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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

By Grant Freeman, edited by Micah Zimmerman

Key Takeaways

  • AI is already becoming a must-have for small businesses, but knowing how to use it well matters more than simply using it.
  • AI can move fast and be incredibly useful, but human judgment, oversight and ongoing learning are still essential.

It wasn’t that long ago that AI felt out of reach to many small business owners. Flash forward to today and AI is helping local businesses manage customer communications, create social media campaigns, schedule appointments, monitor inventory, and so much more.

In fact, Thryv’s recent 2026 AI and Small Business Adoption Survey found:

  • AI adoption among SMBs increased from 55% in 2025 to 66% in 2026
  • 70% report increased revenue
  • 92% say AI saves time.

This data tells us that small business owners are embracing AI in large numbers and experiencing measurable benefits. But the survey surfaced a red flag: 70% of SMBs say they need more AI training.

It’s no longer really a question of whether small businesses are using AI. AI has quickly become table stakes. The bigger question now is whether business owners know how to use it effectively and, more importantly, how to use it in ways that actually make an impact on their business.

What’s interesting is that while seven in ten small business owners say they need more AI training, 86% say they’re already comfortable using AI. That suggests there’s an important gap between being comfortable with the technology and actually knowing how to use it well.

For many business owners, using AI might mean asking ChatGPT a question or generating a social media post. But there’s a lot more to using AI effectively. Understanding how to write better prompts, protect sensitive data, evaluate the quality of AI-generated content and integrate AI into existing workflows can make a significant difference in the results.

Marketing is a good example. A roofer can use AI to create a Google ad in a matter of seconds, but that doesn’t necessarily mean the ad will bring in more customers. If they don’t understand what matters to homeowners in their local market, what questions customers are asking or what drives local search performance for roofing businesses in their area, the final ad may sound polished while still missing the things that actually matter.

That’s the real opportunity (and challenge) with AI. It can help businesses move faster and get more done, but speed alone doesn’t guarantee better results. Without the right knowledge and oversight, it’s easy to trust AI too much and end up somewhere you never intended to go. The goal shouldn’t just be to use AI, but to understand how to use it strategically and make sure it’s working toward the outcomes that matter to the business.

Ad hoc AI education

When it comes to learning AI, most SMBs are figuring it out on their own:

  • YouTube and social media (57%)
  • Online resources and webinars (49%)
  • AI tools themselves (31%)

That DIY mindset fits the entrepreneurial spirit, but it can also lead to knowledge gaps.

For example, a florist might use AI to speed up customer responses, only to accidentally share incorrect promotion dates. The technology worked—the implementation didn’t.

The solution? Learn how to use AI effectively while putting the right safeguards in place. checks and balances.

Four ways small businesses can get smarter about AI

1. Start with a question specific to your business. Many businesses begin by asking an AI agent like ChatGPT, “How can I use ChatGPT to improve my business?”  A better question: “What’s the most repetitive task in my business?”

A plumbing company admin can spend hours responding to after-hours inquiries. By implementing an AI-powered chat assistant trained on common service questions, the staffer arrives each morning with qualified leads already captured and ready to be followed up on.

Lesson: Start with a business problem and solve it with AI.

2. Block out an ‘AI Learning Hour’ every week on your calendar. AI is evolving far too quickly to treat training as a “one and done” event. Put some discipline around how you and your employees approach AI training. Experiment with AI on real business tasks, participate in team sharing sessions, attend webinars or tutorials, learn new prompting techniques and explore new AI features in existing software.

An accounting firm designates Friday mornings for employees to test one new AI use case each week. Over six months, the team identifies several automations that reduce administrative work and improve client responsiveness.

Lesson: Small, consistent learning creates major gains over time.

3. Adopt a “Trust but Verify” mindset. Treat your AI tool like a talented intern, not an experienced decision-maker.

Always check and verify customer communications, financial information, legal language, and business recommendations. This process saves time and maintains accuracy.

Lesson: Human oversight is a “need to have” not a “nice to have.”

4. Turn to Your Business Peers. Resources like social media influencers can be helpful, but some of the most valuable AI lessons can come from other business owners who are facing the same challenges. Connecting with industry associations, local business groups, Chamber of Commerce events, or online communities related to your profession can give you practical insights and real-world examples of what’s actually working.

For example, a partner at a boutique law firm might attend a local Bar Association event focused on AI. After hearing how other firms are successfully using AI, they realize their efforts could go beyond blogging. Instead, they decide to focus on automating client intake and summarizing documents, creating more value for both the firm and its clients.

Lesson: Learn from businesses solving problems like yours. Real-world, industry-specific use cases often provide more value than generic AI tips.

Where small business owners can get AI-educated

There are many good options for business owners looking to elevate their AI literacy. The OpenAI Academy has built a dedicated Small Business learning track, and Anthropic offers an AI Fluency Framework & Foundations curriculum. The U.S. Chamber of Commerce has launched a free practical AI education program aimed specifically at SMBs. Local community colleges are another avenue, with many rapidly expanding their AI education through continuing education programs and AI literacy workshops.

Small businesses don’t need to become AI engineers. They need to become AI-literate. The good news is that AI education is more accessible than ever. The businesses that make learning a priority today will be the ones capturing the greatest value tomorrow.

By Grant Freeman

Grant Freeman is President at Thryv, a global sales and marketing platform for small and medium-sized businesses. He ensures that Thryv’s innovative software and inspired customer teams create highly engaged and happy business owners.

Edited by Micah Zimmerman

Sourced from Entrepreneur

By Melania Watson

Interactive’s head of data and AI, Lizzy Jones, has urged marketing and advertising teams to create workplaces where employees feel comfortable experimenting with AI, making mistakes and sharing unfinished ideas.

Speaking to B&T at the recent TechLeaders conference in Bowral, Jones said businesses needed to move away from cultures where employees feel they must have the “perfect answer” before speaking up, arguing that experimentation is essential as AI tools continue to evolve.

For Jones, creating an “AI-curious” culture is not about mandating the use of AI, but giving employees the time and psychological safety to experiment with it.

At Interactive, employees are given dedicated time to explore AI through fortnightly “AI Playground” sessions, where staff from across the business demonstrate how they are using the technology.

The sessions run for around 30 minutes on Friday lunchtimes, with employees typically spending 10 to 15 minutes showcasing an AI experiment, tool or process.

“Some people have slides. Some people do a bit of a demo. Some people walk through the process they’ve gone to get something up and running,” Jones said. “Some people come and show stuff that’s not quite working yet, so it’s really cool.”

Jones said the approach had helped employees think more laterally about how AI could be applied to their own work, rather than simply learning how to use individual tools.

“It’s how do you think, in a diverse way, laterally about how you could use it and how you could optimise for it?” she said.

The approach is also designed to tackle a cultural barrier Jones said could prevent employees from experimenting in the first place: a fear of being judged.

She said people at Interactive could sometimes be reluctant to speak up unless they were confident they had the right answer, particularly given the company’s mix of experienced technology professionals.

But AI has created an environment where “not everyone knows everything”, making it easier for employees to admit they do not know whether something will work and try it anyway.

“I think the AI thing is really cool because not everyone knows everything,” Jones said. “And so that’s been a really big shift for us, and it’s something we’re still promoting.”

Jones believes leadership also has an important role to play in creating that environment.

At Interactive, senior leaders were encouraged not only to learn about AI, but to demonstrate that they were using it themselves and openly discuss what worked and what did not.

“If you know people at the top are trying it, maybe it’s working, maybe it isn’t, that sort of gives you licence to try things as well,” she said.

Rather than creating additional AI-specific meetings and requirements, Interactive has also incorporated AI discussions into existing team meetings, making it a standing agenda item.

Jones said this helped keep AI “top of mind” without adding another layer of work to employees’ calendars.

Why teams shouldn’t be locked into one model

While Interactive has a company-endorsed AI tool, Jones said she did not believe creative and marketing teams should necessarily be restricted to one model.

The company endorses Microsoft Copilot for business use, particularly from a security and governance perspective, but employees are not prevented from accessing other models such as ChatGPT or Claude through their browsers.

Jones said organisations should continue testing different models because they are evolving at different rates and can produce different results.

She pointed to Microsoft’s efforts to bring multiple models into its Copilot environment, allowing users to compare outputs from different systems.

“I think it’s good to keep testing because they advance at different levels, and so different models are going to be better at doing different things,” Jones said.

However, once a team identifies a model that consistently delivers strong results for a repeatable task, Jones said there was little value in constantly switching between tools.

“You can just go ‘Great this model works really well, I’m not going to even think about it again for another sort of few months,.’”

The key, she argued, is avoiding a “model monopoly” without creating unnecessary noise for employees.

And as AI becomes more embedded in agency and marketing workflows, Jones also expects greater transparency around how the technology is being used.

She suggested agencies could increasingly spell out their AI practices to clients, while AI usage could also become part of contractual discussions.

Some clients may welcome AI if it creates efficiencies, while others may explicitly prohibit its use.

Jones said Interactive already works with some large government clients that have instructed the business not to use AI.

At the same time, she believes agencies could eventually build AI-driven productivity gains into their commercial arrangements, potentially reducing costs as certain processes become more efficient.

“You could start to build into some of your deals is like a productivity gain and you say, ‘Well, maybe we’ll use AI, and our services won’t be as expensive over time’,” she said.

Ultimately, however, Jones said organisations should focus less on forcing employees to use AI and more on giving them the opportunity to learn.

“The message we try to give people is this is your opportunity to learn and the company’s got your back, and we’ll actually create space for you to learn and try,” she said.

“This technology is not going anywhere, so it’s an opt-in thing. We don’t certainly mandate people do it or use it, but we’re trying to inspire them to get on the train, because the train has definitely left the station.”

Feature image credit: Lizzy Jones.

By Melania Watson

Melania is B&T’s senior reporter, covering all things martech and adtech across the industry. When she’s not chasing breaking news, she’s chatting with industry leaders to discuss the big changes in the marketing, advertising, and media landscape. She kicked off her journalism career in 2022 at TV3 in New Zealand as a digital reporter and producer, later moving into a technology reporter role that brought her to Sydney. Driven by a desire to push herself into a new niche, she joined B&T at the start of 2026.

Sourced from B&T

By

As AI continues to reshape the digital marketing landscape, companies are emerging that harness AI and promise not just incremental improvement but genuine structural change. NDA’s interview series, AI Marketing Pioneers, talks to leaders in this space to discover what they are doing differently.

In the latest installment, New Digital Age sat down with Thomas Ives, Founder of RAAS LAB, and Christian McAlinden, Programmatic Director at dentsu. Their conversation tackled the industry’s deep-seated reliance on legacy metrics, why traditional DSPs fall short on context, and how an innovative programmatic campaign for home improvement giant B&Q delivered 813,000 unique creative variants tailored to real-time consumer intent.

Moving past 2015

For nearly two decades, digital advertising has leaned heavily on easily quantifiable metrics like click-through rates (CTR), reach, CPMs, and viewability. Yet, as both Ives and McAlinden argue, those surface-level indicators belong to an earlier technical era.

“Clicks were a metric for a system which had limitations,” says Thomas Ives, Founder of RAAS LAB. “We should be past it now. We should be thinking more about the quality of the placement, the relevance of the placement, and being a lot more intelligent at that impression level.”

Historically, assessing contextual relevance was treated as a creative judgment rather than a measurable media variable because analysing every impression was humanly impossible.

“If you had a campaign which served nine million impressions, it would take 13 and a half years of one person sat at a desk analysing how well that web page matched each impression,” Ives points out.

Christian McAlinden, Programmatic Director at dentsu, agrees that standard industry habits have held buyers back. “Historically, relevance has been  overlooked. Our industry measured what was easiest to measure, reach, CPMs, viewability, frequency, the basic metrics that we’ve always been able to report on,” McAlinden said. “The result was that campaigns could look efficient on paper, but still miss the nuance of whether the message, context, and consumer mindset were actually aligned.”

Speed vs. genuine innovation

While AI is often treated as a fast forward button for existing tasks, true innovation comes from unlocking completely new capabilities. Rather than relying on static site lists or broad keyword rules, RAAS LAB integrates directly into SSPs, scanning hundreds of thousands of incoming URLs per second to evaluate alignment before the ad is served.

“With AI, it’s the first time that we can possibly measure relevance,” Ives said. “There’s two ways that you can use AI. You can do things quicker, which everyone seems to want to use AI for, or you can do new things with it.

What we’re doing here is using AI to do something that wasn’t possible before.”

For agency leaders, evaluating context within the supply path provides a missing layer that standard buying tools can overlook.

“At the moment in the DSPs, we don’t have a relevancy score,” McAlinden said. “I can build out a site list, I can layer targeting in the open exchange, I know where the ads are going to run, but what are we missing? We’re missing a metric for relevancy.”

B&Q and 813,000 creative variants

The real-world value of AI-driven relevance is best illustrated by a recent campaign executed for retailer B&Q in partnership with dentsu.

Retailers with massive product ranges, B&Q carries over 500,000 products, face a major challenge matching creative messaging to diverse customer needs. A consumer researching shed assembly requires a completely different message than someone browsing blue bedroom paints.

“Every user’s needs are very different,” Ives said. “What we’ve deployed for B&Q across multiple campaigns essentially took in 813,000 unique creative variants that were deployed as part of that campaign. It really solves that problem which advertising’s always had: How do you bring together the creative and the targeting thinking?”

The results demonstrated the impact of context over simple reach. The campaign achieved click-through rates 4x higher than standard display benchmarks, maintained a content relevance score over 70%, and drove a statistically significant lift in brand consideration around ‘ease of shopping.’

Previous iterations of this relevance-first approach even generated higher physical store footfall than paid social efforts, said Ives.

The path to adoption

Despite clear performance gains, shifting an entire industry away from legacy habits takes time. Agencies play a critical role in bridging the gap and educating brand marketers who may be hesitant to stray from familiar metrics.

“It’s like everything in advertising, it’s reluctancy. You stick to what you know,” McAlinden said. “The education piece needs to come from the agencies. We need to be taking charge here, encouraging new strategies to move forward.”

With shortening attention spans in a fragmented media environment, Ives believes brands need a new approach to consumer communication.

“To get these attention-poor people to connect with you again is through being relevant,” Ives said. “It’s not just the message itself, it’s a message that matches the moment that the user is currently in.”

By

Sourced from NewDigitalAge

By Laure Malergue

In this article, Laure Malergue of Displayce, explains how agentic AI can bring DOOH into planning earlier, make recommendations easier to defend and connect data, activation and learnings. She explores what makes a strong DOOH agent, why specialist data and workflows matter and how this approach could help the media secure a more influential role in media strategies from the very first planning conversation.

Walk along the Croisette during Cannes Lions, and advertising is everywhere in its most physical form: posters, digital screens, branded spaces and large outdoor moments. This year, another presence was just as visible: AI agents.

For the past two years, most of the AI conversation in advertising has focused on production. This year’s adtech announcements mark a shift from AI-assisted tools towards agentic systems able to make, coordinate and execute advertising decisions.

That shift raises a more important question for the industry: Will AI agents make media decisions better?

Media planning has always been a discipline of choices: Which audience matters most? Which context will make the message stronger? Which channel should enter the plan earlier? Which budget split is easiest to defend? These choices shape the campaign long before it goes live.

For digital out-of-home (DOOH), this could be a turning point.

DOOH is invited too late to the party

DOOH has many of the qualities brands are looking for today: real-world and brand safe visibility, public attention, contextual relevance with the ability to adapt to location, weather, time of day, audience flows and live events. Yet, it still lacks discoverability within the media mix.

Too often, it enters the media plan late, once the brief, strategy and main budget choices have already been shaped. DOOH is then considered at the execution stage, although its strongest value could have influenced the plan earlier.

The challenge is practical. DOOH requires strong knowledge of audience data, mobility patterns, local specificities, screen environments, formats, availability, pricing, context, timing and measurement. The value is clear, but turning these parameters into a confident recommendation takes time.

Programmatic DOOH has solved a large part of the activation challenge by automating the buying process. The next challenge sits earlier, when a media planner decides whether DOOH deserves a place at the table, how much budget it should receive and why one scenario is stronger than another.

Making DOOH easier to recommend

This is where agentic AI can change the role of DOOH in the media mix. The strongest agents will be judged by the quality of the decisions they support: can they explain why one scenario is stronger than another, help a planner defend DOOH in a wider media strategy and turn campaign results into learnings for the next brief?

This is the concept Displayce is bringing to the market with Agentic DOOH: a way to connect the brief, recommendation, activation and learning loop, so DOOH can influence media thinking before the plan is locked.

This conviction comes from more than a decade building programmatic DOOH technology. Our goal is now to make DOOH easier to recommend, with specialist intelligence available inside the environments where agencies and brands are already building media plans.

This is why Displayce’s agents are available via Model Context Protocol (MCP). The MCP allows AI applications such as ChatGPT or Claude to connect with external data sources and tools. For agencies building their own agentic workspaces, this means DOOH intelligence can be accessed where planners already work. For Displayce, MCP is a way to bring specialist DOOH expertise into the existing media ecosystem, with more openness and interoperability.

What makes a good agent for DOOH?

A generic AI model can understand language, but it does not understand media by itself. For DOOH, a good agent needs to be built on a strong data ecosystem, expert workflows and simulation algorithms. This is the foundation behind Displayce’s agents.

The data ecosystem gives the agent access to the signals that matter: audience data, mobility patterns, inventory context, screen environments, local context and historical campaign performance.

The expert workflow helps the agent understand how DOOH planning actually works: the objective, the constraints, context, timing and budget, and the level of explanation a planner needs to defend a recommendation.

The simulation algorithm allows the agent to compare scenarios, test assumptions and understand why a city, venue type, screen, audience group, daypart or context is relevant.

Without these foundations, an agent can create a recommendation that looks convincing but has limited media value. With them, it can support stronger, clearer and more transparent DOOH decisions.

Make DOOH impossible to ignore from the very first media conversation

By bringing DOOH expertise into planning workflows earlier, Agentic DOOH can change how the medium is considered by agencies, brands and media owners.

For agencies, this means less time assembling data and more time shaping the argument. For brands, it means clearer recommendations and greater confidence in DOOH investment. For media owners, it means making local knowledge and premium inventory visible while the plan is still being shaped.

Human judgment remains central. Planners still decide what is right for the brand, the role of agents is to give teams better scenarios, stronger explanations and more time to think.

Agentic DOOH should be measured by one question: Does it help the medium earn a stronger place in the media plan? If the answer is yes, AI agents will help bring DOOH further upstream in the media decision-making process, at the moment when budgets are shaped, strategies are built, and channel choices are made.

 

By Laure Malergue

Sourced from The Drum

BY ASHLEY COUTO

New data shows AI assistants cite independent YouTube creators more than brand-owned content. Founders who adjust their creator briefs now will get pulled into the answers that drive buying decisions.

YouTube creator content now appears in more than 25 percent of prompts answered by AI assistants, according to new research from Jellyfish shared exclusively with Adweek. In high-intent categories like consumer electronics and financial services, nearly one in two references come from Youtube.

The Jellyfish team analyzed 27 million responses across seven AI search assistants and found that independent, niche creators consistently outrank brand-owned content and celebrity influencers in AI-generated answers.

For founders, that means the customer journey is being rerouted through creators they may have never considered partnering with. While Reddit used to be the belle of the AI search ball, YouTube has overtaken it. Here are five things founders can change about their creator strategy to get pulled into AI search results.

Look beyond celebrity influencers

Jellyfish analyzed responses across Claude, ChatGPT, Gemini, DeepSeek, Meta AI and Perplexity. Smaller creators consistently came out on top, especially those with high niche authority as opposed to more generalized creators.

An OtterlyAI study of more than 100 million citations found that roughly 41 percent of cited YouTube videos had fewer than 1,000 views and 36 percent had fewer than 15 likes. The median cited channel had posted fewer than 41 total videos.

That means a micro-creator with 2,000 subscribers and a well-structured tutorial can earn more AI citations than a high-profile partnership. AI systems prioritize reference value over popularity metrics, according to OtterlyAI’s analysis.

Prioritize long-form video over short-form

Jellyfish’s data favored videos longer than 10 minutes. OtterlyAI found AI platforms barely cited YouTube Shorts at all outside Google’s own surfaces, with 94 percent of cited YouTube videos qualifying as long-form.

That contradicts the standard creator brief at most startups, which still asks for 30-second cuts optimized for TikTok and Reels. To surface your brand in AI search assistants, fund a structured 12-minute walkthrough rather than a punchy clip.

Insist on chapters and timestamps

Only 31 percent of cited YouTube videos have chapter structure, according to OtterlyAI’s research. That gap represents an opportunity competitors haven’t closed.

Chapters turn one video into multiple citable units. When a creator adds timestamps for “unboxing,” “setup,” “first impressions” and “comparison,” each section becomes its own potential AI citation.

Make this a line item in your creator brief. Pay for the editing time required to get the structure right.

Prioritize topical authority over follower count

A detailed YouTube review from a recognized creator now carries more citation weight than a Reddit thread with mixed opinions, according to a Superlines analysis. AI systems read subscriber counts, channel history and stated credentials as authority signals.

The right questions to ask when vetting creators are whether they consistently cover the category and whether their channel has a clear topical focus. A creator who posts consistently about makeup for olive skintones is a better bet than a generalist with five times the audience for AI search.

Track AI citations as an influencer success metric

AI search traffic converts at 14.2 percent, compared with 2.8 percent for traditional Google search, according to data cited in PikaSEO’s analysis. That conversion gap is too significant to leave unmeasured.

Adding creator-sourced AI citations as a tracked metric, alongside backlinks and branded search volume, gives founders a clearer picture of which partnerships are working. Most creators aren’t well versed in the intricacies of SEO, let alone answer engine optimization (AEO) and generative engine optimization (GEO), so you’ll likely need to coach on keywords and structure to maximize generative search capabilities.

AI assistants are sending searchers to video creators, and the brands those creators reference will end up with the sale. Founders who adjust their creator strategy now will get cited while their category competitors catch up.

Feature image credit: Illustration: Getty Images

BY ASHLEY COUTO

Sourced from Inc.

BY JOEL COMM

Paid social is a leading digital marketing option for brand awareness and product discovery. Here’s how AI is taking it to the next level.

Digital marketing used to be innovative, but now it’s the norm. Statista estimates that 82.4 percent of total ad spending will be digital by 2030. Success is no longer about simply showing up online; it’s about the specific strategy you’re using.

Tossing artificial intelligence into the mix makes the answer even more relevant. I looked at two major segments of digital advertising: paid social and paid search. I wanted to see how AI was reshaping the digital advertising conversation around these two critical points.

The results were notable. Let’s dig in.

The shift toward paid social in an AI-driven landscape

Google used to dominate search. Sure, some people shopped on Facebook, but in reality, most people turned to Google or even Amazon when they needed to buy something.

That balance appears to be shifting. One recent report found more than 50 percent of social media users are on the lookout for new things to buy. Specifically:

  • 26.7 percent of social media users are looking for either new activities or items to purchase.
  • 26.1 percent of those on social media are actively looking for products to buy.

Add those two numbers together, and more than one in two people are using social media to discover things they may want to buy. Other marketers, like Sixty Gelu, already put the number at 60 percent, which is especially notable now that Google search traffic is facing growing pressure. Yes, AI tools are coming in their wake, but these haven’t been as widely adopted for e-commerce yet.

AI-powered tools like ChatGPT and Claude are changing how consumers research products and gather information. But in the end, social platforms remain one of, if not the, primary drivers of product discovery and brand engagement.

That is the first trend marketers should pay attention to: the landscape has changed. Paid search may not be your only priority anymore. You need to make sure you have a foot in the paid social world, too. Start by auditing your budget and moving a small percentage of your search spend into targeted social testing to meet these buyers where they are.

Navigating customer journeys on social media

Paid social ads are the first step, but they aren’t the whole story. An initial branded encounter on a social media platform is just the beginning of the customer journey. In fact, the best uses of AI go far beyond the top of the funnel.

This shift is forcing an evolution in performance metrics.

To counter this, many performance marketing teams are using AI-assisted workflows to maximize lead quality in real time. For example, agencies like Unicorn Marketers use AI to evaluate incoming leads and filter out low-intent prospects before they drain sales teams’ time and resources.

By using advanced algorithms to continuously audit live campaigns, you can help ensure your creative assets and targeting parameters consistently perform well for high-intent buyers. It can review real-time, granular data at a scale that would be difficult for a human to match. AI is also helping marketers understand why certain ads outperform others. Platforms like Motion analyse creative performance across thousands of ads, helping marketing teams identify the images, messaging and formats that consistently drive stronger engagement and conversions.

Building an AI-backed social strategy in 2026 requires moving past the trap of chasing the latest shiny tool. To avoid the common mistake of over-automation, you should focus on strategy, clean data and intentional integrations by implementing a few tactical steps:

  1. Defining the use of an AI tool in your paid social strategy before investing in it.
  2. Avoiding the “magic bullet” approach to AI by looking for quality data to drive decisions.
  3. Piloting one new AI tool at a time, letting it settle and then adjusting based on the results you get.
  4. Maintain the “human” element behind your AI tools that help preserve nuance and brand voice. (i.e., don’t over-automate!)

Effectively (and profitably) reshaping paid social with AI

Social media is a well-established option that millions use when they want to find something to buy. This has helped position paid social as a major force in digital ad spend and the future of digital marketing.

As AI handles the operational and executional work around paid social, it’s critical to step back from the dashboard and evaluate your broader positioning. Make sure you’re integrating AI with intention while keeping the role of human creativity, strategic thinking and brand positioning in focus. This ensures your automated tools are serving a distinct business objective rather than just generating noise.

Feature image credit: Adobe Stock

BY JOEL COMM

AUTHOR AND SPEAKER @JOELCOMM

Sourced from Inc.

By Hunter Schwarz

These ads might be fake, but they skewer a real problem: AI ‘slop voice’ that made nonsense the new normal.

There’s a specific voice and vagueness to technology advertising today.

The ads are often for startups you’ve never heard of selling a service or software that’s somehow related to AI. And while the ad voice is direct, in that it’s written as if it’s speaking directly to you, the viewer, the copy is intentionally cryptic. “Own Your Inference.” “Put AI Agents to Work for People.” Sometimes it’s menacing. “Stop Hiring Humans.”

These kinds of ads seem to be everywhere lately, but that doesn’t mean that they make much sense. Now, comedians Harris Alterman and Dave Ross are emphasizing just how banal and meaningless the AI ad age is turning out to be by creating their own fake tech ads that skewer the medium simply by amping up its tropes: AI industry gobbledegook and design minimalism.

[Photo: courtesy Harris Alterman and Dave Ross]

The ads, which they put up as banners in a New York City subway station (much like the controversial, real ads for the AI companion Friend last fall) ask asinine questions. “What if forks were spoons?,” “What if Texas was upside-down?,” and “What if the Rizzler was purple?” One fake ad is for a company with a human name, “Dennis.”

[Photo: courtesy Harris Alterman and Dave Ross]

Another advertises a faux company that recently rebranded. “Zipline is now Froggle,” the ad says matter-of-factly. “The cloud-based online safety you know and love, now in the palm of your hand.” An ad for a brand called Fivetable confidently states “We Put the Q in QR1777,” and Wireflow promises “you pay us, we pay you.”

[Photo: courtesy Harris Alterman and Dave Ross]

Alterman and Ross were especially inspired by a real ad for the product development software company Linear, which shows cursors pointing toward God’s outstretched hand, as in Michelangelo’s “The Creation of Adam,” and another for Dawn, an AI mental-health app, that says “Racing Thoughts Don’t Do Waiting Rooms.”

[Photo: courtesy Harris Alterman and Dave Ross]

They call the lack of distinctiveness around AI advertising in its design, voice, and fonts “slop voice,” and note that while these ads sound like they’re speaking to you, they’re really talking to someone else: a high tech, SaaS-speaking in-group. And it’s ok if the copy alienates everyone else.

[Photo: courtesy Harris Alterman and Dave Ross]

“People are confused by tech advertising,” they tell Fast Company in an email. “99% of the people reading these ads have no idea what they’re talking about. It feels like 20 people in tech, advertising to 20 other people in tech. Do you really need to put up ads? Can’t you guys just get in a group chat together?”

[Photo: courtesy Harris Alterman and Dave Ross]

That’s how Alterman and Ross made their ads, after all. The comedians wrote them together. Ross then designed the ads in Photoshop and made websites for them using HTML/CSS and Javascript. While their fake ads aren’t for real tech companies, they are selling something. The comedians put up a merch shop under their brand called Goofstump.

Alterman says they plan to make more fake ads, and they’d like to partner with the MTA to make an official art installation. Though they’ve set up websites for some of the parody brands, he says they didn’t pay for all the URLs they’re advertising. The URL for sellyourposessions, for example—”Are You Poor? Sell Your Possessions,” is the tagline—costs about $700, or “a little too much for a joke,” he says. Especially in times like these.

Feature image credit: courtesy Harris Alderman and Dave Ross

By Hunter Schwarz

Hunter Schwarz is a Fast Company contributor who covers the intersection of design and advertising, branding, business, civics, fashion, fonts, packaging, politics, sports, and technology.. Hunter is the author of Yello, a newsletter about political persuasion More

Sourced from FastCompany

By 

AI speeds up advertising, but without strategy it risks making brands forgettable

For small businesses, AI has made advertising easier to produce but much harder to differentiate.

What should have been a useful shortcut is quickly becoming a creative trap. As more brands rely on the same tools to write copy, shape campaigns and generate ideas, too much of the output is starting to feel interchangeable.

The ads may look polished enough to publish, but polish is not the same as impact, and efficiency is not the same as originality.

That matters because most ads are not competing in a vacuum. They are fighting for attention in crowded feeds, against endless lookalike content, in front of audiences who have become highly skilled at filtering out anything that feels generic.

When brands use AI without a clear point of view, they do not just risk making weaker creative. They risk making work that disappears on contact.

The problem is not AI, it’s the way many businesses are using it.

Patterns & performance

Most generative AI tools are built on patterns. They are good at producing what is probable, what is familiar and what already resembles successful marketing. That can help with speed, but it also creates sameness.

Similar phrasing, similar structure, similar claims, similar tone. After a while, entire categories begin to sound like they were written by the same person for the same audience, regardless of who is actually selling the product.

For brands trying to grow, that is a serious commercial issue.

When advertising starts to blend in, performance usually follows. Ads that feel vague or formulaic tend to attract less curiosity, fewer clicks and weaker engagement. Businesses then spend more trying to force results from creative that never had enough edge in the first place.

The waste is not always obvious at first, which is partly why the problem is spreading. A campaign can still be technically competent while quietly underperforming where it counts.

Over time, that gap compounds. Budgets get allocated based on surface-level performance rather than true effectiveness, and teams double down on what feels safe instead of what actually works.

The result is more output, more spend, and very little movement in terms of brand recognition or recall.

For SMEs, the stakes are even higher.

Solving the wrong problems

Big brands can sometimes get away with forgettable advertising because they already have reach, recognition and budget on their side. Smaller businesses do not. They depend far more heavily on clarity, distinctiveness and trust.

Their marketing has to do more with less, which means the brand itself needs to be sharper, not flatter. If AI strips out the character, specificity or conviction that made a business memorable in the first place, it starts eroding one of the few real advantages that smaller brands have.

That is the irony in all this. Many businesses are using AI to save time and strengthen output, only to end up producing ads that weaken the very thing they are trying to build.

Solving the wrong problem

The issue usually starts long before anything goes live. Too many marketers are asking AI to solve the wrong problem. They are using it to generate finished ads before they have properly defined what the brand wants to say, who it needs to resonate with, or why anyone should care. When the strategic thinking is thin, AI does not improve it. It simply accelerates it.

That is why so much AI-assisted advertising feels hollow. It fills space nicely, but it rarely lands with force. It often says the sort of thing a brand should say, in the sort of tone a marketer expects, without ever arriving at something sharp enough to be remembered.

There is also a growing risk around brand dilution. When multiple teams, agencies or founders rely on similar prompts and tools, the outputs begin to converge. Without strong internal direction, even well-intentioned campaigns can start to blur together, weakening long-term brand equity in ways that are difficult to reverse.

Taking a clearer position

The brands getting this right are taking a more disciplined approach. They are not handing the whole process over to a tool and hoping for the best. They are using AI to support execution while keeping the core thinking firmly human. That means using it to test routes, speed up production and explore variations, but not to define the message.

They are also investing more time upfront. Clear positioning, sharper audience insight and stronger creative direction are doing the heavy lifting, with AI acting as an amplifier rather than a substitute. That shift alone is often the difference between content that performs and content that fades.

Good advertising has always depended on knowing what makes a business distinct and then expressing it clearly. That has not changed. If anything, it matters more now. In a market flooded with passable content, originality has become more valuable, not less.

AI can help marketers move faster, but speed only matters if you are moving in the right direction.

We’ve listed the best email marketing platforms.

Feature image credit: Getty Images

By 

SEO & Digital PR search specialist at Cupid PR.

Sourced from techradar.pro

By Krystal Scanlon

OpenAI is poised to automate one of advertisers’ most laborious tasks: ad creative.

Over the past four months of its ad pilot, OpenAI has required advertisers to upload the creative they want to submit, while the platform supports ad delivery. But now, OpenAI is going one step further in the process, by helping advertisers automate the mass production of creative, according to an updated section of its Ad Tools Term Document.

“OpenAI may make available AI-powered Creative Tools that allow you to generate, modify, transform, optimize, localize, or translate advertising creatives using Ad Materials,” the updated policy said.

That’s where OpenAI’s intention would stop. It has no intention of taking accountability for what the system produces. The policy states that the advertiser is “responsible” for reviewing the creatives and ensuring they are accurate and compliant where necessary.

“OpenAI is not responsible for errors, omissions, outdated information, or inconsistencies in Ad Materials or for Claims or losses arising from Generated Creatives that you approve or use,” the policy stated.

The move is hardly surprising, according to eMarketer’s principal analyst, AI in marketing and commerce, Nate Elliott. As he put it, it makes sense for OpenAI to throw AI at their ad operation.

“They know as well as anyone the power of AI for enterprise workflows; it’d be shocking if they didn’t tap that capacity for something they hope will become a major source of revenue,” he said. “And if they’re trying to make billions selling this type of capability to other companies, then they’d better very well eat their own dog food.”

It would also make it more competitive. Every major platform has spent years automating more of the buying process, which has made creative the last real variable. The logic being that once algorithmic optimization took over media buying, the bottleneck shifted upstream to creative. Platforms had a structural incentive to close that gap. More variants mean more auction signals, more liquidity, and ultimately more revenue.

“Marketers are moving faster than ever, and having proven creative assets ready to deploy lowers the barrier to experimenting with new advertising channels like ChatGPT,” said Brian Quinn, president and general manager of AppsFlyer. “But the real test will be campaign performance. If OpenAI can help brands launch seamlessly, demonstrate measurable results, and shorten the path from testing to scale, advertisers won’t just come back. They’ll commit larger budgets and make ChatGPT a meaningful part of their media mix.”

The creative tools aren’t the only update to OpenAI’s growing ad suite.

The AI platform has added conversion tracking for app installs and app opens, according to screenshots verified by Digiday. Ads for apps is a major category in online marketing, so laying the groundwork to track measurable actions such as installs, opens and purchases, would not only attract app marketers, but also provide OpenAI with another justification for more ad spend. And given how fast things are moving, the company clearly expects more ad dollars to start trickling in, having updated the daily ad budget from $100 to $200.

These latest updates show that OpenAI is moving beyond just selling inventory, toward building the complete infrastructure advertisers expect from mature platforms. Having the ability to produce creative at scale helps to solve the problem of having enough creative to feed the AI systems, while advancing conversion tracking helps to prove that those ads drive outcomes. And ultimately, these additions represent another step toward OpenAI turning its four-month old ad pilot into a fully-fledged ad business.

Feature image credit: Ivy Liu

By Krystal Scanlon

Sourced from DIGIDAY