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AI Goes Mainstream as Nearly Half of Retail Brands Now Use It Weekly

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Artificial intelligence is no longer an experiment in retail. Nearly half of retailers now use AI daily or several times per week, according to data from Amperity’s 2025 State of AI in Retail survey.

From customer data platforms to predictive models and chatbots, the technology is today being embedded into everyday operations, reshaping how brands engage with customers and compete in a crowded market.

“We have AI embedded across many parts of the business, which makes it feel seamless rather than experimental,” Daniel Chasle, chief data officer at U.K. fashion brand New Look, told Newsweek.

“For example, we use Amperity to run algorithmic stitching of customer profiles, AI chatbots in Zendesk to support customer service deflection, and AI coding assistants for our developers. We’ve also rolled out Microsoft Copilot to a subset of employees to help with daily tasks. Together, these tools are becoming part of the new ‘norm’ in how we work,” he said.

Amperity CEO Tony Owens told Newsweek that “every retailer should be experimenting with AI right now. But leaders take it further. They embed AI into the way the business runs.”

Newsweek Illustration/Canva

The normalization of AI reflects a turning point for the industry. Ninety-seven percent of retailers plan to either maintain or increase their AI spending this year, with priorities focused on personalization, media spend and demand forecasting. Loyalty and customer service are also key targets, as executives look to reduce costs and strengthen relationships at the same time.

“What retailers are really asking for are demonstrable outcomes,” Tony Owens, CEO of Amperity, told Newsweek. “They don’t want AI for AI’s sake—they want proof it drives growth. Every use case has to tie back to revenue, efficiency, or loyalty in ways you can measure.”

A Shift in Omnichannel Strategy

One of the biggest changes in 2025 is how retailers think about omnichannel, astrategy for giving customers a consistent shopping experience, whether in person, online or through mobile.

“Omnichannel 1.0 was about being where your customers are—stores, websites, apps,” Owens explained. “Omnichannel 2.0 is about the customer journey itself, and AI is what makes it possible to personalize those journeys in real time. The customer decides the channel, not the retailer, and they’re voting with their wallets.”

Retailers see the potential: 63 percent believe AI will help improve customer loyalty, while 65 percent expect it to increase customer lifetime value. But fewer than half—just 43 percent—are currently applying AI in customer-facing applications.

“Customers don’t think of themselves as segments or cohorts. They’re on a journey with your brand,” Owens said. “AI helps retailers meet them in that journey by anticipating needs, tailoring offers, and staying consistent across every channel. People know when a brand truly ‘gets’ them. That’s when the relationship shifts from transactional to personal, and that’s what drives loyalty and lifetime value.”

Still, adoption is uneven. While enthusiasm is high, retailers are cautious about pushing AI directly into customer touch points, often holding back because of costs, skills gaps and infrastructure challenges.

Solving the Data Puzzle

The survey highlights one major obstacle: 58 percent of retailers say their customer data is fragmented or incomplete. That fragmentation raises IT costs, delays decisions and complicates personalization.

“The challenges are the acquisition of the data from the disparate systems and knitting the data together to give a consistent view of the physical customer behind the data,” Chasle said. “The opportunities are to have the unified view of the customer, their shopping behaviors and preferences, to be able to understand all our touch points and interactions with the customer. This becomes an incredibly powerful data set that can power our decision-making and our engagement with customers.”

The New Look brand tackled the issue by combining an enterprise data platform with Amperity’s identity resolution. “Amperity also makes the data seamlessly available back into our data platform for our data science teams to access,” Chasle said.

That effort already has delivered results. New Look is using real-time customer profiles to fine-tune marketing campaigns and improve personalization. According to Owens, the unified data helped the brand identify nearly 26 percent more high-value customers than it had recognized before, insights that led to stronger offers and higher conversions.

Owens said it’s “proof that when you put the right data behind AI, you deliver a better journey for the customer and measurable return on customer data for the business.”

And the results are tangible. “The newly created Real-time Customer Profiles with Amperity are already fueling our paid media suppression activity, CRM optimization and will soon start to power a new wave of personalization experiences,” said Chasle.

Owens said that New Look’s example illustrates the potential benefits. “By using Amperity to unify customer profiles and power predictive models, they uncovered nearly 25 percent more high-value customers than they knew about before. That insight led to better offers, stronger conversions, and proof that when you put the right data behind AI, you deliver a better journey for the customer and measurable return on customer data for the business.”

But not every retailer has made this leap. The survey found that only 23 percent are currently using AI in production to resolve customer identities or prepare data for marketing, underscoring how widespread the data challenge remains.

From Experimentation to Embedding

For many retailers, AI adoption is moving beyond pilot projects. Nearly half are already using AI weekly, and those with customer data platforms are far ahead of their peers.

Organizations with a customer data platform (CDP) are twice as likely to use AI daily (60 percent vs. 29 percent) and more likely to have full adoption across multiple business units (22 percent vs. 10 percent).

“We don’t have the luxury of budget to experiment, and so we are approaching it on a value basis as part of our transformation roadmap and the prioritization of the business value and alignment to the overarching strategy,” Chasle said.

Owens said the distinction between experimenters and leaders is becoming clearer. “Experimenters usually see productivity gains, such as reduced costs, faster workflows, or incremental improvements. That’s valuable, and every retailer should be experimenting with AI right now. But leaders take it further. They embed AI into the way the business runs. That’s when you move beyond efficiency to true personalization at scale.”

That gap is likely to widen. As some retailers build AI into core operations, others risk being left behind, stuck in pilot mode without the confidence or resources to scale.

What Comes Next

Both Owens and Chasle pointed to personalization as the next big opportunity.

“Yes, the personalization of the web experience is in our immediate roadmap, with a vision of this leading to a personalized AI-stylist capability supporting our customers both in the digital and retail channels,” Chasle said.

Owens predicted that the next wave will be even more transformative. “By 2026, retailers will start to democratize data across the entire enterprise, using it to orchestrate the customer journey end-to-end. That’s when AI will deliver the full return on customer data.

“And that’s the moment of separation,” he continued. “The retailers who master this will win the bulk of customers in their category and set the standard for the next generation of brands. The ones who don’t will fall behind. This is a defining moment for retail. There will be winners and there will be losers.”

The findings echo broader consumer research, such as Cognizant’s recent survey showing that shoppers increasingly expect AI-powered personalization in their retail journeys. Taken together, the two reports show both sides of the AI revolution: consumers demanding seamless experiences and retailers racing to build the data foundations to deliver them.

Whether those predictions materialize depends on how quickly retailers overcome the same obstacles that have slowed AI before: siloed data, high costs and employee training.

The survey underscores the tension between ambition and readiness. While 97 percent of retailers are ramping up AI investment, only 11 percent feel strongly that they are prepared to deploy AI tools at scale. High costs, technical gaps and fragmented data remain persistent hurdles.

Still, the direction is clear. “Being able to tackle these business processes and re-imagine them with AI is the biggest opportunity,” Chasle said.”It is going to require significant business buy-in with senior stakeholder sponsorship, a clear end-state vision and a roadmap of activity that progressively tackles the required change.”



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5-Week AI Mentorship for Startups in SF

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OpenAI has unveiled a new initiative aimed at nurturing the next generation of artificial intelligence innovators, marking a strategic push into talent development amid intensifying competition in the AI sector. The program, dubbed OpenAI Grove, targets early-stage entrepreneurs who are either pre-idea or in the nascent phases of building AI-focused companies. According to details shared in a recent announcement, the five-week mentorship scheme will be hosted at OpenAI’s San Francisco headquarters, providing participants with hands-on guidance from industry experts and access to cutting-edge tools.

The program’s structure emphasizes practical support, including technical assistance, community building, and early exposure to unreleased OpenAI models. As reported by The Indian Express, participants will have opportunities to interact with new AI tools before their public release, fostering an environment where budding founders can experiment and iterate rapidly. This comes at a time when AI startups are proliferating, with OpenAI positioning itself as a hub for innovation rather than just a technology provider.

A Strategic Move in AI Talent Cultivation OpenAI’s launch of Grove reflects a broader effort to secure its influence in the rapidly evolving AI ecosystem, where retaining and attracting top talent is crucial. By offering mentorship to pre-seed founders, the company aims to create a pipeline of AI-driven ventures that could potentially integrate with or complement its own technologies. Recent posts on X highlight enthusiasm from the tech community, with users noting the program’s potential to accelerate startup growth through exclusive access to OpenAI’s resources.

Industry observers see this as OpenAI’s response to competitors like Anthropic and Grok, which have also been aggressive in talent acquisition. The first cohort, limited to about 15 participants, is set to run from October 20 to November 21, 2025, with applications closing on September 24. As detailed in coverage from CNBC, the initiative includes in-person sessions focused on co-building prototypes with OpenAI researchers, underscoring a hands-on approach that differentiates it from traditional accelerator programs.

Benefits and Broader Implications for Startups Participants in Grove stand to gain more than just technical know-how; the program promises a robust network of peers and mentors, which could be invaluable for fundraising and scaling. Early access to unreleased models, as mentioned in reports from NewsBytes, allows founders to test ideas with state-of-the-art AI capabilities, potentially giving them a competitive edge in a market where speed to innovation is key.

This mentorship model aligns with OpenAI’s history of fostering external ecosystems, similar to its past investments in startups through funds like the OpenAI Startup Fund. However, Grove appears more focused on individual founders, particularly those without formal teams or funding, addressing a gap in the startup support system. Insights from The Daily Jagran emphasize how the program could help participants raise capital or refine their business models, drawing on expert guidance to navigate challenges like ethical AI development and market fit.

Challenges and Future Outlook While the program has generated buzz, questions remain about its scalability and inclusivity. With only 15 spots in the initial cohort, selection will be highly competitive, potentially favoring founders with existing connections in the tech world. Recent news on X suggests mixed sentiments, with some praising the initiative for democratizing AI access, while others worry it might reinforce Silicon Valley’s dominance in the field.

Looking ahead, OpenAI plans to run Grove multiple times a year, potentially expanding its reach globally. As covered in TechStory, this could evolve into a cornerstone of OpenAI’s strategy to build a supportive community around its technologies, much like how Y Combinator has shaped the broader startup world. For industry insiders, Grove represents not just a mentorship opportunity but a signal of OpenAI’s commitment to shaping the future of AI entrepreneurship, ensuring that innovative ideas flourish under its umbrella.

Potential Impact on the AI Innovation Ecosystem The introduction of Grove could catalyze a wave of AI startups, particularly in areas like generative models and ethical AI applications, by providing resources that lower barriers to entry. Founders selected for the program will benefit from personalized feedback loops, helping them avoid common pitfalls in AI development such as data biases or scalability issues.

Moreover, this initiative underscores OpenAI’s evolution from a research lab to a multifaceted player in the tech industry. By mentoring early-stage talent, the company may indirectly fuel advancements that enhance its own ecosystem, creating a virtuous cycle of innovation. As the AI sector continues to mature, programs like Grove could play a pivotal role in distributing expertise more evenly, empowering a diverse array of entrepreneurs to contribute to technological progress.



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San Antonio Spa Unveils First AI-Powered Robot Massager

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In the heart of San Antonio, a quiet revolution in wellness technology is unfolding at Float Wellness Spa on Fredericksburg Road. The spa has become the first in the city to introduce the Aescape AI-powered robot massager, a device that promises to blend cutting-edge artificial intelligence with the ancient art of massage therapy. Customers lie face-down on a specialized table, where robotic arms equipped with sensors scan their bodies to deliver personalized treatments, adjusting pressure and techniques in real time based on individual anatomy and preferences.

This innovation arrives amid a broader surge in AI applications within the health and wellness sector, where automation is increasingly tackling labor shortages and consistency issues in human-delivered services. According to a recent feature by Texas Public Radio, the Aescape system at Float Wellness Spa uses advanced algorithms to map muscle tension and provide targeted relief, marking a significant step for Texas in adopting such tech.

Technological Backbone and Operational Mechanics

At its core, the Aescape robot employs a combination of 3D body scanning, machine learning, and haptic feedback to simulate professional massage techniques. Users select from various programs via a touchscreen interface, and the system adapts on the fly, much like a therapist responding to subtle cues. This isn’t mere gimmickry; it’s backed by years of development, with the company raising substantial funds to refine its precision.

In a March 2025 report from Bloomberg, Aescape secured $83 million in funding from investors including Valor Equity Partners and NBA star Kevin Love, underscoring investor confidence in robotic wellness solutions. The technology draws from earlier prototypes showcased at events like CES 2024, where similar AI-driven massage robots demonstrated personalized adaptations to user needs.

Market Expansion and Local Adoption in San Antonio

The rollout in San Antonio follows successful debuts in cities like Los Angeles, as detailed in a December 2024 piece by the Los Angeles Times, which described the experience as precise yet impersonal. At Float Wellness Spa, appointments are now bookable, with sessions priced competitively to attract a mix of tech enthusiasts and those seeking convenient relief from daily stresses.

Posts on X, formerly Twitter, reflect growing public intrigue, with users like tech influencer Mario Nawfal highlighting the robot’s eight axes of motion for deep-tissue work without the awkwardness of human interaction. This sentiment aligns with San Antonio’s burgeoning tech scene, where AI innovations are intersecting with local industries, as noted in recent updates from the San Antonio Express-News.

User Experiences and Industry Implications

Early adopters in San Antonio report a mix of awe and adjustment. One reviewer in a Popular Science article from March 2024 praised the Aescape for its customized convenience, likening it to “the world’s most advanced massage” powered by AI that learns from each session. However, some note the absence of human warmth, a point echoed in an Audacy video report from August 2025, which captured the robot’s debut turning heads in the city.

For industry insiders, this represents a pivot toward scalable wellness tech. With labor costs rising and therapist shortages persistent, robots like Aescape could redefine spa economics, potentially expanding to chains like Equinox. Yet, challenges remain, including regulatory hurdles for AI in healthcare-adjacent fields and ensuring data privacy for body scans.

Future Prospects and Competitive Dynamics

Looking ahead, Aescape’s expansion signals broader trends in robotic automation. A Yahoo Finance piece from August 2025 introduced a competing system, RoboSculptor, which also leverages AI for massage, hinting at an emerging market rivalry. In San Antonio, this could spur further innovation, with local startups like those covered in Nucamp’s tech news roundup exploring AI tools in customer service and beyond.

As AI integrates deeper into personal care, ethical questions arise—will robots supplant human jobs, or augment them? For now, Float Wellness Spa’s offering provides a tangible glimpse into this future, blending Silicon Valley ingenuity with Texas hospitality. Industry watchers will be keen to monitor adoption rates, as success here could accelerate nationwide rollout, transforming how we unwind in an increasingly automated world.



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California AI Regulation Bill SB 1047 Stalls Amid Tech Lobby Pushback

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California’s ambitious push to regulate artificial intelligence has hit another snag, with key legislation stalled amid fierce debates over innovation, safety, and economic impact. Lawmakers had high hopes for 2025, building on previous efforts like the vetoed SB 1047, but recent developments suggest a familiar pattern of delay. According to a report from CalMatters, the state’s proposed AI safety bill, SB 53, which aimed to impose strict testing and oversight on advanced models, remains in limbo as Governor Gavin Newsom weighs his options. This comes after a year of intense lobbying from tech giants and startups alike, highlighting the tension between fostering cutting-edge tech and mitigating potential risks.

The bill’s provisions, including mandatory safety protocols for models trained with massive computational power, have drawn both praise and criticism. Proponents argue it could prevent catastrophic misuse, such as AI-driven cyberattacks or misinformation campaigns, while opponents warn it might stifle California’s tech dominance. Newsom’s previous veto of similar measures cited concerns over overregulation, a sentiment echoed in recent industry feedback.

The Political Tug-of-War Intensifying in Sacramento

As the legislative session nears its end, insiders point to behind-the-scenes negotiations that have bogged down progress. Sources from White & Case LLP note that while some AI bills, like the Generative AI Accountability Act, were signed into law effective January 1, 2025, broader safety frameworks face resistance. This act requires state agencies to conduct risk analyses and ensure ethical AI use, but it stops short of comprehensive private-sector mandates. Meanwhile, posts on X from tech figures like Palmer Luckey express relief over potential federal pre-emption, suggesting that national guidelines might override state efforts to avoid a patchwork of rules.

The delay’s roots trace back to economic pressures. California’s tech sector, home to Silicon Valley heavyweights, contributes massively to the state’s GDP. A Inside Global Tech analysis reveals that over a dozen AI bills advanced this session, covering consumer protections and chatbot safeguards, yet core safety bills like SB 53 are caught in crossfire. Industry leaders argue that vague liability clauses could drive companies to relocate, with estimates from X discussions indicating potential job losses in the thousands.

Economic Ramifications and Industry Pushback

Compliance costs are a flashpoint. A study referenced in posts on X by Will Rinehart, using large language models to model expenses, projects that firms could face $2 million to $6 million in burdens over a decade for automated decision systems under bills like AB 1018. This has mobilized opposition from companies like Anthropic, which paradoxically endorsed some regulations but lobbied against overly burdensome ones, as reported by NBC News via X updates. Startups, in particular, fear being crushed under regulatory weight that Big Tech can absorb, with TechCrunch highlighting how SB 243’s chatbot rules could set precedents for accountability without derailing innovation.

Governor Newsom’s decision looms large, influenced by his national ambitions and the state’s budget woes. Recent web searches show a June 2025 expert report, The California Report on Frontier AI Policy, informing revisions to make the bill less “rigid,” per Al Mayadeen English. Yet, delays persist, with critics on X platforms like @amuse warning that California risks ceding AI leadership to China if regulations become too stringent.

Looking Ahead: Innovation vs. Safeguards

The holdup underscores a broader national debate. While California has enacted laws on deepfakes and AI transparency—such as AB 2013 requiring training data disclosure, as detailed by Mayer Brown—comprehensive AI governance remains elusive. Experts predict that without resolution by year’s end, federal intervention could preempt state actions, a scenario favored by some X commentators like Just Loki.

For industry insiders, this delay offers a reprieve but also uncertainty. Companies are already adapting, with some shifting operations to states like Texas for lighter oversight. As Pillsbury Law outlines, the 18 new AI laws effective in 2025 focus on sectors like healthcare and elections, yet the absence of overarching safety nets leaves gaps. Ultimately, California’s AI regulatory saga reflects the high stakes: balancing technological progress with societal protection in an era where AI’s potential—and perils—are only beginning to unfold.



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