The advertising industry has developed a complicated relationship with artificial intelligence: eager to use it, reluctant to trust it. New research from TripleLift, an advertising technology company, finds that while 60 percent of advertising professionals say their organizations have a centralized AI strategy, fewer than 30 percent express high confidence in it.

The findings, drawn from a survey of 200 global advertising professionals, expose what TripleLift calls an industry-wide standoff — organizations that recognize AI’s potential to improve campaign efficiency but cannot bring themselves to relinquish control over creative quality, brand safety and execution decisions.

The divide is sharpest when broken down by function. AI has become standard practice for data-heavy, repeatable tasks: 73 percent of respondents use it for campaign optimization, including bidding and audience adjustments, and 59 percent use it for audience targeting and segmentation. Creative work is a different matter. Only 25 percent use AI for creative production at all, and most of those limit its role to testing and iteration rather than generating finished assets.

Fully autonomous campaigns remain rare. Only 19 percent of respondents report using AI to completely automate campaign execution, while 40 percent say they still maintain manual control over the entire process.

Part of the problem is what the report calls the “review tax.” Whatever time AI saves in execution, much of it is clawed back by the human oversight it requires. Nearly three-quarters of respondents say they spend several hours a week validating AI-generated work — checking for technical errors, brand safety risks and inconsistent creative quality. Automation, in other words, has not reduced the workload as much as it has relocated it.

Also Read: The Organizations That Survive Disruption Never Had to Recover From It

“When AI systems work in sync across media, measurement and audience, the opportunity is clear: faster, always-on campaign execution,” said Rob Deichert, TripleLift’s chief operating officer. “The missing piece is creative. Bridging that gap — without losing the human spark — is what will define the next era of advertising.”

The report’s broader conclusion is that the industry’s AI hesitancy is not irrational. It reflects a genuine tension between the efficiency gains AI can deliver and the brand risks that come with reducing human judgment in creative and editorial decisions. Until that trust gap narrows, the gap between AI ambition and AI execution is likely to persist.

The post Ad Industry Wants AI to Work. It Just Doesn’t Trust It. appeared first on MartechView.

ComplyAdvantage, a financial crime compliance software company, has launched an upgraded payment screening product built on its AI-native Mesh platform, promising financial institutions faster sanctions checks and fewer false positives across a broader range of payment types.

The move completes the migration of the company’s existing payment screening capability into Mesh, a unified compliance platform it introduced last year. The integration means that payment screening now sits alongside transaction monitoring and customer risk scoring in a single system, giving compliance analysts a complete view of a customer’s history when reviewing a flagged transaction — rather than having to switch between separate tools.

The practical problem the product is designed to solve is a familiar tension in financial services: instant payment networks require near-real-time decisions, but sanctions lists change constantly and the consequences of missing a hit are severe. ComplyAdvantage says its system screens transactions across bank, card, remittance and crypto rails at low latency, with sanctions data updated in minutes rather than hours because the company controls its data pipeline from source to alert.

Two features are positioned as the most operationally significant. The first is a no-code interface that allows compliance teams to adjust match sensitivity and manage internal watchlists without involving engineering staff — a meaningful change for institutions that currently queue regulatory updates behind development backlogs. The second is what the company calls intelligent muting, a system that identifies and suppresses repeated false positives so analysts can concentrate on genuine threats rather than relitigating the same erroneous matches.

Also Read: E-commerce Doesn’t Have a Data Problem. It Has a Speed One.

Alert fatigue is a well-documented problem in financial crime compliance. Analysts at large institutions can review thousands of flagged transactions daily, the vast majority of which turn out to be legitimate. Systems that reduce that noise without increasing the risk of missing real sanctions violations are in strong demand, particularly as regulators at bodies including the U.S. Office of Foreign Assets Control, the United Kingdom’s HM Treasury and Singapore’s Monetary Authority have tightened enforcement expectations.

“By upgrading our payment screening capability to reside natively within this environment, we are giving our customers a strategic advantage,” said Mark Watson, ComplyAdvantage’s chief product and technology officer. “They can now intercept risk at industry-leading speeds with a system that reduces the operational friction and alert fatigue that traditionally hinder business growth.”

ComplyAdvantage says more than 3,000 enterprises across 75 countries use its platform.

The post ComplyAdvantage Speeds Up Payment Screening for Banks appeared first on MartechView.

SurveyMonkey has extended its sponsorship partnership with Williams Formula 1 driver Alex Albon through the remainder of the 2026 season, the companies announced Thursday, building on a first year that generated results the partnership’s organizers describe as well above expectations.

The renewal keeps SurveyMonkey branding on Albon’s helmet at select race weekends and continues fan engagement activations across the remaining Grands Prix. Under the arrangement, Albon uses SurveyMonkey’s platform to poll his global fanbase — more than 6.6 million social media followers — on content ideas, merchandise preferences, and race-weekend experiences, feeding responses back into decisions about how he engages with his audience.

The 2025 campaign, which produced three video activations built around fan interaction, generated 61.6 million total impressions and 3.6 million engagements, for an engagement rate of 5.86 percent — a figure the partners say significantly exceeded their benchmarks.

“SurveyMonkey gave us the tools to really listen — to understand what our fans want, what they care about, and how to build something meaningful together,” Albon said. “Continuing that into 2026 feels like a natural next step.”

Also Read: Your CX Partner Is a Revenue Engine. Treat It Like One.

Eric Johnson, chief executive of SurveyMonkey, said the first season validated the company’s core proposition in an unusually public arena. “We gave fans a voice and turned their feedback into insights that shaped content and engagement throughout the season,” he said, adding that Albon’s genuine rapport with supporters was central to the campaign’s success.

The partnership is managed through FanCapital, a sports marketing firm that brokers sponsorships for athletes and leagues. SurveyMonkey sits alongside Monsoon Valley, Beats by Dre, Domo, and Bell Helmets in Albon’s personal sponsorship portfolio.

The post SurveyMonkey Stays in the Fast Lane With Albon appeared first on MartechView.

Amplified, a research firm specializing in human attention measurement, has unveiled AttentionAI, a product designed to forecast how audiences will engage with advertising creative before media budgets are committed.

The tool represents a meaningful shift in how attention is used in media planning — moving it from a retrospective metric into a forward-looking signal. Built on more than a decade of academic research and 50 billion real human attention data points, AttentionAI enables brands and agencies to assess which creative assets are most likely to capture and hold audience focus across digital platforms.

The underlying methodology distinguishes it from conventional tools that lean on proxy measures such as viewability. AttentionAI draws on live, in-environment data from over two million people across 21 markets. Each creative asset is evaluated against a modeled audience of 20,000, generating second-by-second attention decay curves calibrated against observed human behavior. The model is continuously validated as creative formats and platform trends evolve.

Alongside the product launch, Amplified introduced API and MCP integration, allowing predictive attention data to flow directly into the planning platforms, media mix models and reporting tools that agencies and brands already use. Connect, a South African media agency and Amplified’s first API integration partner, is already processing 500 creative tests per month through the system.

Also Read: Contextual Advertising: What It Is and Why It Matters

Industry figures suggest that up to 75 percent of digital advertising fails to earn active audience attention. Early pilots with WPP, Procter & Gamble and easyJet have tested the product’s ability to improve both creative selection and campaign outcomes.

“AttentionAI is the next major leap — turning that science into a predictive intelligence layer that can be applied across almost every corner of the media ecosystem,” said Dr. Karen Nelson-Field, founder of Amplified.

Kyle Nimmo, head of media investment at easyJet, said the tool gave his team a more practical way to compare assets and make decisions before campaigns go live. He noted that analysis revealed the format and platform context — not just creative quality — could significantly affect how much attention an ad earns.

The post Amplified Launches AI to Predict Ad Engagement appeared first on MartechView.

DoubleVerify, the ad verification software company listed on the New York Stock Exchange, has launched post-bid measurement across the LinkedIn Audience Network, giving advertisers independent data on how and where their ads are delivered across LinkedIn’s extended publisher network.

The new capabilities cover four key signals: invalid traffic detection, viewability, brand suitability and geographic accuracy. Together, they provide a layer of third-party scrutiny that has been standard practice in broader digital advertising but was previously unavailable across LinkedIn’s off-platform inventory.

In addition to post-bid reporting, DoubleVerify offers pre-bid avoidance controls through its Authentic Brand Suitability product, allowing brands to screen and exclude inventory before a campaign runs. The combination gives advertisers oversight across the full campaign lifecycle — blocking unsuitable placements upfront and auditing delivery after the fact.

The LinkedIn Audience Network extends campaigns beyond LinkedIn’s own feed to a broader set of publishers, and LinkedIn’s internal data suggests campaigns using the network generate 3.9 times more monthly impressions and 66 percent higher conversion rates than feed-only campaigns.

“Advertisers are demanding greater clarity into the quality and performance of their media environments,” said Steve Woolway, DoubleVerify’s executive vice president of business development. “This brings the independent verification advertisers need to make more informed investment decisions across this important channel.”

Also Read: The Organizations That Survive Disruption Never Had to Recover From It

Lee Womer, LinkedIn’s vice president of product and business development, said transparency was central to how the company was evolving its audience network, adding that the partnership would help advertisers better understand media quality and drive stronger results.

The announcement extends a broader integration between the two companies. DoubleVerify recently added measurement support for LinkedIn’s CTV ad formats, covering invalid traffic, viewability, brand suitability and geography across streaming devices.

The post DoubleVerify Brings Ad Quality Checks to LinkedIn appeared first on MartechView.

Fuse Media, a Latino-owned media company, has named iSpot as its official measurement provider for connected TV and free ad-supported streaming television inventory, the companies announced. The partnership is designed to give advertisers verifiable proof that campaigns running across Fuse Media’s portfolio are reaching audiences and driving real-world results.

The deal enables Fuse Media to link ad exposure directly to consumer actions — including website visits, app installs, foot traffic and in-store purchases — across channels, including Billboard Español TV, LOL! Network, Fluffy TV, El Rey Rebel and Complex TV. The company reports 84 million monthly unique viewers across CTV, linear and FAST environments, with its programming reaching more than 85 percent of multicultural households in the United States.

Through iSpot’s Unified Measurement and Outcomes at Scale tools, Fuse Media will now be able to offer advertisers deduplicated cross-platform audience data in a single view, incremental reach metrics beyond linear television, closed-loop attribution and real-time in-flight optimization.

“Helping brands not only reach these audiences — who are often tech-savvy, early adopters — but convert ad messages into consumer action is a critical opportunity,” said Yasmin Mitchell, Fuse Media’s head of business intelligence and insights.

Also Read: Your PR Strategy Was Built for a Newsroom That No Longer Exists

Stuart Schwartzapfel, executive vice president of media partnerships at iSpot, called the current video landscape “the most complex and splintered in media history” and said the partnership would allow Fuse Media to demonstrate how effectively its inventory reaches and converts multicultural audiences.

The post Fuse Media Bets on iSpot to Prove Its Ad Value appeared first on MartechView.

KERV.ai, the leader in video analysis, performance, and monetization announced a strategic partnership with Canela Media, a leading technology and innovation-driven multicultural media company, to bring additional interactive advertising capabilities to Canela Media’s ecosystem.

This collaboration marks KERV.ai’s first partnership with a Spanish-language media company, further expanding its global footprint across a growing roster of premium publishers, OEMs, and platforms. As Canela Media continues to scale its advertising offerings, the company is focused on introducing innovative ad solutions that create more engaging experiences for both audiences and advertisers alike.

Through this partnership, Canela Media will integrate KERV.ai’s interactive video and shoppable creative technology across its ecosystem, enabling more immersive, commerce-driven ad experiences that unlock new opportunities for audience engagement, campaign performance, and monetization.

Also Read: E-commerce Doesn’t Have a Data Problem. It Has a Speed One.

“We’re excited to partner with Canela Media as our first Spanish-language media company,” said Marika Roque, Chief Strategy Officer at KERV.ai. “As Hispanic audiences continue to lead the nation in streaming adoption — with streaming accounting for 55.8% of total TV time versus 46% for the general population, according to Nielsen — this partnership creates new opportunities for brands to engage viewers through more relevant, interactive and shoppable ad experiences.”

“This partnership reflects our continued focus on innovation and creating engaging experiences for our audiences,” said Chechu Lasheras, Canela Media’s Chief Strategy Officer. “By integrating KERV.ai’s technology into our ecosystem, we’re able to introduce additional interactive capabilities that feel seamless for viewers while creating new opportunities for advertisers to connect with consumers.”

The post KERV.ai Expands Into Spanish-Language Streaming appeared first on MartechView.

Quiq, the enterprise agentic AI platform used by more than 150 global brands, has launched Voice AI and advanced its platform to support organizations moving from isolated AI experiments to full-scale deployments that must operate reliably across every customer touchpoint. The company has also introduced a new brand identity it says reflects this next phase of customer experience infrastructure.

From Pilots to Production

The challenge facing customer experience teams has shifted. A year ago, the question was whether AI could handle customer interactions at all. Today, the question is whether it can do so consistently, transparently, and at enterprise scale — across channels, brands, languages, and compliance requirements simultaneously.

Most customer experience systems still fall short of that bar. Context is lost as customers move between chat, SMS, and voice. AI decision-making is difficult to audit or explain. Human agents stepping in to handle escalations frequently lack the interaction history needed to resolve issues without asking customers to repeat themselves.

Quiq’s platform addresses all of these problems within a single coordinated system. Voice, messaging, and human agents operate together, sharing context throughout each interaction. AI and human workflows follow the same configurable guardrails, ensuring that every touchpoint — regardless of channel — remains consistent with brand standards, regulatory requirements, and operational oversight expectations.

Also Read: E-commerce Doesn’t Have a Data Problem. It Has a Speed One.

Voice AI: Completing the Channel Picture

The launch of Voice AI extends Quiq’s platform into real-time phone conversations, completing a channel coverage that previously encompassed text-based interactions only. Customers can now move between voice and messaging without losing conversational context. When escalation to a human agent is required, the complete interaction history transfers with the call — eliminating the most common source of customer frustration in enterprise support environments.

Every voice interaction operates under the same governance framework as Quiq’s digital channels, with identical configurable guardrails, transparency mechanisms, and brand alignment controls.

The practical scope of what the platform can manage is considerable. In one current deployment, a global retail organization runs a single AI agent supporting four brands, seven countries, and four communication channels simultaneously — adapting in real time to each brand’s voice, each market’s language, and each customer’s individual history.

The Rebrand

Alongside the platform expansion, Quiq is introducing a new brand identity. The company is explicit that this is not a cosmetic refresh. The rebrand is intended to articulate what has become the platform’s defining proposition: a continuous, connected customer experience supported by transparent AI decisioning and architecture built to apply a company’s specific workflows, standards, and brand voice at every point of interaction.

“Customers expect interactions to feel simple, but delivering that in real-world operations is incredibly complex,” said Jen Grant, Chief Marketing Officer at Quiq. “The real challenge is getting AI to work through the entire customer experience in a way that is reliable, understandable, and under control — which is where the market is heading.”

Also Read: Your CX Partner Is a Revenue Engine. Treat It Like One.

The Underlying Argument

The most effective AI platforms, in Quiq’s framing, do not eliminate enterprise complexity. They absorb it — translating workflows, business rules, and brand standards into systems that are simultaneously highly customizable and consistently executable. The complexity lives in the platform. The simplicity is what the customer experiences.

Quiq says it will continue investing in deeper visibility into AI performance, improved coordination between AI and human agents, and more reliable execution across full customer journeys. As enterprise AI moves from demonstration to dependability, the platform is positioning itself as infrastructure for organizations that can no longer afford the gap between the two.

The post Quiq Launches Voice AI to Unify Enterprise Customer Experience appeared first on MartechView.

SurveyMonkey, the survey and forms platform used by more than 60 million people worldwide, has launched a connector for Claude that lets users create, send, and analyze surveys without leaving the AI interface — turning what would otherwise be a multi-step workflow into a single conversation.

The integration, powered by Anthropic’s Model Context Protocol, is available now through the Claude connector directory. It represents a direct response to how knowledge workers are increasingly operating: inside AI tools, not alongside them.

What the Connector Does

The SurveyMonkey connector for Claude enables four core functions within a single chat interface.

Users can create, edit, and send surveys using natural language — describing what they want to learn and letting the system generate the appropriate questions in seconds. Survey data can be accessed and analyzed in real time, with trends and patterns surfaced directly in the conversation without requiring data exports or platform switching. Feedback from SurveyMonkey can also be combined with other data sources available within Claude, enabling richer, cross-referenced analysis. The entire process — from survey creation through to insight generation — can be managed without breaking the flow of work.

The underlying premise is straightforward. AI tools are most valuable when they operate on real data from real people. SurveyMonkey’s connector is designed to put that data — customer feedback, employee sentiment, market research responses — directly into the environment where decisions are increasingly being made.

Also Read: Peak Martech? The Landscape Has Plateaued, but the Real Story Lies Beneath

The Broader Context

The launch reflects a structural shift in how enterprise software is being designed and consumed. The model of navigating between specialized tools for each step of a workflow — build in one platform, analyze in another, act in a third — is giving way to AI interfaces that orchestrate multiple capabilities from a single point of interaction.

“Instead of jumping between tools, people expect workflows to happen where they are,” said Eric Johnson, Chief Executive of SurveyMonkey. “With our Claude connector, we’re bringing real survey feedback into the tools where people already work.”

The emphasis on real human feedback is deliberate. As AI-generated content proliferates across business workflows, the signal value of genuine customer and employee responses becomes a more significant differentiator — not less. SurveyMonkey’s integration positions human data as a first-class input to AI-powered decision-making rather than an afterthought.

The post SurveyMonkey Launches Claude Connector for Live Feedback appeared first on MartechView.

For more than a decade, the enterprise world chased hyper-automation. Boards, consultants, and venture capitalists all shared the same dream: big productivity gains delivered by clever workflow tools, low-code connectors, and robotic process automation (RPA). Top-tier valuations, a parade of IPOs, and billion-dollar rounds crowned automation as the next trillion-dollar frontier.

But today that narrative is collapsing — not because automation isn’t useful, but because the architecture underpinning it is fundamentally obsolete. What the market once celebrated as “hyper-automation” is now being written off as incremental plumbing — not strategic leverage.

Public markets and valuations aren’t bluffing — they are signaling a tectonic shift. 

Public Market Reality: When Automation Valuations Drop Hard

One of the poster children of hyper-automation was UiPath. Valued at over $35 billion in late-stage private funding, UiPath’s public market debut was among the largest software IPOs of the 2020 cohort. But today, the stock trades 70% below its peak, reflecting a stark reset in what public markets are willing to pay for legacy automation that fails to expand margins or deliver sustained enterprise outcomes. 

This sell-off isn’t isolated — it’s a shift in valuation narrative. Capital now demands results, not engineering complexity. Ask yourself: if automation fundamentally transformed productivity across every business unit, why haven’t legacy automation stocks maintained their valuations in an era obsessed with AI growth?

The answer is simple: legacy automation still operates like software from the 1980s — static, linear, brittle workflow logic under the hood — while the world around it has become exponentially more dynamic.

Also Read: E-commerce Doesn’t Have a Data Problem. It Has a Speed One.

The Funding Frenzy That Built Fragile Architecture

While UiPath faced valuation compression, private markets saw a secondary boom in no-code and security automation startups — each promising to elevate hyper-automation through low-code workflows and drag-and-drop playbooks.

Consider Tines, a Dublin-based no-code automation platform focused on security workflows, with hundreds of millions in funding across multiple rounds. 

Meanwhile, Torq — another “hyper-automation” platform — raised a massive $140 million in its Series D at a $1.2 billion valuation in 2026, bringing its total raised to $332 million. 

These companies embody exactly the market the last decade valued: drag-and-drop workflows that connect tools, handle alert triage, and automate repeatable tasks. Yet the core architecture for these systems remains deterministic-first — defined by prewritten steps assembled into stories or playbooks that attempt to anticipate every possible state of the world. That’s fine for checklists — less so for real business outcomes. 

Why This Architecture Is Headed Toward Zero

Here’s the uncomfortable truth:

Hyper-automation didn’t redefine how work actually gets done. It repackaged deterministic workflows with prettier UIs and AI buzzwords. The entire paradigm assumes:

• Enterprise processes are static enough to be defined in workflows.
• Users can anticipate every possible branch in a decision tree.
• You can write your way around complex human-machine interaction.

But the 2020s enterprise is not static. The business landscape, cloud ecosystems, security threats, and digital environments change by the minute. Legacy architecture cannot adapt because it starts with rules rather than objectives.

When markets recognize that a product’s DNA can’t survive the world it claims to automate, they repriced those assets accordingly. That’s exactly what’s happening with companies built on the old stack. 

Public Sentiment Is Shifting — Investors Want Outcomes, Not Workflows

The public markets — and savvy late-stage investors — are increasingly separating automation as a product from real enterprise leverage. The headlines today aren’t about bots mimicking clicks anymore — they’re about agents that act autonomously on business objectives.

Investors are no longer content to pour capital into incremental workflow stitching. Capital is chasing systems that can:

• Interpret intent, not just follow static rules
• Plan and execute across systems, not just trigger steps
• Adapt to uncertainty, not crash when conditions change

The evidence is clear: when the underlying architecture is rigid, valuations get compressed. Investors won’t buy another round of “better connectors” if the fundamental utility is low-margin and rigid. 

Also Read: Contextual Advertising: What It Is and Why It Matters

Agentic Automation Is What Comes After Hyper-Automation

The next wave isn’t a prettier drag-and-drop canvas. And it definitely isn’t another workflow builder packaged as the next big thing.

It’s agentic automation — systems built for objectives, not sequences; for dynamic coordination across tools, not fixed playbooks. An agentic system understands intent, negotiates tasks across APIs, adapts to context, and achieves outcomes without human-modeled paths for every permutation of work.

We’ve moved beyond single-thread dragons to intelligent meshes of agents that collaborate to solve ambiguous problems. This isn’t a minor upgrade — it’s an architectural rewrite of how work gets done. 

Legacy Automation Architecture Is Becoming the New Mainframe

Here’s the real market truth:

Legacy process automation, even with AI badges slapped on workflows, is rapidly approaching the same fate as outdated computing paradigms — valuable in a historical context, obsolete in a strategic context.

Public markets already charge legacy automation names discount multiples compared to true AI-driven growth platforms. Venture capital is tightening its focus on workflow stitchers while expanding its backing of agentic computation pioneers. And boards are starting to ask deeper questions about outcome economics instead of demo bells and whistles.

If legacy automation is mainframe-era tools rebranded for the cloud, then agentic systems are the next computational substrate for business execution.

That’s the disruption that will wipe out the old market cap — and unlock orders-of-magnitude value for enterprises that embrace the new paradigm.

The automation story isn’t over — it’s rewritten.

The post Hyper-Automation Is Over. Agentic AI Is What Comes Next. appeared first on MartechView.