As AI accelerates its takeover of creative workflows—from image generation to personalized marketing copy—the industry has been sprinting toward efficiency while sprinting past responsibility. Marketers want speed. Creators want protection. Consumers want transparency. Regulators want clarity. And the technology itself? It never stops moving.

Santa Cruz Software believes the industry desperately needs a reset.

Today, the company announced Santa Cruz Software Labs, a dedicated initiative built not to chase the AI hype cycle but to shape what comes after it: a foundation for ethical, transparent, evidence-based AI in creative and marketing technology.

The goal isn’t to slow innovation. Instead, it’s to make sure innovation doesn’t bulldoze over authorship, data privacy, creative ownership, or human judgment.

“Responsible AI in marketing is not just about what technology can do — it's about what it should do,” said Luis Mendes, Innovative Solutions Expert at Santa Cruz Software Labs. It’s a pointed reminder in an era where AI tools routinely generate art without attribution and analyze customer data without always asking permission.

Santa Cruz Software Labs aims to create a space where marketers, agencies, technologists, and creators can experiment with AI — but with guardrails.

Why an Ethical AI Lab — and Why Now?

AI in creative technology is evolving faster than any previous marketing innovation. That speed means two things:

1. Brands are adopting tools they don’t fully understand

2. Creators worry their work is being absorbed into datasets with no recourse

The tension is real. Generative AI is transforming content creation, but the ethics behind training data, model transparency, and authorship rights remain murky.

Santa Cruz Software Labs enters the scene at a moment when:

• AI-generated creative is flooding digital platforms

• Consumer trust in AI-driven experiences is under scrutiny

• Copyright lawsuits are reshaping expectations for AI training data

• Marketing teams are under pressure to adopt AI but fear unintended consequences

• Regulators globally are sketching the first outlines of AI governance

In short: the market needs practical standards and transparent tools, not marketing gloss.

Inside Santa Cruz Software Labs: A Framework for Ethical AI

The lab anchors itself around three pillars that form a feedback loop between research, experimentation, and governance.

1. Research & Surveys: Evidence Over Assumptions

Santa Cruz Software Labs will publish ongoing studies exploring:

• How teams actually use AI in creative workflows

• Where they struggle (ethics, bias, reliability, quality control)

• How fast-paced AI adoption affects creators, agencies, and enterprise marketing teams

• What makes users trust — or distrust — AI tools

• Whether consumers understand when content is AI-generated

These findings will be crucial for marketers who want to defend AI investments with data, not storytelling.

Right now, most teams are operating on instinct, vendor claims, or competitive pressure. Santa Cruz wants to pivot the industry toward evidence-based adoption.

2. Tech Experiments & Early-Access Prototypes: Hands-On Innovation

The lab will offer access to early-stage prototypes built by Santa Cruz Software’s engineering team. These are not polished commercial tools — they’re experimental playgrounds.

Marketers and creators will get to:

• Test new AI-driven creative concepts

• Explore efficiency gains without sacrificing control

• Validate what workflows are enhanced — and which ones break

• Provide direct feedback that shapes real product direction

• Influence AI features before they reach the mainstream

Think of it as a wind tunnel for AI ideas: test, refine, stress-test, validate.

3. AI Code of Ethics: Guardrails for the Future of Creative Tech

The Lab’s AI Code of Ethics is its most consequential component — and the one likely to resonate widely across the industry.

Built around three commitments:

• Ethical data stewardship

• Human-centered intelligence

• Transparent, accountable innovation

The code aims to answer questions the industry is still wrestling with:

• How do you ensure AI models respect copyright?

• How do you prevent “black box” creative decisions?

• How do you design AI that augments human creativity rather than replace it?

• How do you ensure consumers know when AI is being used?

• How do you prevent marketing AI from becoming surveillance AI?

These aren’t academic questions. They define the next decade of digital marketing.

Marketing Teams Are Hungry for AI — But Fear Its Consequences

Santa Cruz Software’s timing is strategic. Most marketing organizations are:

• Increasing their AI budgets

• Testing new generative tools

• Integrating AI into design platforms

• Experimenting with personalized content at scale

But they also express major concerns:

• Dataset transparency

• Creative ownership

• The potential loss of originality

• Overreliance on machine-generated content

• Difficulty validating AI outputs

• Fear of brand risk from ungoverned AI usage

AI skepticism is rising at the same time AI adoption is accelerating — which is precisely the gap the Lab aims to close.

The Industry Has Been Moving Too Fast — and It Shows

One reason this initiative stands out is because most AI innovation in marketing has followed a different playbook:

• Release tool

• Market benefits

• Add features

• Scale adoption

• Then think about ethics

Santa Cruz Software Labs flips that script.

Here, ethics isn’t a compliance afterthought; it’s the foundation.

This is a sharp contrast to companies that launch AI features first and worry about safeguards later — a pattern that has already led to:

• Accidental copyright violations

• Unintended data exposure

• Questionable algorithmic decisions

• Consumer backlash

There’s an opportunity for Santa Cruz Software Labs to set a precedent for the industry.

A New Hub for Collaboration, Community, and Accountability

The Lab is not positioning itself as the sole authority on AI ethics — instead, it wants to be a collaborative hub.

The initiative plans to:

• Publish regular research reports

• Release public demos

• Invite community participation

• Host discussions across marketing, design, and tech

• Spotlight best practices from across the industry

• Encourage shared standards rather than proprietary definitions

This matters because AI is reshaping marketing faster than any single organization can control. The industry needs consensus, not isolated guidelines.

Ethical AI Is Becoming a Competitive Advantage

Marketers have long competed on channels, creative, data, and performance metrics. In the next phase of digital marketing, they’ll compete on:

• Trust

• Transparency

• Provenance

• Creative integrity

• Data respect

• Authenticity

AI will help brands create more content than ever — but ethical AI will help them create content consumers believe in.

Santa Cruz Software Labs is betting big on a simple truth: the future of marketing belongs to brands that innovate responsibly.

And as generative AI continues blurring the line between inspiration and imitation, that bet looks increasingly smart.

The Bottom Line

The launch of Santa Cruz Software Labs signals a shift from reactive AI adoption to principled, proactive innovation.

For marketers and creative teams navigating a world where AI is powerful, unpredictable, and often misunderstood, the Lab offers something the industry has lacked: a structured, transparent place to explore AI without compromising ethics or creativity.

As AI continues to redefine digital marketing, Santa Cruz Software Labs is urging the industry to slow down — not to stop progress, but to ensure we’re building something worth accelerating.

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It’s no secret that marketing leaders are under mounting pressure to “do something with AI.” Boards want faster growth. Executives want efficiency. Teams want clarity. Vendors want budget. And the industry at large is drowning in lofty promises, abstract frameworks, and slide decks that over-index on possibility rather than practicality.

MatrixPoint, a digital strategy consultancy, believes marketers need less hype and more direction. Today, the firm introduced the Marketing AI Accelerator, a structured program designed to help organizations identify high-impact AI opportunities — not by evaluating technology in the abstract, but by grounding decisions in real-world, proven use cases.

The premise is simple but refreshing: instead of spending months analyzing readiness, capability maturity, or theoretical ROI models, start with what already works. Then decide how — and whether — those solutions can map onto real marketing priorities.

Why Use Cases Matter More Than AI Theory

For years, the marketing ecosystem has been captivated by AI frameworks, vendor demos, and increasingly technical roadmaps. But in reality, most marketing teams struggle with a much more basic question:
Where, exactly, should we apply AI first?

MatrixPoint argues that the industry’s obsession with capability assessments often creates analysis paralysis. Leaders want action; teams want clarity; and no one wants to be the company that experimented for 12 months only to realize they never solved a real business problem.

“Brand leaders are under pressure to demonstrate AI progress but face competing priorities and unclear paths forward,” said Eran Goren, Managing Principal at MatrixPoint. “The Marketing AI Accelerator gives senior leaders a clear framework for deciding where AI can drive impact quickly and effectively.”

Put simply: less theoretical alignment sessions, more practical ROI.

Inside the Marketing AI Accelerator: A Pragmatic Playbook

MatrixPoint’s Accelerator is built around a three-phased methodology designed to compress the time between curiosity and implementation — while avoiding the common pitfall of chasing AI for AI’s sake.

1. Discovery & Prioritization Workshop

MatrixPoint engages cross-functional teams with a curated library of proven marketing AI use cases — spanning personalization, content automation, audience modeling, predictive intelligence, and more. The workshop isn’t just an idea dump; it’s structured to identify which use cases align with strategic priorities and operational constraints.

2. Return & Readiness Measurement

Traditionally, organizations start with a readiness assessment, spending months diagnosing systems, data hygiene, governance maturity, and resource availability — only to discover that some initiatives were never viable to begin with.

MatrixPoint flips this order.
Use cases come first.
Feasibility comes second.

By working backward from validated use cases, the Accelerator determines whether the organization is actually equipped to implement the ideas that matter — not the ones that merely sound innovative.

3. Implementation Roadmap for High-Potential AI Opportunities

Once priorities and feasibility align, the program delivers a detailed roadmap for execution. This includes:

• Required data sources

• Technology considerations

• Workflow implications

• Cross-team dependencies

• Talent and training needs

• Time-to-value projections

Senior leaders get a practical, actionable blueprint — not a 150-slide maturity assessment.

Why This Approach Lands With CMOs Right Now

Marketing AI is reaching its consolidation phase. Generative engines aren’t novelties anymore; they’re standard tools. Predictive models aren’t special; they’re expected. And personalization isn’t an experiment; it’s table stakes.

What’s actually rare is clarity.

MatrixPoint’s approach mirrors what’s happening in finance, supply chain, and operations: a shift toward use-case-first AI evaluation, where organizations focus on outcomes before infrastructure. It’s a methodology already embraced by leaders in AI-heavy sectors — but comparatively new in marketing.

Steve King, Principal at MatrixPoint Consulting, underscores this point:
“Most organizations spend months on readiness assessments without validating whether their AI initiatives will actually solve business problems. We reverse that.”

The Accelerator pushes CMOs toward prioritizing business alignment instead of technical aspiration — something the marketing industry has needed for years.

A Growing Trend: Pragmatism Over Possibility

Across the broader market, the AI conversation is maturing:

• Brands are shifting from experimentation to measurable impact

• Boards are demanding cost savings and efficiency, not prototypes

• Teams want AI that integrates into existing workflows

• Vendor fatigue is real — and rising

• Risk, governance, and data privacy concerns continue to increase

MatrixPoint’s use-case-first model reflects a shift away from experimental AI toward operational AI: solutions that are ready, proven, and context-specific.

This is also a subtle but important contrast to many consulting approaches, which often sell AI strategies but deliver frameworks with limited execution value. MatrixPoint positions the Accelerator as a shortcut through that fog.

Supporting the Launch: A White Paper for CMOs

To broaden the impact of the Accelerator, MatrixPoint also released a white paper titled:
“The Use Case Advantage: How Leading CMOs Prioritize Marketing AI Initiatives.”

The white paper outlines:

• Why traditional AI planning processes break down

• Common pitfalls that derail AI adoption

• A framework for evaluating impact, feasibility, and readiness

• Real examples of marketing use cases delivering measurable ROI

• How top CMOs build AI portfolios aligned with business strategy

It’s written for senior marketing executives who need defensible, business-aligned logic behind their AI decisions — not just inspiration.

The Bigger Picture: Making AI Practical, Not Theoretical

MatrixPoint’s Marketing AI Accelerator arrives as marketing enters a phase where AI isn’t optional but neither is unstructured experimentation.

Organizations know they need to adopt AI, but they’re unsure:

• Where to start

• What problems AI should solve

• What to prioritize

• How to avoid wasted investment

• How to measure outcomes

• How to build internal confidence

The Accelerator aims to remove that uncertainty by anchoring decision-making in use cases that are already delivering results across the industry — making AI less mysterious, less abstract, and far more actionable.

Bottom Line

MatrixPoint isn’t launching another AI framework or high-level consulting model — it’s launching a pragmatic system for identifying, validating, and implementing AI where it actually matters.

For CMOs and marketing teams struggling to translate AI ambition into executive-ready plans, the Marketing AI Accelerator offers something the industry has been missing: speed, clarity, and grounded decision-making.

AI adoption may be complex, but prioritization doesn’t have to be. And MatrixPoint is betting that the fastest path to enterprise AI success starts not with assessing maturity — but with understanding what already works.

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Adobe is doubling down on AI-powered marketing. The company announced a $1.9 billion deal to acquire Semrush, a widely used SEO and brand visibility platform. Adobe will pay $12 per share in cash, and the deal should close in the first half of 2026. Semrush stock surged more than 70% after the news, while Adobe shares dipped about 2%.

This move marks Adobe’s biggest push yet into AI search optimization. The company wants to strengthen its grip on digital experience tools at a time when algorithms, not humans, shape discovery. The shift toward LLM search is accelerating, and Adobe intends to meet that moment.

Semrush offers tools that thousands of marketers use to track visibility, manage SEO, and monitor audience reach. It also brings a decade of expertise in what it calls generative engine optimization. The approach blends search optimization with AI-driven discovery across platforms such as ChatGPT and Google Gemini. Brands need those tools as customers increasingly rely on AI agents for recommendations and decisions.

Adobe argues that this shift will redefine how brands stay visible. The company cites new Adobe Analytics data showing a massive 1,200% spike in traffic from AI-driven sources to U.S. retail sites over the past year. That growth signals a new distribution channel. It also explains why Adobe wants Semrush before the landscape moves further.

Marketers feel the pressure as search habits evolve. Traditional SEO is only part of the equation now. AI summaries and chat-based search reshape what customers see and trust. Many CMOs know that visibility is slipping into algorithmic black boxes. Adobe wants to sell them a way out. Semrush’s tools will fold into Adobe’s stack, joining AEM, Adobe Analytics, and Adobe’s emerging Brand Concierge service.

Semrush enters this acquisition with momentum. The company recently reported 33% year-over-year ARR growth in its enterprise segment. Its biggest customers include Amazon, JPMorgan Chase, and TikTok. Adobe believes the platform will accelerate its push into the agentic AI era, where autonomous systems manage everything from content workflows to customer interactions.

Competition across martech remains intense. Many SaaS platforms have struggled this year as AI eats into legacy software budgets. Adobe has felt that pain as well. Its stock is down more than 25% in 2025. Semrush, however, held steady. The pairing may help Adobe regain ground in a market where AI-native platforms expand quickly.

This also is not Adobe’s first major bid to reshape its ecosystem. The company attempted a $20 billion acquisition of Figma in 2022, but regulators blocked the deal in late 2023. Figma went public this July as the IPO market reopened. Regulators will likely take a close look at this Semrush deal as well, though it sits in a less crowded segment.

Both company boards have already approved the acquisition. Semrush founders and major shareholders, representing more than 75% of voting power, agreed to support the sale. Semrush will soon file a proxy statement with the SEC. Shareholders will then vote on the agreement.

If the deal clears regulatory review, Adobe gains a powerful asset in the new race for AI-driven brand visibility. Semrush gains the scale and distribution of one of the world’s most established software giants. And marketers get one more sign that AI search will soon rival traditional search as a discovery channel.

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Interact Marketing is stepping deeper into AI-powered marketing, and the timing couldn’t be more urgent. With Google’s AI Mode and Deep Search reshaping how users find information, traditional SEO is losing ground. Many brands have watched their organic visibility evaporate, and click-through rates tumble by as much as 61 percent. Interact Marketing’s answer is a specialized AI Mode Marketing Program engineered to reclaim that lost traction—and keep brands visible inside the next generation of search.

The agency’s move follows a major industry inflection point. As AI-generated answers replace conventional blue links, search behavior is shifting. Users are relying more on AI summaries, conversational chat responses, and multi-step deep searches that bypass traditional SERPs. For marketers, this means less screen time, fewer impressions, and lower conversions—unless you adapt.

Joe Beccalori, CEO of Interact Marketing, says the program wasn’t an overnight reaction. “GPT sparked a massive shift in how people search, and we began building this program two years ago in response. When Google launched AI Overviews in May 2025, it validated the need,” he explained. Their strategy now centers on the mechanics behind AI responses—citations, relevance scoring, and content structures that large language models prioritize.

Rather than tweaking old SEO frameworks, the agency focused on Google’s AI Mode from the start. This approach includes real-time response testing, performance tracking tuned to generative results, and AI-driven workflows that don’t exist in standard agency packages. Interact Marketing’s team appears to be betting on a simple reality: If AI is becoming the new front page of the internet, then optimizing for it isn’t optional.

The program zeroes in on several areas where brands stand to lose—or gain—the most:

Improved Rankings
AI Mode prioritizes a different set of ranking factors. Interact Marketing uses targeted optimization to help brands secure premium placement inside generated answers.

Enhanced Citations
Credible citations heavily influence whether a brand appears in AI responses. The program ensures accuracy, consistency, and authority across all digital listings.

Increased Conversions
AI responses may reduce page visits, but high-quality content can still drive intent. Interact’s strategies aim to convert those interactions into measurable business outcomes.

What sets the program apart is its early adoption curve. While many agencies are still trying to decode Google’s shifting search ecosystem, Interact Marketing built its framework before AI Overviews even rolled out. As Google continues refining its AI-driven search experience, the agency’s approach centers on continual testing, cross-channel analytics, and rapid adaptation.

The real story here isn’t another SEO product—it’s a preview of the next competitive battlefield. As AI-driven search becomes the default experience, brands that optimize for Deep Search and generative results will shape the visibility landscape. Everyone else risks disappearing from it.

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Automotive dealerships have no shortage of data. What they lack is coherence. Today, Data Axle and Tealium announced a partnership designed to fix that problem by unifying fragmented dealership systems and turning scattered records into actionable intelligence. For an industry long plagued by inconsistent communications and conflicting customer profiles, the timing is ideal.

The collaboration blends Data Axle’s verified consumer and vehicle datasets with Tealium’s real-time customer data platform. Together, the companies aim to help dealers and OEMs merge disjointed customer information, eliminate duplicate records, and guide marketing, sales, and service decisions with far greater accuracy.

The need is clear. A new survey commissioned by Data Axle shows consumers are tired of mixed signals from dealerships. Nearly half of respondents reported receiving duplicate or contradictory messages from the same dealer. Even worse, 68 percent said dealership outreach often feels inconsistent or irrelevant. Yet consumers are willing to share core data—emails, phone numbers, and vehicle history—so long as they receive real value in exchange, such as accurate trade-in estimates or relevant service offers.

According to Chris McTague, managing director of automotive at Data Axle, the issue isn’t a shortage of information. “Dealerships are sitting on a wealth of data that rarely works in concert. Cars have become interchangeable, but data isn’t. What separates one dealer from the next is how intelligently they use their information,” he said. For McTague, the partnership offers a foundation that turns verified insights into better decision-making and more trustworthy customer experiences.

At the core of the integration is Data Axle’s deterministic identity graph, which feeds directly into Tealium’s CDP to clean and enrich customer profiles in real time. Dealers gain a unified view of each customer, allowing them to refine audience models, tailor outreach, and strengthen service engagement without relying on guesswork. It’s a shift from fragmented databases to measurable impact.

Stephen Smith, RVP of Partnerships at Tealium, emphasized that automotive data challenges run deeper than outdated CRMs. “The auto industry faces unique challenges and often navigates fragmented data systems, making it difficult for dealers to fully understand and engage their customers,” he said. By combining Tealium’s orchestration engine with Data Axle’s verified datasets, the partnership brings “clarity, control, and connected data” into the dealer’s workflow.

With pressure mounting for dealerships to modernize how they identify and reach buyers, the partnership arrives at a strategic moment. As digital-first shoppers expect relevant and timely interactions, the dealer that masters its data—not just its inventory—wins. Data Axle and Tealium’s solution offers a path toward more credible, efficient, and intelligent customer engagement.

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Thanks, the customer-first native ad network known for reimagining post-purchase experiences, is expanding into live events through a new partnership with Oztix, Australia’s largest independent ticketing company. The collaboration brings Thanks’ AI-powered media monetisation platform directly into Oztix’s digital ecosystem, turning the high-intent moment after a ticket purchase into a smarter, more curated discovery experience for fans.

Instead of generic ads or irrelevant offers, buyers will now see contextually aligned recommendations—travel options, local dining, merch, and experience upgrades—delivered at the precise moment their excitement peaks. It’s a strategy already trusted by brands like eBay, Linktree, and Booking.com, but this marks its first major expansion into the live-events sector.

The partnership extends beyond commerce. As part of the rollout, Thanks and Oztix will jointly support Make-A-Wish Australia, dedicating a portion of proceeds each quarter to help grant wishes for children with critical illnesses. The initiative brings an added layer of purpose to every ticket sale, allowing fans to contribute to a national cause simply by completing their purchase.

“Oztix is a cornerstone of Australia’s live-event scene, connecting millions of fans to the experiences they love,” said Steve Tesoriero, Founder and Co-CEO of Thanks. He noted that Oztix’s audience-first ethos made the partnership a natural fit. The goal, he added, is to “add more meaning to every ticket purchase” while expanding the same AI-driven platform used by global brands into a new industry.

Oztix has spent more than two decades building partnerships across promoters, venues, artists, and community organisations. Its reputation for strengthening the live-events ecosystem makes the collaboration especially aligned with its mission. The integration with Thanks introduces a new way for fans to both receive value and give back—connecting entertainment with meaningful social impact.

Stuart Field, Co-Founder and Managing Director of Oztix, emphasized the human side of the initiative. “Together, we’re showing how retail media and community can work side by side—creating rewarding experiences for fans while giving back,” he said. For many on the Oztix team, the partnership with Make-A-Wish is personal. “It’s about spreading the same joy and connection we see at live events to children and families who need it most.”

For Thanks, the collaboration marks another milestone in its Australian expansion and reinforces its mission to bring more relevance, humanity, and value to the moments brands often overlook. As retail media continues its shift toward high-intent, first-party environments, the post-purchase audience is becoming one of the most strategically important surfaces in digital marketing. By pairing real-time contextual intelligence with charitable impact, Thanks and Oztix are betting that the future of advertising isn’t just more efficient—it’s more meaningful.

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Live events have never struggled to attract people—they’ve struggled to measure what actually matters. ROME Insights, a new analytics and AI startup, wants to fix that. Today, the company unveiled ROME (Return on Memorable Experiences), a measurement framework built to capture human attention, emotional engagement, and the lasting impact of in-person experiences. For an industry built on connection, it’s a shift that feels long overdue.

Co-founded by event-technology veteran Justin Zebell and marketing strategist and AI researcher Bob Hutchins, ROME Insights was created in response to a long-standing issue: traditional event metrics don’t tell the full story. Attendance numbers, badge scans, and booth visits offer surface-level signals. What they miss is the depth—the moments that spark memory, influence behavior, and justify sponsorship spend.

“We built ROME because the events industry has been measuring the wrong things,” Zebell said. His argument is simple: event organizers, sponsors, and internal stakeholders need proof of value grounded in real human experience, not just headcounts. ROME provides that proof.

The ROME framework blends multiple data sources—behavioral attention tracking, qualitative feedback, and quantitative analytics—to produce a composite engagement score. It captures both immediate reactions and longer-term recall, offering what the company calls a clearer, more defensible metric for understanding event impact.

Hutchins sees ROME as a tool that protects the value of live gatherings in an increasingly digital world. “Live events are one of the few places where people still gather in person to learn, connect, and be moved by ideas,” he said. “ROME helps organizers protect and prove the value of that experience in a world that demands measurable outcomes.”

ROME Insights will work directly with conference organizers, trade show producers, and corporate event teams to integrate the framework across different formats. The company also provides consulting services to help teams interpret findings and use those insights to shape future events—whether that means adjusting programming, reallocating budget, or refining sponsor packages.

Early adopters are already seeing results. Event organizers using ROME have reported higher sponsor renewal rates, smoother budget approvals, and a sharper understanding of which moments drive the most value. For a sector where gut instinct has often outrun measurement, ROME’s data-backed approach could become a competitive advantage.

In an era defined by metrics, the company is betting that the most important event KPIs aren’t clicks or counts—they’re the moments people remember.

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AI has already transformed market research, but according to the new 2026 Market Research Trends report from Qualtrics, the divide between teams using basic AI and those embracing purpose-built capabilities is widening rapidly. The stakes aren’t small: research groups relying only on generic tools are four times more likely to lose influence inside their organizations.

Meanwhile, 72% of teams using synthetic responses, agentic AI, and AI-native workflows say their organizations rely on research far more than they did last year—momentum that’s translating directly into higher budgets. Traditional teams, however, are almost twice as likely to face stagnant or shrinking demand for their work.

“In today’s fast-moving economies, rapid access to consumer insights is a huge advantage,” said Ali Henriques, Executive Director of Edge at Qualtrics. “The teams embracing AI are reimagining what research looks like, asking bigger questions, and moving earlier in the innovation cycle.”

Purpose-Built AI Becomes the New Standard

AI adoption has crossed a maturity threshold. More than half of researchers now use AI regularly, and nearly nine in ten have experimented with it, but the report shows a clear shift away from generic chatbots toward AI embedded directly in research platforms. Purpose-built capabilities grew from 62% to 66% adoption, while usage of general-purpose tools dropped by nearly ten points.

The teams gaining the most traction are those leaning into specialist functions. Conversational analytics and visual content analysis—both at 49% adoption—give researchers deeper qualitative insight at a fraction of the time. What once took weeks can now be processed in hours.

Synthetic data is driving an even more dramatic evolution. Researchers using synthetic datasets are:

• 11% more likely to engage in early-stage innovation

• 7% more likely to run go-to-market studies

• 5% more likely to perform final product testing

Among those who’ve adopted it, 45% now consider synthetic data their most reliable source, surpassing traditional online panels—a remarkable shift for an industry built on human surveys.

Brands like Gabb are already using Qualtrics’ purpose-built synthetic model to reduce fielding costs, accelerate discovery, and test messaging against emerging trends. As Research Director Garred Sheppard described it, “Synthetic data became our cultural radar—cutting timelines from a week to hours while letting us validate high-stakes decisions with human panels.”

AI Agents Push Research Toward End-to-End Automation

Another major shift is the rise of agentic AI. While only 15% of researchers use AI agents today, nearly 80% expect that these tools will handle more than half of research projects end-to-end within the next three years.

Efficiency gains are already visible. Among teams using agentic AI, 84% report significantly higher efficiency, compared with 68% of those who haven’t tried it.

Henriques said the biggest unlock isn’t workload reduction—it’s democratization. Product teams can test ideas without submitting requests. Marketing can evaluate sentiment without waiting on insights teams. Executives can explore new markets directly. “The barrier to insights is no longer specialist knowledge,” she said. “It’s simply asking the right question.”

Leadership vs. Frontline: A Growing Execution Gap

Despite major investments in AI, many organizations aren’t seeing the full return. The report highlights a sharp misalignment between research leaders and individual contributors:

• 39% of leaders say AI has revolutionized their processes vs. 19% of frontline researchers

• Only 5% of leaders fear layoffs due to AI vs. 15% of individual contributors

• 68% of leaders consider themselves synthetic data experts vs. 41% of contributors

• 79% of leaders trust synthetic data quality vs. 61% of contributors

This mismatch results in underused tools, wasted budget, and slower execution—while competitors with tighter alignment surge ahead.

“When frontline teams don’t buy in, expensive AI tools go unused,” Henriques warned. “Organizations need shared definitions of success, real hands-on training, and clarity across levels about the practical applications of new AI capabilities.”

A Global Snapshot of a Rapidly Changing Industry

The findings come from a global Qualtrics study conducted in Q3 2025 with more than 3,000 research professionals across 14 countries. The data reveals a sector in transition—from manual workflows and traditional surveys to hybrid human-synthetic models, autonomous research agents, and a new definition of what strategic research teams look like.

The message from Qualtrics is clear: the teams that invest in purpose-built AI now will set the pace for the next decade of research. The ones that don’t risk being left behind.

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