home Improvement Means — Lowe’s brand transformation moves beyond the old perception of construction-only retail. Jennifer outlines how expanded marketplace offerings, décor, and immersive store experiences allow Lowe’s to support both structural projects and personal expression. The goal is relevance across mindsets, whether a customer is fixing a problem or shaping how their home feels.

[06:52] AI as an Enabler for People, Not a Replacement — AI in home improvement retail helps remove friction without removing people. Jennifer shares how AI supports associates with real-time knowledge and helps customers visualize projects, solve problems, and move faster. At the same time, Lowe’s continues to invest in skilled trades, treating AI as a confidence booster, not a substitute for human expertise.

[08:54] Serving Doers and Impulse Shoppers Together — Jennifer describes two distinct shopping behaviors shaping home improvement brand marketing. Some customers love hands-on projects and learning by doing. Others shop through inspiration, social discovery, and impulse. Lowe’s adapts its assortment and storytelling to meet both needs, reflecting a grounded approach to how brands stay culturally relevant without choosing sides.

[10:41] Why Staying Put Changes Spending Patterns — With higher interest rates keeping homeowners in place longer, Lowe’s sees increased focus on refresh cycles, repair, and upgrades. Jennifer explains how appliance innovation, faster delivery, and awareness of home equity influence spending decisions. These moments create opportunity for Lowe’s to support improvement without requiring a move.

[12:51] Creators as a Scaled Growth Channel — Lowe’s investment in creator economy retail brands has grown rapidly, with more than 26,000 creators contributing to storytelling and reach. Jennifer shares how creator-led moments, like the viral Black Friday bucket campaign, translated into real-world impact, long store lines, emotional stories, and sustained attention that traditional channels alone could not deliver.

[15:51] Sports as a Cultural Connector — Through its NFL partnership and “Earn Your Sunday” campaign, Lowe’s taps into shared rituals that still command attention. Jennifer explains how sports provide live moments, emotional context, and scale, allowing Lowe’s to connect home improvement to culture in ways that feel unexpected but grounded in everyday life.

[23:08] Loyalty and Retail Media as a Growth Engine — With over 38 million members, loyalty marketing in retail fuels repeat visits, personalization, and insight-driven growth. Jennifer outlines how Lowe’s retail media network strategy uses first-party and home data to help partners reach underserved audiences while improving the customer experience across channels.

Amazon is cracking open its ad infrastructure to AI agents.

At IAB’s Annual Leadership Meeting on Monday, the company announced the open beta of its Amazon Ads Model Context Protocol (MCP) Server, a new way for advertisers and ad tech partners to connect AI agents to Amazon Ads through a single integration—rather than building custom connections for every workflow.

The MCP server allows advertisers to hook their agents into Amazon’s ad software in minutes, Paula Despins, VP of ads measurement at Amazon Ads, told ADWEEK. 

Instead of stitching together bespoke integrations, advertisers can rely on a shared protocol that translates natural language prompts into structured API calls—essentially acting as a middle layer between AI agents and Amazon’s ad systems. Built on the Model Context Protocol—an open standard originally developed by Anthropic—the server sits between AI agents and Amazon Ads’ APIs. Once connected, agents can handle tasks like setting up campaigns, adjusting budgets, managing products and pulling reports.

“Advertisers’ get a connection point and a translator that lets agents or AI tools talk and communicate in a standard way to our systems, instead of requiring custom codes for every integration,” Despins said.

A core part of the rollout is what Despins called a “tool for common actions.” Advertising workflows often span multiple steps and systems, but MCP tools bundle those steps together so agents can execute them through a single conversational prompt.

“In almost any ad system, multiple actions are required,” she said. “With tools, you can simplify the common actions down to a single conversational prompt. It simplifies taking the multi-step actions that are common in advertising.”

That simplification is meant to solve a familiar problem with agentic AI: reasoning overload. Agents typically need to understand what an API does, how it works and which version to use—decisions that can slow things down or lead to the wrong outcome.

“Agents today need context of what an API does, how it operates—that creates a lot of load in reasoning for the agent,” Despins said.

She pointed to early internal testing as an example: In one prototype, Amazon asked an agent to generate a path-to-conversion report showing all ad touchpoints before a purchase. Instead of pulling from an existing API, the agent wrote its own code and ran it through Amazon Marketing Cloud, processing more than three years of data. In other cases, agents defaulted to technically correct but outdated API versions—choices a human developer likely wouldn’t make. MCP tools are designed to prevent those missteps by giving agents explicit instructions for common workflows.

“It’s a lot of heavy lifting to teach the agent how all of this works,” Despins said. “The difference with MCP is you’re going to start from the language in which it operates, and then when you add tools, you take common multi-step workflows and you give an instruction manual to it. Then it’s going to spend its time reasoning on things that matter rather than reasoning—and sometimes getting it wrong—on the basics.”

The open beta follows Amazon’s closed MCP pilot announced last year and mirrors similar efforts across ad tech, including AdCP which was announced last year, as platforms push toward standardizing how AI agents interact with ad systems. It also comes as the ad industry shakes off its old habits–breaking away from rigid, manual ad workflows toward agent-led automation. Meanwhile, Amazon has hinted at leaning further into agentic commerce. In Q3 of last year, CEO Andy Jassy said AI agents could expand how consumers shop online, as the company’s ad business grew 24% year over year to $17.7 billion.

Despins noted that Amazon’s ad partners had been asking for this kind of protocol, though she declined to say how many advertisers participated in the closed beta.

In our tech-powered world, technology marketers cannot afford to lose sight of client trust. And yet, they do. 

On this episode of the Marketing Vanguard podcast, Ariel Kelman, president and CMO of Salesforce, reveals why customer success storytelling matters more than ever, how to ground AI in trusted first-party data, and the key strategies for balancing AI automation with human creativity. 

What you’ll learn:

• How to ground AI agents in customer data to drive measurable outcomes 
• Why CMO leadership must pioneer AI adoption before mandating it across teams 
• AI’s productivity paradox: Why it enables both more output alongside better work-life balance 
• How to position your brand’s trust as a competitive moat in the AI era
• The framework for balancing centralized brand strategy with decentralized field execution  
• Why “show, don’t tell” beats feature-focused marketing when launching complex products 

Ariel Kelman is the president and CMO of Salesforce, leading global marketing strategy across one of the enterprise software industry’s most diverse and expansive product portfolios. With a background spanning product management, startup leadership and tenures at AWS and Oracle, Ariel brings two decades of expertise in B2B tech marketing, customer success positioning and AI-driven business transformation. 

His work at Salesforce—from pioneering early SaaS adoption strategies to spearheading the AgentForce platform launch—has shaped how enterprise organizations approach AI implementation and customer engagement at scale, making his POV essential for CMOs navigating the intersection of brand, demand generation, and artificial intelligence.

Episode Highlights: 

[12:54] How Show, Don’t Tell Cuts Through AI Hype — Ariel highlights that, in crowded AI markets, letting proven customers tell stories is exponentially more credible than marketing claims about platform superiority, speeds, or power — a lesson Salesforce operationalized through AgentForce City at Dreamforce. Rather than explaining technical specifications, Salesforce constructed an interactive village where attendees walked into real customer implementations: Williams Sonoma using AI agents for shopping, and so on.

This “show, don’t tell” approach succeeds because decades of tech industry overpromising have made B2B buyers deeply skeptical; only peer proof shifts perception and preference. This strategy transforms marketing from persuasion to evidence-gathering, dramatically shortening sales cycles and improving win rates.

[21:50] Leadership Must Try AI Before Expecting Their Team To — Ariel identifies the biggest barrier to AI adoption in marketing organizations as psychological resistance to change, not technological capability. Assigning teams to “find one AI efficiency initiative” fails because it ignores change aversion; instead, executive leaders must become visible pioneers, experimenting with tools themselves and sharing results transparently with their organizations.

For CMOs managing organizational change, leading by example removes the psychological burden of “moving someone’s cheese” and transforms AI adoption from mandate to aspiration.

[24:55] Without Unified Customer Data, AI Will Fail — Ariel stresses that first-party data activation is foundational to AI success. In his opinion, it’s a prerequisite even more critical than technology selection as generic AI outputs fail when grounded in incomplete or siloed customer information. CMOs must ensure their customer data platform harmonizes all signals (purchase history, product usage, support interactions, web behavior, location) into a single source of truth accessible to AI engines without compromising security or privacy.

Without this foundation, AI agents will generate irrelevant follow-ups to customers with open support tickets, deliver location-generic event invitations, or miss high-propensity opportunities buried in unconnected systems. 

[27:14] Learn From Other CMOs’ Failures, Not Just Their Wins — Ariel reveals that his most actionable insights come from peer networks where CMOs discuss hard organizational problems — centralization versus decentralization, attribution models, brand-versus-demand-gen budget splits and which AI tools actually deliver ROI — but emphasizes that failures teach more than successes.

When leaders openly share what didn’t work (organizational structures that looked good on paper but failed in execution, AI tools hyped but underutilized, attribution models that broke under scrutiny), CMOs gain pattern recognition to avoid repeating mistakes. For CMOs building their leadership practice, construct a personal network of 5-10 peers at comparable companies and establish regular cadences to discuss what’s failing, not just what’s winning. This vulnerability-based learning accelerates decision-making, de-risks major structural changes, and prevents expensive pivots.

The U.K.’s top anticompetition regulator, the Competition and Markets Authority (CMA), issued a critical ruling against Google on Wednesday, calling for the search giant to provide content creators with more agency over how their content is used by the company’s AI tools.

The proposal deals with a concern that arose when Google first introduced its AI Overviews, which use content pulled from publisher websites to answer user queries on the search page, reducing users’ need to visit the websites themselves. 

The new ruling calls for Google to allow publishers to opt-out of AI Overview feature without harming their visibility on search.

This dynamic, colloquially known as Google Zero, has become an existential issue for publishers, which rely on search traffic to generate advertising revenue. The same issue holds with Gemini, which also ingests publishers’ data to answer user queries without sending those users to the original sources of that information. According to data from Chartbeat, search traffic to publishers declined globally by a third in 2025.

In response to the issue, publishers have sought to block the Google crawlers that pull information from their sites and feed it to Gemini and AI Overviews. But Google uses the same indexing system for Gemini and AI Overviews as it does for its search engine, meaning if publishers blocked the crawler, they would also risk losing search visibility.

Publisher advocates and critics of the set-up have called on Google to split its lone crawler into two, allowing publishers to block scraping for Gemini and AIOs while still allowing the company to crawl and index sites for search. 

Google has resisted those efforts, but the CMA finding may now force its hand.

Two key caveats

While the proposal is a welcome ruling for publishers, it is hardly a windfall.

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First, the CMA does not explicitly require Google to split its crawler into two; instead, it simply calls on the search giant to create a system that provides publishers with more choice, transparency, and attribution. This could lead to a decoupling of the crawler functions, but it does not mandate it.

Second, the CMA governs how Google must behave in the U.K., but those regulations do not necessarily apply in other jurisdictions, like the U.S. or European Union. 

Historically, Google has often abided by the standards set by U.K. regulators; it pledged to do so with the Privacy Sandbox developments, as well as with cookie depreciation findings. But it is not legally required to do so, meaning a CMA ruling does not necessarily compel Google to change its behavior in the U.S.

Still, the proposals are significant. Whatever system Google adopts will have a wide-ranging impact, affecting every website on the internet. It also marks a defining symbolic turn, as it marks the first regulatory ruling to affect Google’s AI business.

Alex Mashrabov didn’t set out to build another AI company promising to revolutionize marketing.

At Snap, a company popularly known for its flagship Snapchat product, Mashrabov led the company’s generative AI efforts. He had a front-row seat to how quickly culture now moves online—and how often marketing shows up just after the moment has passed. A song goes viral, a format takes off, a meme mutates into something new, and by the time a campaign clears approvals, the internet has already moved on. For younger audiences that lag often meant irrelevance.

“For a marketing team, it’s very difficult to figure out every day what’s actually worth attention for younger demographics,” Mashrabov told ADWEEK. 

That disconnect became the premise behind Higgsfield. Founded in October 2023, the company positions itself as an end‑to‑end AI platform for social video creation that is explicitly built for marketers.

Unlike companies racing to dispatch a single best video model almost every week, Higgsfield positions itself as an orchestration layer. The platform offers access to a growing roster of generative video tools—including models from Google, Freepik and others—allowing people to choose which system makes the most sense for a given prompt or creative goal. 

Mashrabov borrows the company’s name from quantum physics, where the Higgs field is the invisible force that gives particles their mass. In marketing, he sees a similar problem where brands and creators may have ideas and stories, but video is the only way those ideas gain weight online. Higgsfield’s pitch is simple—turn fast‑moving trends into tangible, on‑brand video before the moment passes. 

Once on Higgsfield, the workflow is deliberately simple. People can generate images, turn stills into video, animate presets, or build AI influencers. Brands can start with an image or prompt, chooses a preset designed for a specific social format, and renders a video already sized for platforms like TikTok or Instagram. Qatar Airways, for instance, used Higgsfield to produce a 2026 New Year post on Instagram, turning static brand assets into short-form video. 

The platform’s early adoption skewed heavily toward creators. Musicians including Snoop Dog, Madonna and Will Smith used Higgsfield to augment existing content, experimenting with camera controls and visual effects, Mashrabov said. Over time, that experimentation evolved a creative guidance for marketers, moving from storyboards to finished video.

Today, about 85% of activity on Higgsfield is tied to brand campaigns, according to the company. Mashrabov said the platform has amassed more than 15 million creators in the past year and surpassed $250 million in annual recurring revenue, most of it driven by subscriptions. This comes as its most recent $80 million Series A extension, bringing the total funding to $130 million and values the startup at more than $1.3 billion, ADWEEK previously reported. 

“We’re not designing a platform just for prompt engineers or technical users,” Mashrabov said. “We want it to be accessible to a regular social media marketer.”

Higgsfield does not train its own foundation models from scratch. Pre-training is handled by the underlying models it integrates with, the company said, while Higgsfield post-trains and fine-tunes its systems using proprietary data sources.

Notably, Mashrabov doesn’t see Higgsfield competing most directly with companies like OpenAI or Adobe. Instead, he points to ByteDance—the global tech giant behind TikTok—as the real benchmark. Beyond the social platform itself, ByteDance operates a growing stack of video and marketing tools, from CapCut to its marketing cloud platform BytePlus.

“When I looked at CapCut, it’s way more suited for marketers and creators, than any other incumbent software, including Adobe and other giants,” he said. 

That philosophy extends to how Higgsfield differentiates itself from consumer‑facing AI tools such as Sora. Mashrabov draws a sharp line between platforms built for mass experimentation and those designed for professional production. Consumer products must drive generation costs toward zero to support scale, he said. Higgsfield, by contrast, is built for teams with budgets and deadlines, where speed and throughput are the real constraints.

“The key unlock is definitely the cost of the production, but also the velocity,” Mashrabov said. “On Higgsfield one person can easily produce 30 to 40 seconds of content every day. This velocity is important to stay on trend.”

Where Higgsfield fits—and where it doesn’t 

That focus on velocity has caught the attention of ad agencies experimenting with how AI fits into modern production workflows. 

“Higgsfield is a rapid prototyping engine for us–it lets our ideas become visible very fast. And for clients, that’s been a big game changer,” said Alex Foster, head of creative studio, Code and Theory. Higgsfield sits within the agency’s broader Creative Studios as is used for storyboard frames and making animatics with camera movements. Some of those prototypes ultimately graduate into final executions, after concepts are pressure‑tested before committing traditional production budgets.

“The fidelity [has] not always quite been there, but we saw the creative potential to it,” said David Dorsey, associate director, motion,  Code and Theory. Features like the lips sync function or camera controls, such as dolly zoom presets, offer more creative direction than text prompting alone, he noted. 

Still, Code and Theory doesn’t attempt to replace the entire production ecosystem with Higgsfield. 

“AI is still not at a stage where it’s perfectly indistinguishable from live action or real motion, both in physics, in human reaction and emotion,” Foster said. Legal risks, such as copyright infringement, also shape how agencies choose which AI tools to use: Platforms like Adobe Firefly, which are designed to be safer for commercial use, are often used to develop characters or brand assets early, while tools like Higgsfield are brought in later for creative control and developing campaigns faster.

A director from WPP’s innovation team who wasn’t authorized to speak to media told ADWEEK that Higgsfield appeals to teams without deep prompt‑engineering expertise. The platform’s stylistic presets reduce guesswork, allowing people to generate editorial‑style visuals—high‑contrast lighting, wide cinematic angles—without crafting complex prompts. In practice, the executive said, Higgsfield is often used internally or presented to clients as conceptual groundwork for larger campaigns rather than final output.

In internal testing, WPP often runs identical prompts across competing systems, finding that results vary based on how each model has been trained. The question becomes less about which model is seen as “the best” and more about which produces the right creative outcome with the least friction.

Zero production tax

For Mashrabov, that flexibility is the point. He isn’t interested in the AI slop currently clogging feeds—those repetitive, uncanny-valley ads that feel like cheap replicas of a mid-2000s infomercial. Instead, he’s betting that the next era of advertising will be won net‑new concepts that are only possible with AI, making it easier to localize and adapt content across demographics and languages.

“We decrease production tax to zero with AI,” Mashrabov said.

Looking ahead, Higgsfield has plans to expand into performance marketing, allowing performance data to feed back into the content generation process and guide which variations are worth producing. 

In an increasingly crowded AI video market—one that has pulled in more than $500 million in new funding in 2025 alone—Mashrabov is betting that marketers don’t need another black box but a system that helps ideas gain mass fast enough to matter.

“Marketing technology obviously has not been very popular across Silicon Valley venture firms,” Mashrabov said. “AI is going to create this renaissance opportunity specifically with the companies who advertise on social media.”