Contextual advertising is not a new coinage in search of a use case. It is a response to a structural shift in how digital advertising works — and, increasingly, how it is permitted to work.

At its core, contextual advertising is the practice of placing ads based on the content of the page a user is currently viewing, rather than on a profile built from their browsing history. It does not require cookies, does not rely on third-party data, and does not track a user across the internet to build a behavioral profile. Instead, it attempts to reach the right person at the right moment by understanding the context in which they are already engaged — and placing a relevant message there.

Contextual vs. Behavioral Advertising

The distinction between contextual and behavioral advertising is worth understanding precisely, because the two are frequently conflated.

Behavioral advertising serves ads based on what a user has already done — their search history, the pages they have visited, and the purchases they have made. It is retrospective, drawing on accumulated data to infer likely future interest. Contextual advertising works differently. It does not wait for a user to display identifiable behavior. It attempts to anticipate relevance before that behavior occurs, matching the message to the moment rather than to the person’s recorded past.

A user reading a review of running shoes is, in that moment, a more receptive audience for athletic gear than any browsing history alone could confirm. Contextual advertising acts on that signal directly — without a cookie, without a data broker, and without the user having searched for anything at all.

Why It Is Gaining Ground

Today’s consumers are more sophisticated about advertising than previous generations. Continuous exposure to marketing across multiple platforms has produced a kind of selective attention — most people have developed an instinct for filtering out messages that do not feel immediately relevant. Contextual advertising is, in part, a response to that dynamic. By placing messages where they are genuinely pertinent to what a user is already thinking about, it improves the odds of cutting through.

It is also gaining ground for structural reasons. The deprecation of third-party cookies — a process that has been uneven but directionally consistent — has eroded the data infrastructure on which behavioral advertising depends. Privacy regulations in Europe, the United States, and a growing number of other markets have raised the compliance costs of tracking-based approaches. Contextual advertising sidesteps most of those constraints by design, making it an increasingly attractive option for publishers and advertisers navigating a more restrictive data environment.

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How It Works

Contextual advertising uses machine learning to analyze the content of a webpage — keywords, page type, topic category, and media format — and identifies the most relevant advertising placement without referencing user data. The system reads the page, not the person.

For publishers using platforms such as Google AdSense, contextual targeting is built in. Google’s system analyzes the content of each page in its display network and attempts to match the ad to the most relevant available content. For those using Google Ad Manager, ensuring that foundational targeting values are correctly configured is the essential first step.

Getting Started: A Practical Checklist

For advertisers and campaign managers approaching contextual targeting, preparation is less technical than strategic—and begins with a thorough understanding of the product being advertised and the content environments where it is most likely to resonate.

The following steps provide a working framework.

Build a robust keyword repository. Select target keywords, key topics, and commonly used phrases with care. These will determine which pages your ads appear on and, by extension, which moments of user attention you are buying.

Configure reach settings deliberately. Display network settings can be set to a broad or specific reach. Broad reach places ads based on topic targeting; specific reach restricts placement to pages that match both specified keywords and at least one targeted topic. The right choice depends on campaign objectives and the degree of contextual precision required.

Verify the ad order. Before a campaign goes live, confirm that placements have been identified that contextually match the content of the intended web pages. This step closes the loop between targeting intent and actual placement.

Monitor and refine continuously. Contextual advertising is not a set-and-forget discipline. The culture, language, and content landscape in which ads appear shift over time, and targeting parameters should be reviewed and updated accordingly.

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

The Broader Opportunity

For advertisers, contextual advertising represents a path toward relevance that does not depend on surveillance. It is a model that aligns with where privacy regulation is heading, with what consumers say they prefer, and with what the data suggests actually works — messages placed in context perform better than messages placed by default.

Personalization has long been the stated goal of digital advertising. Contextual advertising offers a version of it that does not require knowing who someone is — only what they are paying attention to right now.

In an attention economy, that distinction turns out to matter quite a lot. 

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Typeform, the AI engagement platform used by more than 150,000 businesses, has launched Growth Flow — a customer lifecycle solution that converts every form submission into the starting point of an automated workflow, connecting lead capture, data enrichment, nurturing, and conversion without requiring a separate tool for each step.

The launch targets a structural inefficiency familiar to most growing businesses: the gap between the moment a customer signals interest and the moment a business responds. That gap, typically the product of fragmented tools and manual handoffs, is where leads go cold and revenue opportunities quietly disappear.

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What Growth Flow Does

Growth Flow is built on a straightforward premise. A form submission is not the end of a customer interaction — it is the beginning of one. The platform uses AI to interpret each response in context and automatically trigger the appropriate next action, without delay or data loss between systems.

In practice, that means a prospect can book a meeting, sign an agreement, or complete a payment directly within the form, at the moment of peak intent, without being routed to a separate platform. Sales teams receive instant Slack alerts. Meetings are booked via Google Calendar. Multi-step nurture campaigns launch automatically across email, SMS, and integrated tools, including Klaviyo, from a single submission.

Every response also generates a dynamic customer profile, automatically enriched with third-party data on company size, industry, job title, and demographic signals. The enrichment layer operates within a security-first architecture aligned to GDPR and CCPA requirements, reaching match rates of up to 92 percent for business-to-business contacts and 71 percent for business-to-consumer. Teams gain immediate segmentation and routing context without manual research.

Typeform says its conversational form design already helps businesses collect up to 3.5 times more data than conventional form tools. Growth Flow extends that data advantage into automated action, surfacing real-time conversion signals and audience patterns that allow teams to adapt workflows as customer behavior evolves.

Also Read: Three Myths That Are Keeping Brands Away From AI

The Broader Shift

“For decades, forms have been treated as the end of a conversation — a place where customer intent gets captured and then handed off, disconnected from everything that happens next,” said Aleks Bass, Chief Product and Technology Officer at Typeform. “What used to take ten tools and a lot of manual effort now takes one platform and a single prompt. Typeform is no longer just the form, but the engine that turns responses into revenue.”

The launch reflects a broader consolidation trend in marketing technology, as businesses under resource pressure look to reduce the number of point solutions required to manage the customer journey. Growth Flow’s pitch is that the form — historically a data collection endpoint — can serve as the operational hub for the entire early customer lifecycle, provided the automation and enrichment layers behind it are sufficiently capable.

For small and growing teams running lead generation, sales capture, or customer onboarding, the commercial argument is direct: fewer tools, less manual coordination, and a shorter path from customer intent to customer action.

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Nectar has launched a new partnership with Merlin Entertainments, giving the loyalty scheme’s members a way to redeem points at more than 20 attractions across the United Kingdom — including Alton Towers Resort, Thorpe Park, LEGOLAND Windsor Resort, Chessington World of Adventures, the London Eye, SEA LIFE aquariums, Warwick Castle, and Cadbury World.

The partnership, live from Wednesday, is available through the Nectar app and includes a points-doubling offer: members who swap £5 worth of Nectar points receive £10 off Merlin ticket prices — effectively doubling the redemption value for those who choose to use it.

How to Redeem

The redemption process has been designed to minimize friction. Members open the Nectar app, select the Merlin partner offer, book through the dedicated Merlin-Nectar booking page, choose how many points to apply at checkout, and receive tickets by email. No separate account registration or third-party redirect is required.

“Making it easy for customers to spend their points is really important,” said Amir Rasekh, Managing Director of Nectar360. “By stripping out unnecessary steps, we’ve created a faster, more intuitive redemption experience — one that’s much better for our Nectar members.”

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

The Commercial Logic

For Nectar, the partnership extends the utility of its loyalty currency beyond grocery spending — historically the scheme’s primary redemption context — into leisure and family experiences. For Merlin, it opens access to Nectar’s membership base as a new acquisition and demand-generation channel at a time when the company is investing in new attraction launches.

“We know our Nectar members want great value and real rewards,” said Mark Given, Sainsbury’s Chief Technology, Marketing and Data Officer. “It’s all about giving families fun days out for less and making every point count towards something special.”

Stan Swinton, Merlin’s Chief Growth Officer, pointed to the launch timing as particularly relevant, given that new attractions have recently opened at several sites. “With Bluey the Ride: Here Come the Grannies at Alton Towers and World of PAW Patrol at Chessington World of Adventures, now is an ideal time for Nectar customers to enjoy the benefits of Merlin attractions,” he said. The full list of participating Merlin attractions is available through the Nectar app.

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Awin Global, one of the world’s largest affiliate marketing networks, has cut its content localization turnaround time by 57 percent — reducing a four-week process to under 12 days — by consolidating a fragmented internal operation into a single, AI-powered workflow built on technology from Acclaro and Lokalise.

The results mark a significant operational shift for a company operating across 17 countries on four continents, producing 1.5 million source words and approximately 12 million localized words annually across eight languages.

The Problem: Six Teams, No Single Process

Awin’s localization operation had grown organically across product, user experience, marketing, and engineering teams — each running its own processes, tools, and review cycles. The fragmentation introduced inconsistencies in brand voice and terminology, slowed product and marketing releases, and made it difficult to track where a given piece of content was in the localization pipeline at any given time.

The cost was measurable. Turnaround times stretched to four weeks. Backlogs accumulated. And the internal review burden consumed the equivalent of three to five full-time employees who could otherwise have been directed toward higher-value work.

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The Solution: One Workflow, Shared Infrastructure

Working with Acclaro, a technology-driven language services provider, and Lokalise, an AI language platform, Awin replaced its six disparate localization streams with a single, centralized workflow that combines AI-powered translation, automated orchestration, and expert human post-editing.

Shared linguistic assets — glossaries, translation memories, and style guides — now ensure consistent terminology and brand voice across all eight languages, reducing redundant work and eliminating the variance that had crept into content produced by isolated teams. Smart automation integrates localization tasks directly into existing development processes, reducing internal review requirements by up to 80 percent.

The human element has been preserved rather than eliminated. Trained linguists provide domain expertise and post-editing at critical points in the workflow, maintaining the quality and contextual accuracy that pure machine translation cannot reliably deliver at scale.

“By consolidating tools and workflows into a standardized, technology-enabled process, we have been able to reduce localization turnaround times by more than half while clearing our existing localization backlog,” said Rosario Messina, Senior Product Operations Manager at Awin Global. “We are now enabling faster market releases without any budget increase.”

Also Read: Is Intercom’s AI CX Score the End of CSAT?

The Broader Argument

The Awin case illustrates a pattern increasingly visible across global businesses: localization infrastructure that develops organically tends to fragment at scale, and the cost of that fragmentation — in time, consistency, and internal resource allocation — compounds quietly until it becomes a strategic constraint.

“Disparate localization processes become bottlenecks at scale,” said Devin Lynch, Chief Growth Officer at Acclaro. “By unifying workflows, leveraging automation, and embedding a central language platform, we helped Awin unlock faster releases with greater consistency — without increasing spend. Awin now has scalable localization infrastructure to accelerate expansion, product iteration, and competitive differentiation with high-quality multilingual content that resonates locally and performs globally.”

For a network of one million partners spanning influencers, technology companies, and global brands, the ability to move faster in local markets — without sacrificing the consistency that holds a global brand together — is not an operational nicety. It is a competitive requirement.

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Avaya has announced that avatarin, a Tokyo-based robotics and AI startup spun off from Japanese airline holding company ANA Holdings, has selected Avaya Infinity as the platform underpinning its customer experience operations — a deployment that connects AI-powered social robots, phone agents, and chat support into a single, unified intelligence layer.

The partnership is among the more concrete early examples of what the industry is beginning to call physical AI: artificial intelligence that operates not within a screen or speaker, but through robots moving through physical spaces — airline reservation desks, local government service counters, retail floors — engaging customers in person while remaining connected to the same real-time data infrastructure that serves digital channels.

The Problem Being Solved

The central challenge avatarin faced was one of fragmentation. A customer who began an interaction with a phone agent, continued via chat, and then engaged with an on-site robot would, in a conventional system, effectively be starting over at each handoff — their history, context, and unresolved needs lost in the transition between channels.

Avaya Infinity, integrated with Model Context Protocol, addresses this by preserving and passing the full context of each customer interaction across every channel in real time. Whether the next touchpoint is a human agent, a chat interface, or a social robot navigating a physical environment, the conversation history travels with the customer.

“Our objective is to create ‘One Intelligence,’ where AI, robotics, and contact centers function as a single unit,” said Akira Fukubari, chief executive of avatarin. “We place a strong emphasis on AI that enhances human capabilities rather than replacing them. While AI handles scalability and responsiveness, human experts continue to provide the empathy, sophisticated decision-making, and complex problem-solving that customers demand.”

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

New Avaya Infinity Capabilities

The avatarin deployment is built on three new capabilities within the Avaya Infinity platform.

The first, Tandem Care, pairs human agents with agentic AI for self-service and automation, using Model Context Protocol to connect to enterprise systems, including billing and customer relationship management platforms for secure, real-time context and task execution. The underlying data intelligence layer is powered by Databricks, providing a central control layer for AI governance and compliance.

The second, Out-of-the-Box Real-Time Insights, combines customer interaction data with enterprise business data from systems such as CRM and ERP to give customer experience managers immediate visibility not just into what happened during an interaction, but why, and what action is recommended in response.

The third, Delta Sharing, uses an open standard for zero-copy data access to feed real-time interaction insights directly from the contact center into the broader enterprise data environment, eliminating the complex extract, transform, and load processes that have historically created operational blind spots for enterprise AI initiatives.

The Physical AI Context

The avatarin partnership arrives amid accelerating physical AI adoption. According to Deloitte’s 2026 State of AI in the Enterprise report, global adoption of physical AI — robotics and autonomous systems operating in real-world environments — is projected to reach 80 percent by 2028. The Asia-Pacific region is currently leading the adoption, with 71 percent of organizations already implementing these technologies, compared with 56 percent in both the Americas and EMEA.

Seventy-four percent of organizations surveyed by Deloitte expect to deploy agentic AI within the next two years, with AI agents increasingly described as force multipliers that allow human workers to shift into more strategic roles — the precise dynamic that avatarin’s hybrid model is designed to operationalize.

“Avaya Infinity was born in the AI age and is specifically designed to meet the broad and diverse needs of companies like avatarin,” said Marylou Maco, Chief Revenue and Customer Experience Officer at Avaya. “Seeing them bring to life ‘One Intelligence’ is truly a testament to the power of innovation at the speed of AI.”

Also Read: Three Myths That Are Keeping Brands Away From AI

Beyond the Contact Center

The broader significance of the Avaya-avatarin partnership lies in what it suggests about the future architecture of customer experience infrastructure. As AI moves from screens into physical spaces, the systems that manage customer context, route interactions, and preserve conversation history must extend beyond the traditional contact center perimeter.

Avaya Infinity’s hybrid cloud design — supporting private, on-premises, and cloud environments without requiring organizations to choose between flexibility and data sovereignty — positions it as infrastructure for exactly that expanded perimeter. The avatarin deployment is an early proof-of-concept. The market it points toward is considerably larger.

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Artificial intelligence-powered search and answer engines have overtaken traditional search engine optimization as the leading content distribution channel for B2B technology marketers — and most organizations are not yet producing content designed to perform in that environment. That is the central finding of a new report from 10Fold, a B2B technology communications agency, based on a survey of 400 marketing decision-makers across the United States and Europe.

The report, titled The Visibility Reset: How AI Search Is Changing B2B Content Strategy, was conducted in partnership with research firm Sapio Research and marks a significant shift in how B2B buyers discover, evaluate, and validate solutions. Fifty-two percent of respondents ranked AI-generated search and answer engines as their most effective content distribution channel — displacing SEO from the top position it has held for years.

A Readiness Gap

The urgency of that shift is broadly understood. More than half of respondents said visibility in AI-generated search is very important, and 15 percent called it a top strategic priority. Yet the majority — 41 percent — said only between a quarter and half of their content had been created or updated for AI-driven search in the past year.

The gap between recognition and readiness is the report’s most consequential finding. B2B organizations understand that the rules of content visibility have changed. Most have not yet adapted their content portfolios to reflect that understanding.

The nature of the challenge is also shifting. In a traditional search environment, visibility depended on ranking. In an AI search environment, it depends on credibility, specificity, and authority — on producing content that AI systems judge worth surfacing, citing, and presenting to buyers as a trustworthy source.

“The companies that win will not be the ones that publish the most AI-generated content,” said Susan Thomas, chief executive of 10Fold. “They will be the ones that create content worth finding, citing, and believing.”

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

Credibility as the New Currency

The top content challenge cited by respondents — named by 31 percent — was earning visibility from credible sources to support stronger discovery. Differentiating in an AI-saturated market ranked as the second biggest barrier at 29 percent, followed by producing sufficient high-quality content at 23 percent.

The findings point toward a growing premium on original research, expert perspectives, and content supported by credible third-party validation — through trusted publications, analyst firms, peer reviews, and industry influencers. In an environment where AI systems are synthesizing answers from authoritative sources, being cited matters more than being clicked.

Traffic Metrics Are Being Redefined

One of the most persistent concerns about AI-generated search is that it reduces website traffic by delivering answers before buyers click through to a company’s site. The 10Fold data offers a more nuanced picture. Forty-two percent of respondents said both visibility and traffic increased as a result of AI-generated search.

More significantly, the metrics by which marketers define content success are shifting. AI and search visibility were the most frequently cited success metrics at 40 percent, ahead of marketing-qualified leads at 33 percent and brand awareness at 31 percent. Meanwhile, 85 percent of respondents said lead quality improved over the past 12 months — including 32 percent who said it improved significantly — suggesting that while AI search may be changing the volume and nature of inbound traffic, it is not necessarily degrading the commercial value of leads.

Human Oversight Remains Uneven

The report also examines how B2B marketing teams are balancing AI-generated content with human editorial judgment. Thirty-nine percent of respondents said they use a balanced collaboration between AI and humans to develop content. Another 21 percent use AI-generated drafts with human review, and 8 percent produce content that is mostly AI-generated.

Governance practices, however, remain inconsistent. Roughly a third of respondents said that every piece of AI-developed content is reviewed by both a subject-matter expert and an editor. But 9 percent said they do not review AI-generated content at all, or only spot-check it — a meaningful exposure, given that accuracy and data privacy were cited as the top barriers to AI adoption by 30 percent and 29 percent of respondents, respectively. Only 38 percent of companies reported having a formal, enterprise-wide AI usage policy in place.

Also Read: Three Myths That Are Keeping Brands Away From AI

What Marketers Are Doing About It

B2B marketers are already experimenting with a range of tactics to improve visibility in AI-powered discovery environments. The most common approach — cited by 44 percent — is improving product and solution explainer content. Creating content that answers role-specific buyer questions was cited by 39 percent, and producing quote-ready summaries or key takeaways by 35 percent.

The tactical picture suggests an industry in active transition: aware of the new rules of visibility, experimenting with responses, but not yet operating with the systematic content infrastructure that the AI search era will ultimately require.

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Despite the rapid and widespread adoption of artificial intelligence, most marketing teams remain constrained by fragmented data, slow measurement cycles, and experiments that fail to translate into scalable results. That is the central finding of the 2026 AI and Marketing Performance Index, a survey of more than 300 marketers and data leaders across the United States and Canada, released by GrowthLoop in partnership with research firm Ascend2.

The report arrives at a moment of apparent contradiction. Eighty-seven percent of marketers say they have implemented AI in their processes. Yet the structural problems that have historically limited marketing effectiveness — siloed data, lagging measurement, and an inability to connect actions to outcomes — remain largely unresolved.

Also Read: The CIO Who Says Governance Can Actually Speed Up AI

The Experimentation Paradox

The report’s most striking finding concerns the state of marketing experimentation. Fifty-eight percent of marketers say they spend a moderate or significant amount of time running tests. Only 20 percent report high impact from those efforts. More tellingly, 77 percent say that winning experiments fail at scale at least some of the time — a finding that points not to a failure of effort, but of foundation.

The report argues that the underlying cause is reliance on historical behavioral data to guide decisions. Most teams are optimizing for past performance rather than building a causal understanding of what actually drives outcomes. Just 23 percent of marketers surveyed say they can reliably link marketing actions to business results.

“AI helps marketers move faster, but it doesn’t necessarily compel them to move smarter,” said Anthony Rotio, co-founder and co-chief executive of GrowthLoop. “Many marketing teams assume they’re data-driven because they’re running tests. Without a foundation of causal data to show what’s actually driving outcomes, those tests can fall short of delivering real return on investment.”

The Data Infrastructure Gap

The report identifies fragmented data infrastructure as the root cause of most of the performance gaps it documents. Only 46 percent of organizations report having a fully centralized, single source of truth for customer data. Among those that do, the performance differential is significant: companies with a unified data foundation reported revenue growth of 44 percent, compared with 8 percent for those without one. A centralized data foundation is also associated with faster marketing speed, more effective data use, and stronger returns from experimentation.

The location of that data foundation matters as well. Organizations using data clouds or lakes are less likely to struggle with measuring real impact — 42 percent versus 54 percent — and managing manual work — 31 percent versus 38 percent — compared with those relying on marketing suites as their primary source of truth.

Personalization: Still Mostly Aspirational

Despite the volume of industry conversation around real-time personalization, the data suggests the reality is considerably more modest. Only 12 percent of marketers say they use primarily real-time signals to execute campaigns. Eighty-five percent rely on historical data, or a mix of historical and real-time data — indicating that truly dynamic, signal-driven personalization remains an aspiration for the overwhelming majority of marketing organizations.

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

The Path Forward

The report points to a consistent pattern among the organizations outperforming their peers: rather than moving data between fragmented systems, leading teams are bringing AI closer to the source — running models directly within their cloud data infrastructure and using composable AI decisioning tools to optimize campaigns in the same environment where the data lives.

“While the tools are getting smarter, the data infrastructure underneath hasn’t kept pace,” said Phil Gamache, founder of Humans of Martech. “If teams want to move fast and stay competitive, they must figure out that data bottleneck first.”

The report’s conclusion is direct: AI adoption without data consolidation is acceleration without direction. The companies pulling ahead are not simply using more AI. They are using it on better foundations.

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Nearly all workers describe themselves as curious. Less than a third say their workplace rewards them. That gap, and what is driving it wider, is the subject of SurveyMonkey’s 2026 State of Curiosity report — and its findings arrive at a moment when the question of what humans contribute alongside artificial intelligence has never been more consequential.

The report, based on a survey of 1,925 US workers conducted in April, introduces the concept of “curiosity capacity” — defined as the ability to stay open, ask sharper questions, and keep learning in an environment where AI produces polished answers faster and easier than ever before. The central argument is pointed: as AI commoditizes outputs, the differentiator is no longer what workers produce. It is the questions they ask, the assumptions they challenge, and what they notice that AI missed.

Also Read: CES 2026: The Year “Physical AI” Claimed the Real World

Three Forces Draining Curiosity at Work

The report identifies three workplace dynamics that are systematically suppressing curiosity on the job.

The first is what the report calls the AI middleman. Directors and vice presidents are nearly three times as likely as individual contributors to use AI instead of asking a colleague a question — 33 percent versus 12 percent. Conversations that once built shared judgment and organizational understanding are being replaced by prompts, quietly eroding the connective tissue of institutional knowledge.

The second is the scroll reflex. More than a third of workers who use AI say they accept AI-generated responses as-is or after only a cursory check — even though 58 percent say they trust colleagues more than AI. The path of least resistance is winning over the habit of deeper inquiry.

The third is the efficiency squeeze. Pressure for speed has compressed the space available for exploration and discussion. Only 38 percent of workers describe most meetings as genuine forums for open discussion and idea exploration. More than half say that additional unstructured time would help them be more curious at work.

The Cost of Not Asking

The organizational consequences are already visible. Half of workers say they have had to redo work because the right questions were not asked at the outset. Forty-six percent say they have witnessed time or money wasted because assumptions went unchallenged.

Yet the conditions that would correct this are precisely those that current workplace norms discourage. Nearly half of workers — 44 percent — say that asking too many questions makes them look incompetent. Among Gen Z workers, the numbers are particularly striking: 41 percent report pretending to understand something they do not, 45 percent feel pressure to already know the answer, and 42 percent have stayed silent because they felt they had already asked too much.

“AI allows us to impersonate leadership without doing the hard work of actually leading,” said Anne Morriss, founder of The Leadership Consortium, whose commentary features in the report.

Jack Soll, a distinguished professor of management and organizations at Duke University’s Fuqua School of Business, offered an equally pointed observation: “AI might make us individually smarter, but the opposing force is going to make us all the same — which might make it harder to be creative and innovative.”

Also Read: Visa Bets Big on AI Commerce, Unveils New Partnerships and Innovations

What Workers Say They Need

The report also surfaces a clear picture of what workers believe would help. Seventy-seven percent want more opportunities to brainstorm with colleagues. Seventy percent want greater psychological safety to ask questions without consequence. Sixty-one percent want stronger connections across teams, and more than half want both reduced workload and more unstructured time in the working day.

“Curiosity isn’t the problem,” said Katie Miserany, Chief Communications Officer and Head of Global Marketing at SurveyMonkey. “The way we work is. We hope this inspires everyone to start designing workplaces that strengthen curiosity capacity instead of draining it.”

The full report is available at surveymonkey.com/curiosity/state-of-curiosity-report.

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Sendbird, the AI customer experience company powering conversations for more than 300 million people every month, has launched Agent Steward, an AI agent designed to take full, end-to-end ownership of complex customer issues — and to hold that ownership across systems, channels, and time without requiring human coordination at every step.

The launch is accompanied by two platform updates — Trust OS 2.0 and Voice 2.0 — that together represent a significant expansion of Sendbird’s Delight.ai platform, pushing it from reactive AI support toward autonomous improvement and proactive customer engagement.

The Problem With Reactive AI

The case for Agent Steward begins with a structural critique of how enterprise AI currently operates. Despite rapid adoption, AI agents remain largely channel-specific and reactive — waiting for a prompt, handling the immediate query, and handing off to human teams when anything more complex arises. Mistakes are identified and corrected after the fact, if at all.

Consumer expectations reflect the gap. According to new data from Sendbird, 57 percent of consumers say the ability for AI to correct its own mistakes and reverse decisions would increase their trust. Nearly two-thirds — 59 percent — say the ability to stop or override an AI agent is very important to them. The signal is clear: what customers want from AI is not just competence. It is accountability.

Also Read: The CIO Who Says Governance Can Actually Speed Up AI

Agent Steward: One Owner, Start to Finish

Agent Steward is built specifically for long-horizon, multi-step workflows — the category of customer issues that current AI systems handle poorly. Rather than routing a problem across multiple agents and teams, Agent Steward acts as a single point of accountability from first contact through to resolution, coordinating across backend systems, chat, SMS, and email while escalating to a human only when genuine judgment is required.

The practical implications are significant. A customer who receives a damaged product from a third-party logistics partner currently faces a resolution process that can span days, involve multiple handoffs, and result in a chargeback. With Agent Steward, the issue is identified proactively, owned by a single agent throughout, and resolved within hours — with human involvement triggered only at decision points that require it.

“Most AI systems are effective at handling routine queries, but break down when it comes to complex, multi-step issues,” said John Kim, co-founder and chief executive of Sendbird. “The goal isn’t to replace human agents, but to elevate them — freeing them to focus on judgment and exceptions while AI owns coordination, follow-through, and continuous improvement.”

Trust OS 2.0: AI That Fixes Itself

The launch of Trust OS 2.0 introduces what Sendbird calls Zero-Touch Improvement — a capability that enables AI systems to identify, diagnose, and correct their own errors in real time, without requiring manual intervention from engineering or operations teams.

The contrast with traditional AI management is stark. Under conventional frameworks, identifying a model error, rewriting the relevant rules, testing the fix, and deploying it can take days or weeks. Zero-Touch Improvement compresses that cycle to real time, shifting AI governance from a supervisory burden to an autonomous function.

Also Read: Is Your CRM Making Your Customer Service Worse?

Voice 2.0: Reaching Customers Before They Ask

Voice 2.0 extends the platform’s capabilities to proactive outbound engagement, allowing companies to initiate contact with customers when a potential issue is identified, rather than after it has already created a problem. The capability supports more than 100 languages, enabling global deployment without localization overhead.

The use cases are immediate and practical. An airline can notify a traveler of a disruption before they reach the airport. A financial services company can send a payment reminder before a deadline is missed. The shift from reactive support to anticipatory engagement represents a meaningful change in how AI interacts with customers across the full relationship lifecycle.

Agent Steward, Trust OS 2.0, and Voice 2.0 are available now as part of the Delight.ai platform, following their debut at Sendbird’s annual customer experience event, Delight Spark.

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Omnisend, the email and SMS marketing platform built for e-commerce, has launched a Model Context Protocol integration that allows merchants to access and operate the platform directly within ChatGPT — without switching tabs, opening a separate dashboard, or learning a new interface.

The integration, now available to all active Omnisend account holders globally, lets marketers analyze campaign performance, identify revenue opportunities, and create and send campaigns using plain-language prompts from within the same environment where they already plan and make decisions.

Also Read: Part 2: From Tech Stacks to Trust Stacks, Marketing’s Proof Moment Arrives

The Problem It Solves

The premise behind the integration is straightforward. Marketers are already using ChatGPT as a thinking and planning tool. Requiring them to leave that environment to execute on the insights it generates creates friction — and, in marketing operations, friction tends to mean delayed decisions and missed opportunities.

“MCP is based on a simple idea: people do not want another place to work,” said Bernard Meyer, AI Operations Manager at Omnisend. “They are already using ChatGPT to think through problems, plan campaigns, and make decisions. Instead of asking merchants to come to us, MCP lets Omnisend meet them where the work is already happening.”

What It Can Do

The integration operates across three practical functions.

For performance analysis, merchants can ask direct questions in natural language — such as what drove revenue over the past week, how a recent campaign performed compared to the one before it, or why revenue declined on a given day — without manually navigating reports or comparing dashboards.

For strategic prioritization, the integration surfaces recommended next steps based on store performance and existing marketing activity, helping merchants identify gaps such as missing automation flows or underleveraged audience segments.

For execution, merchants can move directly from insight to action within the same conversation. A prompt asking Omnisend to create a reactivation campaign for customers who have not purchased in 30 days, or to send a weekend sale email to the most engaged subscribers, can be actioned immediately without breaking workflow.

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Access and Data Handling

The integration is available now to all active Omnisend account holders through the Omnisend app in ChatGPT. Access may require a paid ChatGPT plan, depending on the user’s account tier. Users connect their Omnisend account directly within ChatGPT and approve access when prompted. Omnisend says it shares only the data necessary to fulfill each individual request, and users can disconnect the integration at any time.

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