It’s just turned July, but there are all the signs that the advertising industry could be on the cusp of an AI winter.

An unexpected profit warning from WPP sent the advertising agency’s shares down as much as 18% on Wednesday. Shares of rival ad groups, including Omnicom, Publicis, IPG, and Havas, were also down.

WPP said a combination of client losses, a slowdown in new business pitches, and pressured marketer caution amid economic uncertainty meant that its performance since the start of the year had been worse than expected. It forecast that its annual 2025 revenue would decline between 3% and 5%.

While some of WPP’s woes are specific to the company, analysts and other industry insiders told Business Insider the ad group faces challenges that apply to the broader ad agency market.

Madison Avenue is grappling with the advent of AI. The technology can offer agencies opportunities as they help clients figure out how to apply it to their businesses, but also threatens to streamline many of the services they offer, including the creation and placing of ads. These productivity gains also threaten to upend the traditional agency business model of charging hourly rates.

On Wednesday’s trading update, Mark Read, WPP’s outgoing chief executive, quoted data from the research company COMvergence, stating new business pitches so far in 2025 were at a third of the level they were at during the same period last year. Read said this reflected a lower level of marketer confidence, given the prolonged macroeconomic uncertainty. He added that the new business opportunities that are out there tend to be smaller than usual. COMvergence didn’t immediately respond to a request for comment.

Independent media analyst Alex DeGroote told BI that the sharp decline in new business pitches could be a sign of corporate clients replacing some agency services with AI solutions they can use in-house.

“The impact of AI on net new business is hard to quantify, but it is a clear downside risk in our view,” DeGroote said.

Last month, Barclays analysts downgraded the stocks of WPP, IPG, and Omnicom, citing the immediate risks to the agency business posed by artificial intelligence.

WPP’s CEO is leaving, and his successor will inherit a raft of challenges

Ad agencies haven’t been letting AI wash over them without a fight. The largest agency groups, like Publicis and Omnicom, have pledged to invest hundreds of millions in AI over the next few years as they adapt their businesses to harness the technology.

“Agencies and adtech companies thrive on complexity and fragmentation. If advertising is seen as hard to do well, they can charge a premium, whether direct or baked into proprietary products,” said Brian O’Kelley, founder of the sustainability-focused adtech company Scope3 and whose previous adtech company AppNexus received investment from WPP.

AI interfaces “just work,” and that’s a problem for advertising companies, O’Kelley added. He added that the rise of AI search is reducing traffic to publishers and brand websites alike, presenting a challenge to brands looking to get their messages across through online advertising.

For its part, UK-headquartered WPP said it plans to invest £300 million, around $407 million, annually in AI and other technologies. It recently announced an investment in Stability AI, the developer of the AI image generator Stable Diffusion. And it’s prioritizing WPP Open, an AI-powered platform that helps its employees do market research, spin up media plans, and create assets for campaigns using generative AI.

“WPP has the most advanced strategy of any holding company, but clients and investors aren’t waiting for them to finish their transformation,” said O’Kelley.

WPP has lost key clients during its recent slump, like Pfizer and Coca-Cola’s North America account. The company has undergone waves of restructuring in a bid to become more competitive — like the recent merging of its media agency brands to become WPP Media — but the changes and resulting layoffs have “come with some distraction to the business,” Read said on Wednesday.

That’s not to mention the distraction of Read himself announcing in June his exit from WPP this year after more than 30 years with the company. A successor has not yet been named.

Meanwhile, Publicis Groupe is flying high, having topped ad agency new business leagues; the Barclays analysts that recently downgraded the other agency groups, maintained their rating on Publicis, citing its recent strong performance.

Elsewhere, rivals Omnicom and IPG are due to merge to create the world’s largest advertising group — two seismic industry moves that have resulted in WPP dropping down the pecking order.

“It’s clear that more needs to be done to turn WPP’s future around, and while the hunt for a new CEO continues, it’s unlikely that WPP will regain its crown as the world’s biggest advertising agency,” said Aarin Chiekrie, an equity analyst at Hargreaves Lansdown, in a note to clients Wednesday.

5

Fashion trade fairs have long been an industry mainstay: a flurry of handshakes, hurried booth visits, and hopeful encounters in buzzing exhibition halls. But as the cost of attendance rises and ROI becomes less certain, the format many once considered essential is facing a relevance crisis.

That is why Hyperscout, a B2B tech fashion platform launched recently by Dutch fashion veteran Jan Brabers, is on its way to make waves across Europe. Fresh off its showcase at Pitti Uomo, the world’s most influential menswear fair, Hyperscout is offering a radical rethink: use AI-powered matchmaking to bring the right buyers to the right brands, before, during, and after the fair.

“Right now, most fairs are still selling square meters and hoping for foot traffic,” Brabers says. “But brands need results instead of just jangling. Hyperscout is here to deliver exactly that.”

Brabers is no stranger to the inefficiencies of the current system. With nearly two decades in global fashion business development, including buying, brand building, and market expansion, he’s experienced the old model from every angle.

“The ROI for brands attending a tradeshow is still largely a gamble,” he explains. “You fly across the world, pay a small fortune, and hope the right buyer walks past your booth. Meanwhile, the data powering those connections is outdated or non-existent.”

That’s where Hyperscout steps in.

Powered by a proprietary AI engine, the platform profiles and ranks millions of fashion players globally using over 15 commercial and aesthetic variables, everything from product category and price point to target audience and brand DNA. It then delivers precision matches, connecting retailers, brands, and sales agencies through a digital interface that continues working well beyond the trade-show floor. The goal is to turn every interaction into a curated, purposeful exchange and every trade-show into a smart sourcing experience.

Hyperscout’s credibility got an early boost when it was picked up at the e-P Summit in Florence, a prestigious intersection of fashion and tech. Shortly after, the company debuted at Pitti Uomo, where it was used to match exhibitors with qualified buyers ahead of the show.

Pitti Uomo is currently trialing Hyperscout in-house in preparation for their winter show. The hopes are that, instead of relying on serendipity, exhibitors using Hyperscout will have tailored meeting schedules with vetted buyers whose profiles match their commercial goals. For retailers, it will provide previews of aligned brands, allowing for efficient planning and meaningful discovery. “With AI, we’re not removing the human touch,” Brabers stresses. “We’re enhancing it. The real value happens when you meet the right person, not just any person.”

For fashion fairs facing questions about relevance and cost justification, Hyperscout offers a new value proposition.

Organizers can now quantify ROI with post-show metrics, track engagement trends, and expand their global reach by enabling virtual matchmaking. For councils and associations, it’s a way to support local fashion ecosystems by helping designers find international partners without the prohibitive cost of traditional market entry.

“Governments are now interested too,” Brabers notes. “They see this as a way to promote ‘Made in Britain’ or ‘Made in Italy’ in a much more scalable, data-driven way.”

And for young brands, often the most under-resourced players in the market, Hyperscout could be game-changing. With tools for identifying the right retailers and planning market expansion strategically, even a small label without PR muscle or showroom access can chart a viable path toward growth.

“Too often, these brands pour their budget into one big shot, like a showroom in Paris, and just hope for the best,” says Brabers. “We give them something better: proof of concept, market fit analysis, and a smarter way to build wholesale traction.”

The platform isn’t just a tech layer. It’s part of a bigger vision to democratize access to the fashion business. With its app launching this fall, Hyperscout will be available not only to fair organizers and councils but also directly to brands, agents, and retailers. Brabers says the pricing model will remain accessible: “We want as many users as possible. More diversity means better matches.”

Crucially, Hyperscout is not a data-harvesting machine. Contact details aren’t sold or mined for cold outreach. “This isn’t about spam,” Brabers says. “It’s about real conversations with the right people.”

By automating the research and profiling that once took teams weeks or months, the platform allows users to focus on what actually moves fashion forward: creative storytelling, relationship-building, and, yes, touching the fabric.

“Fashion will always need that moment of human connection,” Brabers says. “Our job is to make sure that moment happens between people who are truly meant to meet.”

As the platform continues rolling out with Pitti Uomo and negotiates further deals with global fashion councils, Brabers is focused on scale with purpose. Strategic partnerships, expanded datasets, and smarter onboarding processes are all in motion. But his mission remains clear. “Everyone in fashion is looking to lower risk, cut costs, and expand reach,” he says. “Hyperscout doesn’t replace human instinct. It strengthens it with data. And that’s how we build a more resilient, creative, and connected industry.”

Presented by: APG

When businesses first started using artificial intelligence (AI) for business operations, they were often siloed into performing specific tasks— one model for inventory management, another for pricing, and several for customer service.

But today’s AI models differ significantly from those of years past. With generative AI and open-weight AI, businesses can use best-of-breed specialized AI  to streamline across their business operations.

There are three factors for moving toward more efficient AI models:

1. With open-weight AI, companies will have the ability to fine-tune already-powerful models across various industries.
2. Recently, companies have been working on developing smaller, more efficient AI models, which will enable faster and more cost-effective data processing.
3. The increased availability of cloud computing resources allows companies to deploy and scale AI systems without extensive infrastructure costs.

The result is a new generation of AI that can seamlessly integrate into business operations, optimizing processes and scaling with organizational needs through an evolution that is transforming the business world. Companies are already leveraging these advancements to streamline operations, enhance decision-making, and reduce operational costs.

As AI efficiency continues to improve, adopting these solutions is no longer a matter of speculation. It’s actively reshaping the way businesses function.

Shift to Smaller, More Efficient AI Models

Over the years, AI models have become increasingly powerful, but they have required significant infrastructure to support them as well. Today, the trend is shifting toward smaller, more efficient AI models that provide near-state-of-the-art results while consuming fewer resources. Within the right agentic framework, these compact models are capable of performing complex tasks such as decision-making and delivering insights with remarkable speed.

The move to smaller models is driven by the need for businesses to optimize costs while improving performance. By reducing the size of AI models without compromising their capabilities, companies can run advanced systems on more affordable hardware. This shift also has the added benefit of reducing latency, which is particularly important in industries such as retail, finance, and hospitality, where real-time data processing is particularly crucial.

For businesses, the implications are clear: smaller, more efficient AI models not only reduce the need for extensive computing power but also make AI more accessible, enabling faster implementation and scaling without the high costs traditionally associated with large-scale AI systems.

A Shift Toward Customization

As AI technology matures, businesses are increasingly moving toward customized solutions tailored to their specific needs. While off-the-shelf AI tools can be effective for general tasks, they often lack the depth and specificity required to tackle industry-specific challenges.

More companies are focused on developing AI models trained on their unique datasets, optimizing them for the specific nuances of their operations. This industry-specific approach has led to faster deployments and more relevant AI systems that deliver precise, actionable insights. Whether it’s refining customer segmentation models in retail, improving predictive maintenance in manufacturing, or enhancing personalized guest experiences in hospitality, customized AI models are proving more effective in meeting the specific needs of these sectors.

For businesses, the key takeaway is that AI isn’t a one-size-fits-all solution. Developing tailored AI models allows companies to gain a competitive edge by addressing their unique operational challenges with precision. This move toward customization is not only accelerating the deployment of AI but also increasing its relevance and impact across different industries.

Open-Weight Models

The introduction of open-weight AI models has further accelerated the efficiency of AI applications. Unlike closed systems, which are controlled by a single vendor and often require significant licensing fees, open-weight models allow businesses to access, modify, and deploy AI systems that are customized for their needs.

One of the primary advantages of open-weight AI is the level of control it gives businesses over their systems. Companies can adapt these models to fit their specific operational needs, fine-tuning them to process proprietary data more effectively. Additionally, companies can host open-weight AI models on their own infrastructure, keeping sensitive data in-house while still benefiting from cutting-edge AI capabilities.

The shift to open-weight models has not only reduced the costs associated with proprietary AI solutions but also made AI more accessible to smaller businesses. With the ability to scale AI models more easily and make adjustments as needed, companies can innovate without being dependent on third-party vendors.

Financial, Operational Benefits of AI Efficiency

The increased efficiency of AI models directly impacts a company’s bottom line. Smaller, more efficient models reduce the need for costly hardware and cloud services, enabling businesses to lower their operational costs. Furthermore, the ability to build custom models tailored to specific business functions means AI can deliver more precise results, thereby enhancing decision-making and overall performance.

The impact of AI efficiency isn’t limited to cost savings. By streamlining business processes, AI enables companies to automate routine tasks, minimize human errors, and expedite the time-to-market (TTM) for new products and services. Whether it’s optimizing supply chains, refining marketing strategies, or improving customer support, the financial and operational benefits of AI efficiency are clear.

For organizations already using AI, adopting more efficient models offers an opportunity to further optimize operations, refine existing AI systems, and ensure that AI investments deliver maximum return. As AI continues to evolve, businesses that embrace these advancements will be better positioned to meet the demands of a competitive marketplace.

Key Competitive Advantage

The shift toward more efficient AI models is changing the landscape of business operations. Smaller, more efficient models, customized AI solutions, and open-weight systems are making it possible for businesses to harness the full potential of AI while reducing costs and improving performance. This new generation of AI is not only more accessible but also more adaptable to the specific needs of different industries.

For businesses, integrating these advanced AI systems into operations represents a significant opportunity. As AI continues to evolve, the companies that leverage these advancements will be better equipped to stay ahead of the competition, improve efficiency, and achieve long-term success.

AI efficiency is no longer a future goal but a present reality. Embracing these technologies today is the key to thriving in an increasingly data-driven and competitive market.