Iridio, RRD’s social media marketing platform, has expanded its integrations to include Reddit and LinkedIn — giving brands access to two distinct yet strategically valuable audience environments that largely sit outside the conventional Facebook, Instagram, and TikTok playbook.

The expansion is backed by internal performance data that makes a direct commercial argument for the multichannel approach: combining digital display with paid social produces a 67 percent higher average featured sales lift compared to social-only campaigns, according to an Iridio study of nearly 60 consumer packaged goods campaigns conducted between the fourth quarter of 2023 and the second quarter of 2025.

Why Reddit and LinkedIn

The case for Reddit rests on audience composition as much as scale. The platform reaches more than 190 million weekly active unique users — but its value to Iridio’s clients lies in who those users are relative to other platforms. Approximately 28 percent of Reddit users are not active on Facebook, 36 percent are not on Instagram, and 38 percent are not on TikTok. For brands that have saturated conventional social channels, Reddit represents a meaningful pool of consumers they are not currently reaching.

The platform’s subreddit structure also enables contextual targeting that goes beyond demographic profiling. Brands can align messaging with the specific topics and communities their prospective customers are actively researching — a form of relevance that standard social placements rarely achieve.

LinkedIn extends the proposition into a completely different environment. As the world’s largest professional network, it provides access to business decision-makers in a context where professional identity and purchasing authority are explicit rather than inferred. Iridio is currently in beta testing for LinkedIn campaigns, with full integration expected later in 2026.

The Technology Behind the Targeting

Both integrations are powered by RRD’s patented Consumer Graph and Household Connect technologies — proprietary infrastructure that Iridio positions as a privacy-first alternative to cookie-dependent targeting.

The Consumer Graph maps 130 million unique household personas by connecting layers of intelligence from offline and online data sources, enabling high-precision audience identification without relying on third-party data. Household Connect extends this by grouping multiple devices — phones, televisions, laptops — based on shared behavior and location, creating a unified digital profile of a household and mapping it to a physical address. The result is targeted, compliant marketing at scale across devices, channels, and platforms.

Every social campaign also includes advanced brand safety monitoring, using tiered sensitivity settings and keyword blocklists to ensure ads appear only in appropriate, high-quality environments.

Also Read: Why the Future of Advertising Is Built on Probability

The Performance Case for Combination

The headline finding from Iridio’s internal study is the 67 percent sales lift premium from combining display and social. But the supporting data makes an equally compelling argument. Campaigns that combined display and paid social produced a 47 percent higher average Brand Halo lift — a metric that captures conversion across all of a brand’s measured product lines, not just the featured item — and generated four times the incremental sales compared to single-channel social campaigns.

“We are giving our clients the ability to connect with consumers no matter what platform they are on, while quantifying the direct impact on featured sales lift and incremental revenue,” said Andy Johnson, Senior Vice President and Principal at Iridio. “By integrating Reddit and LinkedIn, we are opening new doors for brands to be part of the conversations that actually drive purchase decisions.”

Iridio operates as a managed service, combining its proprietary demand-side platform with creative, media, and data analytics capabilities. 

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HubSpot has launched AEO Sensor, a free, publicly accessible dashboard designed to track how AI answer engines — ChatGPT, Gemini, and Perplexity — are behaving toward brands in real time. The launch arrives alongside a data point that will focus attention: according to HubSpot’s own infrastructure, ChatGPT sent its lowest volume of traffic to businesses in April 2026 compared to any point in the preceding 12 months.

The tool is available without a subscription or account, positioning it as a shared industry instrument rather than a proprietary commercial product — a deliberate contrast to HubSpot AEO, the paid brand-level monitoring and optimization service the company launched in April 2026 at $50 per month.

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What AEO Sensor Measures

The dashboard is organized around three data streams, each addressing a distinct dimension of the AI visibility problem.

The first is an Answer Engine Volatility Tracker, which generates a daily score derived from four inputs: mention rate, citation rate, citation type, and AI-referred traffic, each measured against rolling averages across ChatGPT, Gemini, and Perplexity. The scoring system uses four classifications — Calm (0–29), Moderate (30–59), Elevated (60–89), and Extreme (90 and above). On launch day, May 14, the tracker registered 20, placing conditions in the Calm range. HubSpot notes that daily scores may adjust for up to three days after calculation, reflecting the time required for data from all three platforms to stabilize — meaning the dashboard is designed for directional trend reading rather than precise point-in-time measurement.

The second is an AI-Referred Traffic Trends chart, drawing on anonymized data from HubSpot’s customer base and modeling it to show aggregated weekly page visit trends from all three platforms. The chart at launch covers data running back to February 16, 2026, providing approximately three months of historical context. HubSpot notes that actual results may differ by region and language, and that it can take several hours for traffic data to populate after each weekly close.

The third covers AI Visibility Benchmarks by Industry, allowing users to track visibility score and citation share across sectors using representative brand examples. The manufacturing segment, for instance, tracks Nvidia, TSMC, Ford, and Volkswagen Group, with data running from late March through early May 2026. Both visibility score and citation share use a 0–100 percent range. HubSpot is explicit that benchmarks reflect trends from representative brands within each sector rather than exhaustive market coverage.

The April Traffic Signal

The data point with the most immediate implications for the marketing community is the April traffic finding. HubSpot’s internal analysis shows ChatGPT sent its lowest volume of referral traffic to businesses in April 2026 relative to the previous 12 months. The company also flags significant citation share fluctuations among information technology companies during the same period.

Neither figure is accompanied by a precise percentage decline or absolute traffic number — HubSpot frames both as directional observations from its anonymized customer dataset. But the pattern is consistent with a sequence of documented disruptions that stretches back more than a year.

ChatGPT’s referral traffic dropped 52 percent in July 2025 when OpenAI adjusted how its retrieval-augmented generation system weighted sources, according to research published by Profound drawing on more than one billion ChatGPT citations. That was the first major documented instance of a model-level citation reconfiguration producing measurable traffic effects across publishers. Analysis of the ChatGPT 5.3 update in March 2026 found that the average number of unique domains per response fell from approximately 19.8 to 15.9 — a roughly 20 percent reduction in outbound linking per response. A separate study from February 2026 found that ChatGPT sends approximately 190 times less traffic than Google despite accounting for around 12 percent of search volume, with an estimated click-through rate of 1.3 percent.

If the April low that HubSpot describes is confirmed across a wider dataset, it would extend this sequence and suggest the traffic contraction from AI answer engines is not stabilizing.

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

The Product Stack It Completes

AEO Sensor is the third instrument in HubSpot’s answer engine product architecture, following HubSpot AEO and the AEO Grader.

The three tools operate at different levels of specificity. AEO Grader provides a one-time snapshot of a single brand’s sentiment, recognition, and competitive standing. HubSpot AEO provides ongoing daily monitoring and recommendations for a specific brand, priced at $50 per month. AEO Sensor sits above both as a macro-level instrument — no brand-specific data, no subscription, no login.

The product stack is built substantially on the infrastructure and methodology of XFunnel, an answer engine optimization platform that HubSpot acquired on October 31, 2025. XFunnel had, at the time of acquisition, analyzed 1,500 companies, collected more than five million AI responses, and examined more than 25 million citations. Its co-founder Beeri Amiel now serves as HubSpot’s Director of Product Development.

What the Tool Can and Cannot Do

AEO Sensor does not resolve the attribution problem that has been complicating AI traffic measurement since mid-2024. It does not tell a brand how it specifically is being cited, whether a competitor is gaining ground, or why citation patterns have shifted. What it provides is an industry-level temperature reading — a shared reference point that allows a marketer to distinguish between volatility that is affecting everyone and volatility that is specific to their own content or brand positioning.

A high daily volatility score, for instance, signals that citation patterns across the tracked platforms are shifting broadly — providing essential context for interpreting changes in a brand’s own visibility data that might otherwise appear to reflect a specific content or strategy failure.

The dashboard’s scope covers ChatGPT, Gemini, and Perplexity — the three platforms for which HubSpot has sufficient traffic and citation data to construct meaningful baselines. Notably absent are Microsoft Copilot, Claude, and other large language models that increasingly receive direct search queries. The three-platform focus means the dashboard does not capture the full picture of answer engine behavior affecting brand visibility, a limitation HubSpot acknowledges.

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

Why This Matters

The structural problem AEO Sensor addresses has been building for several years. HubSpot’s own customer data showed organic traffic falling 27 percent year over year as of the April 2026 AEO product launch — a figure that put quantitative weight behind a qualitative observation circulating in marketing circles since mid-2025: traditional search optimization is becoming a less reliable traffic source at precisely the moment AI answer engines are not yet reliable enough as a replacement to build strategy around.

Research from the Wall Street Journal documented AI chatbot referrals growing from under one million visits in early 2024 to more than 230 million monthly by September 2025 — significant growth in absolute terms, but distributed across the entire web in a pattern that most individual brands cannot yet measure reliably. Citation sets in AI responses change between 40 and 60 percent within a single month, according to research on the ChatGPT 5.3 update — a rate of turnover that makes stable brand representation difficult to assume in any competitive category.

The free positioning of AEO Sensor reflects the same logic that market research firms have long used with publicly released benchmark reports: surface the scale of the problem at no cost, and capture the monetizable demand for solutions in the paid product layer. The marketing community gets a shared instrument. HubSpot gets a sustained top-of-funnel argument for HubSpot AEO.

AEO Sensor is accessible at HubSpot’s marketing properties without a subscription or account.

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Givaudan, the global flavors and fragrances company, has significantly accelerated the pace of its research and decision-making in Europe after deploying Stravito, an AI-powered insights platform, across its Taste and Wellbeing Marketing and Consumer and Sensory Insights teams.

The team, which supports some of the world’s largest food and beverage brands with insight-led flavor strategies, estimates that decision-making on many projects is now three times faster than before — and up to ten times faster in some cases. Projects that previously took weeks can now be completed in days or hours.

The Problem: Insights Scattered, Time Wasted

Givaudan’s Consumer and Sensory Insights team operates at the intersection of science and creativity. When product development teams bring new flavor concepts to life, they depend on consumer and sensory insights to ensure those concepts meet real human needs. The quality of that input directly shapes the quality of the output.

But accessing the right research had become a meaningful operational problem. Insights were distributed across external platforms, internal systems, personal folders, and the institutional memory of individual team members. Answering a single customer brief often required knowing both where the relevant information lived and who to ask for help finding it. Some team members invested time tracking everything down. Many, understandably, did not — because the search cost was simply too high.

The consequences were predictable: slower decisions, a growing reliance on inference when evidence could not be found quickly enough, and a creeping search fatigue that threatened the team’s insight-led working culture. What the team wanted was straightforward — a search experience for internal research that felt as natural and reliable as a general web search.

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

The Solution: Bringing “Genie” to Life

After evaluating several vendors, the team selected Stravito. Four factors drove the decision.

The first was the quality of the relationship. From early conversations, the Stravito team demonstrated a service-oriented approach that resonated with Givaudan’s business-to-business culture. “The attitude made a big difference,” said Basak Oker, a member of the insights team. “You could feel, ‘We’re here for you.'”

The second was user experience. The platform felt modern, intuitive, and self-explanatory — users could understand how it worked within minutes, without extensive training. For a tool intended to drive adoption well beyond the core insights function, accessibility was essential.

The third was speed to value. The team needed a solution they could implement quickly, including integrations with key external content providers. Stravito supported both the technical setup and the internal rollout without significant delay.

The fourth was the overall value. Stravito was not selected for being the cheapest option, but because the combination of speed, usability, support, and adoption potential made a clear business case.

The team named their Stravito instance “The Genie” — in the hope, as the name suggests, that it would make their insights wishes come true.

The Results: Faster Decisions, Richer Conversations

Even in the early period following launch, the impact has been tangible across multiple dimensions.

Stravito’s AI assistant surfaces relevant internal and external research quickly, compressing project timelines and reducing the likelihood that teams commission new research when the answers already exist in the organization’s own knowledge base. The reduction in duplicated work frees both budget and bandwidth for genuinely new questions.

The platform supports both proactive and reactive work. For proactive innovation initiatives — including a process that helps customers develop what the team calls “iconic” flavors — Stravito enables faster, more systematic immersion in category, consumer, and sensory trends. For reactive customer briefs, regional teams can rapidly build an understanding of local markets they do not physically operate in, drawing on Givaudan’s own research and external sources in a single interface. The result, the team reports, is more informed internal discussions and more confident, substantive conversations with customers.

Formal performance metrics are being developed, with further impact measurement underway.

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

What Comes Next

The human benefit of the platform may be as significant as the operational one. Less time spent searching and synthesizing means more time available for the creative and collaborative work that the insights function exists to enable.

“Stravito gives people more time to think about new opportunities and new ideas,” said Oker — and, she added, more time at home with family.

Stravito is currently available to regional and local marketing and consumer and sensory insights teams across Europe. The next phase is to extend access to sales and key technical stakeholders, enabling more people across the organization to lead insight-led conversations with customers. Broader global expansion is under consideration.

For Givaudan, the ambition has moved beyond improving access to insights. The goal now is to translate that access into faster, better-informed decisions — consistently, at scale, and across an organization of more than 16,900 people worldwide.

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Publicis Groupe has agreed to acquire LiveRamp, the global data collaboration platform, for a total enterprise value of $2.167 billion in an all-cash transaction — its most significant strategic bet since the $4.4 billion acquisition of Epsilon in 2019 and a clear signal of where the world’s third-largest advertising group believes the next competitive frontier lies.

The deal, priced at $38.50 per share and representing a 29.8 percent premium to LiveRamp’s closing price on May 15, has been unanimously approved by the boards of both companies. It is expected to close before the end of 2026, subject to regulatory approval and LiveRamp shareholder sign-off.

What LiveRamp Is

LiveRamp is not a conventional advertising technology company. It is infrastructure — a data collaboration platform that enables organizations to unify, manage, and activate data across the digital ecosystem without exposing the underlying sensitive information that makes that data valuable.

Its network spans more than 25,000 publisher domains and over 500 technology and data partners across 14 markets. Thousands of brands, retailers, media platforms, and data providers use its clean room technology to collaborate on data they could not share through conventional means. With 1,300 employees and a business anchored in highly recurring revenue, LiveRamp has delivered a compound annual revenue growth rate of 13 percent over the past five years.

Why Publicis Is Buying It

The acquisition is a direct response to what Publicis identifies as the defining constraint on enterprise AI adoption: most companies lack the right data to make their AI systems genuinely effective. According to figures cited by Publicis, 93 percent of companies do not currently have the data infrastructure required for AI success.

LiveRamp addresses that gap through what Publicis calls data co-creation — the process by which companies combine multiple high-value data sources across partners in secure, governed environments to generate new proprietary data assets that no single organization could build alone.

Combined with Epsilon’s identity resolution capabilities, LiveRamp’s collaborative infrastructure is designed to enable clients to build AI agents that are more capable, more differentiated, and more commercially effective than anything a single organization’s data could support independently.

“After acquiring Epsilon in the name of personalization at scale and enabling our clients to take back control of their data from the walled gardens,” said Arthur Sadoun, Chairman and Chief Executive of Publicis Groupe, “once again we are looking ahead to what’s next. By building the future of data co-creation, we’re empowering our clients to generate new, exclusive and proprietary data — to build the smartest, most differentiated AI agents on top of the leading large language models.”

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The Use Cases

Publicis has been explicit about what data co-creation enables in practice, offering three illustrative examples.

A bank could build a wealth management agent that draws on tokenized customer data from retail banking, credit cards, and wealth management, combined with partner data from merchants, payment networks, and travel providers — without exposing underlying customer records. The result is an agent capable of cross-selling across multiple business lines with far greater precision than any single dataset would allow.

A retailer could connect loyalty, in-store, and retail media data with partner signals to measure the incremental value of each customer touchpoint and build new, proprietary shopper journeys — turning retail media from a cost center into a measurable growth driver.

A pharmaceutical company could build a therapeutic area optimization agent that draws on clinical, commercial, supply chain, and de-identified patient data simultaneously — enabling more efficient field-force deployment and better product lifecycle management across an entire portfolio.

The Strategic Architecture

The LiveRamp acquisition completes what Publicis describes as an end-to-end capability stack for agentic business transformation. Publicis Sapient provides the technology modernization layer that makes enterprise infrastructure AI-ready. Epsilon’s identity resolution connects agents to real people, behaviors, and deterministic transactions. LiveRamp enables secure data collaboration across partners to generate the co-created data that fuels smarter agents. Marcel, Publicis’s internal agentic platform, activates that data across enterprise functions.

Each component addresses a different layer of the same problem. Together, Publicis is arguing, they constitute a capability set that no competitor can currently match end to end.

Financial Implications

The transaction is expected to be earnings-accretive from the first year of consolidation, excluding transaction-related costs. Publicis is funding the acquisition with cash on hand and debt, maintaining financial leverage within existing investment-grade rating parameters, with full deleveraging expected within two years of closing.

The deal also allows Publicis to raise its medium-term financial guidance. The Groupe now targets net revenue growth of 7 to 8 percent and headline earnings per share growth of 8 to 10 percent at constant currency in 2027 and 2028 — up from prior targets of 6 to 7 percent and 7 to 9 percent respectively.

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What Changes — and What Does Not

LiveRamp will continue to operate as a neutral, interoperable platform following the acquisition. Chief Executive Scott Howe will remain in his role, reporting directly to Sadoun. The company’s data will continue to be protected in accordance with existing contractual commitments. Pricing and commercial practices will remain unchanged outside the normal course of business.

The commitment to neutrality is not incidental. LiveRamp’s value depends on the trust of the 500-plus technology and data partners and 25,000-plus publishers in its network — a trust that would erode quickly if the platform were perceived to operate in the exclusive interest of its new parent company.

“Our customers and partners have always been our North Star,” said Howe. “By joining forces with Publicis, we will have greater resources and flexibility to scale our business, continue innovating our platform, and help them unlock even greater value from their data.”

The transaction is expected to close before year-end 2026.

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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.

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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.

The post Avaya and avatarin Unite Robots, AI, and Humans in CX appeared first on MartechView.

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.

The post AI Is Speeding Up Marketing, Not Improving It: GrowthLoop appeared first on MartechView.