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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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.

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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.

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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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For most of the past decade and a half, the annual marketing technology landscape had one reliable characteristic: it grew. Sometimes by a lot. Sometimes by a merely extraordinary amount. Even in the years when analysts confidently predicted consolidation was finally upon us, the landscape found another gear. This year, it did not.

The State of Martech 2026, which debuted at MartechDay on May 5 by Scott Brinker of chiefmartec and Frans Riemersma of MartechTribe, puts the total number of marketing technology products at 15,505 — up just 121 from the 15,384 counted last year. That is growth of 0.79%, rounding to effectively zero. After a run of more than 10,000% expansion since 2011, when the landscape counted just 150 products, the market appears to have hit a ceiling — or at least a plateau.

But flat, as the report and keynote make abundantly clear, is perhaps the most misleading word one could use.

A Market Metabolizing, Not Stagnating

Beneath that near-zero headline number, the market is moving with real intensity. In the past 12 months, 1,488 new products were added to the landscape while 1,367 were removed. The volume of new entrants dropped 40% year on year — down from 2,489 in 2025 — while the removal rate climbed 13%. For the first time in the post-pandemic era, additions and removals are nearly canceling each other out.

Riemersma’s framing at MartechDay was direct: “Peak Martech is a myth. Martech is entering its Darwin phase. The martech landscape is renewing. Value is growing.” The era of accumulating tools, both argued, is giving way to an era of replacing them. At the core of that transition is a structural change in where value actually lives: SaaS platforms are no longer the primary source of differentiation. They are becoming infrastructure — systems of record, workflow engines, and integration layers. The real value is moving on top of that foundation. AI is becoming the value layer.

The companies exiting the market tell their own story. More than half of this year’s removals — 51.7% — came from the 2010–2019 wave of software-as-a-service startups, the first great generation of martech builders. The exits are concentrated among smaller firms: 41.2% had between one and ten employees; 38.7% had between 11 and 50. By revenue, the $1 million to $10 million band accounts for 45.5% of removed products — companies that found enough early traction to survive past zero revenue, but not enough to build a truly defensible position, caught between incumbents bundling AI features from above and AI-native startups attacking from below.

The Content Marketing Bust

Perhaps the single most striking data point concerns content marketing tools. When generative AI went mainstream in 2023, content marketing was one of the first categories to feel the full force of the wave, nearly doubling in two years from 575 tools to 1,102. In 2026, it leads all subcategories in a less coveted ranking: the highest net product removal of any category, at minus-37, with 176 removed and only 139 added.

Three forces converged. The major AI laboratories absorbed the core functionality; incumbent platforms such as Adobe, HubSpot, and Salesforce rapidly embedded generative AI into existing workflows; and many first-wave tools solved the problem of generating content fast without solving the harder problem of generating content that actually works. The report describes this as a natural selection event: not the end of AI-powered content technology, but the clearing out of an undifferentiated first generation in favor of a more mature second.

The Stack Is Stratifying, Not Consolidating

One of the most significant conclusions from the MartechDay keynote — drawing on a survey of 208 marketing and marketing operations leaders across 70 specific AI use cases — is that the long-running debate between platform consolidation and best-of-breed diversification has a 2026 answer: neither. Instead, the stack is stratifying into layers with different competitive physics. 

AI-native tools are largely winning creation — copy ideation, pitch decks, visual production, competitive intelligence — tasks where the primary input is a prompt and model quality is the product. Incumbent SaaS platforms such as HubSpot and Salesforce are largely holding on to orchestration: lead scoring and routing, pipeline management, and channel delivery. These systems increasingly serve as infrastructure for other commercial and custom AI agents.

The survey also revealed a striking divergence between B2B and B2C adoption patterns. Conventional wisdom holds that B2C leads technology adoption. On AI, the data inverts that pattern: B2B shows broader adoption across more use cases, with consistently lower non-adoption rates — likely because B2B teams are chronically understaffed relative to their content and operational demands, and a decade of CRM, MAP, CDP, and revenue intelligence investment had already built natural docking stations for AI capabilities. When B2C does adopt, it builds deeper: the customer-facing AI output is the brand experience, and the differentiation lives in the final 20% — brand voice calibration, proprietary guardrails, custom data integration — that off-the-shelf tools cannot provide.

The AI Agent Paradox

A central tension running through the MartechDay findings is the gap between AI enthusiasm and AI deployment, as researchers described it. Some 90.3% of marketing organizations now use AI agents in some capacity, yet only 23.3% have deployed them in full production. The rest are piloting, experimenting, or running agents in narrow workflows with a human approving every output. The report identifies this as the “Trust Wall”: currently, 80.6% of marketing organizations refuse to let AI agents operate autonomously, requiring a human in the loop for every final decision.

Governance is moving in the right direction — 73% of respondents now report having a formal generative AI policy, up from 52% in 2024 — but the gap between having a policy and having the infrastructure to enforce it remains wide.

Where Growth Is Actually Happening

If content marketing is the cautionary tale, content management systems and e-commerce platforms are the 2026 growth story. CMS and web experience management grew 21.4%, jumping from 504 to 612 products. E-commerce platforms grew 19.9%, from 547 to 656. These are not new categories. They are being reshaped. CMS is evolving into a machine-readable infrastructure for AI agents. E-commerce is adapting to AI-driven discovery. iPaaS is becoming the orchestration layer that connects everything. Growth is happening where AI changes the job to be done.

The explanation lies in a fundamental shift in who—or what—digital properties are built for. For two decades, marketing teams designed experiences primarily for human visitors and search engine crawlers. That audience now includes AI search assistants, agentic browsers, shopping agents, and procurement systems that arrive not to browse but to extract, evaluate, and act. Other fast-growing subcategories follow the same logic: mobile and web analytics grew 11.3%, call analytics 8.9%, data integration 8.0%, and marketing automation 5.9% — the last a sign that AI is reinventing what campaign orchestration can look like, attracting builders who see agentic marketing automation as a meaningful step beyond rule-based systems.

SEO Becomes AEO — but Visibility Is Shrinking

Search engine optimization, widely eulogized as AI assistants swallowed the top of the funnel, is in fact metamorphosing rather than dying. The SEO and answer engine optimization subcategory posted a net positive result this year — 44 added, 38 removed — and has grown for three consecutive years. The market is reflecting a shift in the underlying discipline: from making brands findable by search crawlers to making them findable, credible, and actionable across AI search assistants, answer engines, and agentic browsers. The challenge, the report notes, is that the tools are improving while the marketer’s visibility is shrinking — when a customer consults an AI assistant about which product to buy, that conversation is entirely invisible to conventional tracking.

The Transformation Beneath the Numbers

What ties these shifts together is a structural transformation of marketing itself. As Brinker argued in the lead-up to MartechDay: “AI doesn’t eliminate constraints. It moves them. When content becomes abundant, the bottleneck shifts to relevance. When integrations get easier, the bottleneck shifts to orchestration.” The organizations pulling ahead are those that have recognized where the new bottleneck sits and invested in context engineering, governance, and strategic coherence — rather than continuing to optimize against constraints that AI has already dissolved.

The best stacks are not the most feature-rich. They are the most aligned — focused on a small number of high-impact use cases where SaaS enables, and AI amplifies. Integration is no longer just technical. It is a strategic asset.

Whether 2026 marks the peak of martech or simply a pause before the next expansion remains genuinely uncertain. Brinker and Riemersma’s own position is the latter. The cost to build keeps falling, AI keeps opening new niches, and the minimum viable scale for a sustainable martech business keeps shrinking. The landscape is metabolizing — not dying. But the shape of whatever emerges from the chrysalis will bear little resemblance to what went in.

The State of Martech 2026 was debuted by Scott Brinker and Frans Riemersma at MartechDay on May 5, 2026, and is available free at chiefmartec.com.

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Brands that fail to adapt their marketing strategies for the age of artificial intelligence risk being passed over entirely — not by consumers, but by the AI systems increasingly making decisions on their behalf.

That is the central warning of a new report from digital marketing agency Jellyfish, Brands in the AI Era: Generative Engine Marketing, which argues that large language models have become powerful intermediaries between products and the people who buy them.

The report introduces a framework called Generative Engine Marketing, or GEM, which it positions as a systematic approach to ensuring brands are accurately represented and reliably surfaced by AI systems — whether those systems are recommending a product, answering a question, or completing a purchase autonomously.

“Brands have historically been made by people, for people,” said John Dawson, Vice President of Strategy at Jellyfish and a co-author of the report. “Now, AI is an actor in the system: watching, recommending, choosing, and even purchasing.”

The framework builds on Jellyfish’s proprietary Share of Model platform, which analyses how leading language models perceive a given brand, and combines it with a continuous cycle of technical optimization, content strategy, testing and measurement. The goal, the report states, is to make brand assets legible to models and resonant to human audiences simultaneously.

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The report’s early results are striking. Gentle Monster, the South Korean eyewear brand, used LLM-driven insights to optimize its Google Performance Max campaigns ahead of the US holiday season, achieving a 17 percent improvement in click-through rate, a 14 percent lift in conversion rate and a 39 percent gain in return on ad spend. Industrial supplier MSC Industrial reported a 45 percent revenue increase in the first 30 days after implementing AI-driven campaign optimizations, with an incremental return on ad spend of 758 percent.

Dawson acknowledged that the industry has weathered major technological shifts before — the rise of digital, social media and mobile — but argued this moment is categorically different. For the first time, he said, brands must cultivate relationships not only with consumers but with the models that mediate their choices.

“GEM reframes this challenge as an opportunity,” he said, “to design brands that are legible to machines, resonant to humans, and optimized for both.”

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Appian has announced a set of platform enhancements designed to make AI agents more reliable and controllable in enterprise environments, including AI-assisted spec-driven development, Model Context Protocol integration, and a new technology partnership with Snowflake.

The announcements, made at Appian World 2026 in Orlando, reflect a consistent strategic argument the company has been making: that the primary barriers to AI value in enterprise settings are not model capability but fragmented data, lack of structure, and insufficient control. Appian’s approach anchors AI agents within defined process models, which the company says provide the guardrails needed to operate safely at scale.

The platform’s AI agents are being enhanced in two significant ways. First, by adopting the Model Context Protocol standard, Appian agents will be able to interface securely with external enterprise systems, while third-party AI agents will gain access to Appian’s data fabric, which provides unified read-write access to enterprise data across systems. Second, the platform will allow users to track agent performance over time and apply learned memory across processes to improve decision-making, with the ability to set optimization objectives and receive improvement recommendations for review before application.

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The Snowflake partnership connects Appian’s process orchestration and data fabric directly with Snowflake’s AI Data Cloud through MCP integration, combining data aggregation, model training and process execution. The integration allows agents to interact with Snowflake Cortex AI to surface data-backed decisions within live workflows.

“Enterprises don’t need more AI experiments; they need AI that delivers real business outcomes on governed data,” said Baris Gultekin, VP of AI at Snowflake. “By combining Appian’s process orchestration and data fabric with the Snowflake AI Data Cloud, we’re bringing intelligence directly into the flow of work. Together, we enable secure, enterprise-grade AI where agents can access trusted data through Cortex AI, act with context, and drive measurable impact across the business.”

The second major announcement is AI-assisted spec-driven development, which Appian describes as a response to the limitations of standard AI code generation in mission-critical environments. Rather than generating code directly, the approach uses AI to extract specifications from legacy applications, producing a visual plan covering user interface, data models and process flows. Developer agents then complete tasks according to those specifications under human supervision. New developer MCP servers will allow teams to use external AI development tools — including Claude Code and Kiro — to build and update Appian applications, with support for a broad range of AI models.

“Composer complements Appian’s agentic orchestration and data fabric with new spec-driven development tools that are both conversational and iterative,” said Mike Beckley, chief technology officer and founder of Appian. “Beneath the covers, Appian Composer is built on Appian’s new open MCP — a model-driven representation of your complete application estate — now exposed as context for developers and agents to safely evolve and optimize.”

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Global Excel Management, a healthcare risk management company, said it is using Appian to unify its claims processes. “From initial intake to adjudication, our advanced technology will reduce redundant tasks and lessen complexity for our team members,” said Pascal Tanguay, SVP of global technology services at the company.

The enhancements announced at Appian World 2026 will be available in the coming platform releases.

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Adobe has completed its acquisition of Semrush, the brand visibility and search intelligence platform, in a move designed to extend its customer experience tools into an era in which AI agents are increasingly determining how consumers discover and evaluate brands.

The deal brings together Semrush’s search engine optimization capabilities and agentic search intelligence with Adobe’s existing portfolio of content, commerce and customer engagement products — including Adobe Experience Manager, Adobe LLM Optimizer and Adobe Experience Platform. The combined offering is designed to give marketers a single integrated system covering how their brands appear across traditional search engines, large language models and AI-powered agents.

The acquisition comes as AI-driven traffic to retail sites surges. Adobe data shows AI-generated traffic to US retail sites increased 269% year over year as of March 2026, while the company says most businesses have significant gaps in how their brands appear across AI surfaces.

“The rules of brand discovery and commerce are being rewritten in real time, and marketers who aren’t optimizing for that world today will find themselves invisible tomorrow,” said Anil Chakravarthy, president of Adobe’s customer experience orchestration business. “Together with Semrush’s leading SEO platform and agentic search intelligence, Adobe will empower our customers with the full picture of how their brands show up to consumers — from discoverability in search engines and LLMs to content creation, customer engagement and conversion, all in one integrated system at scale.”

Bill Wagner, chief executive of Semrush, said the combination represented an opportunity to build a definitive platform for brand visibility in an AI-driven world. “Semrush has spent more than 17 years helping marketers scale and grow — and that mission has never been more important than it is today,” he said. “By joining Adobe, we see an incredible opportunity to help marketers ensure their brands are found, trusted and chosen at every touchpoint.”

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Adobe framed the acquisition as part of a broader repositioning around what it calls customer experience orchestration — an approach that treats content supply chain, customer engagement and brand visibility as interconnected rather than separate functions. The company recently introduced Adobe CX Enterprise, an agentic AI system designed to bring those functions together under a single intelligence and governance layer.

Semrush customers can expect continued product investment and an expanded roadmap as the two companies integrate.

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Adobe has unveiled a new agentic AI platform aimed at marketing and creative teams, as the company moves to extend its existing AI infrastructure into a more comprehensive operating layer for agencies and enterprise brands.

The platform, called Adobe CX Enterprise, builds on the company’s Adobe Experience Platform Agent Orchestrator, which Adobe says now powers more than one trillion digital experiences annually across its enterprise customer base. CX Enterprise adds what Adobe describes as “agent skills” — reusable instructions that can be deployed across different AI agents — and is designed as an open system capable of integrating into any technology stack, not just Adobe’s own.

The platform includes two core intelligence products. Adobe Brand Intelligence is described as a continuous engine for enforcing brand consistency, learning from inputs such as review feedback, rejected creative assets and editorial annotations, then making those insights available to agents working on content production. The second, Adobe Engagement Intelligence System, functions as a decision engine for customer communications — determining the next best offer, message or action for a given consumer based on lifetime value rather than clicks and conversions alone, and optimising toward defined business goals.

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Adobe also introduced a product called CX Enterprise Coworker, which assembles and coordinates specialised AI agents around specific business objectives. In the company’s own example, a marketing team targeting a 3% improvement in cross-sell performance could instruct Coworker to identify the relevant agents and tools, assemble audience segments, creative assets and performance data, draft a campaign plan for human approval, and then execute and monitor the campaign once approved.

Beyond the platform itself, Adobe is formalising partnerships with six of the world’s largest agency holding companies — Dentsu, Havas, Omnicom, Publicis, Stagwell and WPP — to deliver joint solutions in areas including content supply chain management and brand visibility in AI-powered search. The company says 20,000 companies have built operations on its technology.

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A separate set of system integrators — including Accenture, Capgemini, Cognizant, Deloitte Digital, EY, IBM, Infosys, PwC and TCS — are using Adobe’s agentic capabilities to build vertical-specific solutions for their own clients. Expanded integrations with AI platforms from Amazon, Anthropic, Google, IBM, Microsoft and OpenAI are intended to make it easier for brands to connect Adobe workflows with tools from other providers.

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Yuno, the global payments infrastructure company, on Sunday introduced Payments Concierge, an autonomous AI agent designed to monitor, troubleshoot, and optimize a merchant’s payment operations around the clock.

The announcement, made at the HumanX conference, represents a departure from conventional payment dashboards and alert systems. Rather than notifying a payments team that something has gone wrong, Payments Concierge is built to detect problems and act on them in real time — adjusting routing rules, toggling payment providers on or off, and reordering checkout options to favor the method most likely to succeed.

The need for such automation, Yuno says, reflects the compounding costs of inaction. A single card issuer going offline can silently decline thousands of transactions before a team identifies the source. A misconfigured routing rule can quietly erode revenue for weeks. And diagnosing what happened — pulling raw data, running performance analyses, assembling reports — can consume hours of manual work.

Payments Concierge addresses each of those pain points. The tool surfaces interchange and scheme fees at the transaction level, giving merchants a granular view of what each payment actually costs and enabling routing decisions that balance approval rates against expense. Reporting tasks that previously took hours, the company says, can now be completed with a single prompt, whether the output needed is a detailed data breakdown or a board-ready summary.

The agent is accessible through WhatsApp, Telegram, WeChat, and Slack, and all automated actions operate within a merchant’s preconfigured security permissions.

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“Payment operations today are mostly reactive, with teams finding out something broke after the revenue is already lost,” said Juan Pablo Ortega, chief executive and co-founder of Yuno. “Payments Concierge catches issues humans can’t see, optimizes costs humans can’t track, and executes changes in real time.”

Yuno’s platform connects more than 1,000 payment methods and fraud tools through a unified API. Its clients include McDonald’s, Uber, GoFundMe, and Rappi.

Ortega is scheduled to speak at HumanX on Monday, leading a roundtable titled “When AI Becomes the Buyer” and joining a panel on building global financial infrastructure.

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Starling Bank is rolling out an agentic AI financial assistant, which it says is the first of its kind in the United Kingdom, as the challenger bank moves to put automated money management directly in the hands of its nearly five million customers.

The assistant, called Starling Assistant, responds to voice and natural-language prompts and carries out banking tasks on the customer’s behalf — from setting savings goals and organizing bill payments to offering personalized financial insights and general banking guidance.

The practical scope is specific. A customer planning a holiday could ask the assistant to calculate how much they need to save monthly to reach a target amount by a given date and instruct it to set up automatic transfers to a dedicated savings pot. A customer wanting to organize their finances on payday could ask it to create separate budget categories for groceries, bills, travel and dining out, specifying how much to move into each on a set date each month.

The assistant is built on Starling’s proprietary technology platform using Google Gemini and Google Cloud infrastructure.

Harriet Rees, Starling’s group chief information officer, said the launch represented a new chapter for the bank. “It’s time to embrace a new era of banking, one that’s powered by agentic AI,” she said. “We want to encourage our customers to trust that AI can help them with money management, and we’re excited to be pioneering the use of this technology to help people be good with money.”

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Starling has been building toward this capability for some time. It previously launched Spending Intelligence, which allows customers to ask natural language questions about their spending habits, and Scam Intelligence, a tool designed to detect online marketplace fraud.

The Starling announcement lands as AI adoption accelerates across European fintech. Klarna uses generative AI for customer service, while Dutch neobank Bunq launched its own AI assistant in 2024. Danish challenger Lunar has said its AI-powered voice assistant will handle around 75% of customer calls over time. Revolut, meanwhile, is exploring a broader push into the AI agent space, with ambitions to automate functions ranging from customer service to sales.

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Contentsquare, a digital analytics platform, announced Monday a set of new capabilities designed to help brands track and understand customer journeys that now begin inside AI assistants and conversational platforms rather than on conventional websites and mobile apps.

The expansion reflects a structural shift in how consumers discover and interact with brands. Journeys that once started with a search engine or a direct website visit increasingly begin inside tools like ChatGPT, where a customer might ask a question, receive a brand recommendation and click through to a website — a sequence that existing analytics infrastructure was not built to capture end-to-end.

“Brands that want to succeed in this agentic era need visibility into every interaction — from conversations and support tickets to social feedback and AI agent behavior,” said Jonathan Cherki, chief executive and founder of Contentsquare. “Teams can finally connect the dots, prioritize what matters most, and act in real time to improve experiences, retention and growth.”

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A Configurable AI Agent for Analytics

The centerpiece of the announcement is an updated version of Sense Analyst, Contentsquare’s analytics agent, which has been redesigned to be configurable to each organization’s specific goals and priorities. Rather than reporting metrics on demand, Sense Analyst now proactively identifies improvement opportunities and surfaces insights tied to business impact.

The updated agent includes a customizable dashboard — the company calls it a Newsroom — where AI agents analyze experience data around the clock, detecting issues and flagging growth opportunities. Insights can be delivered directly to users’ email inboxes on a scheduled basis, reducing the need for teams to monitor analytics tools continuously.

Tracking What Happens Inside ChatGPT

As brands begin building applications within AI assistants, Contentsquare is now providing analytics for activity within ChatGPT apps — showing how customers discover brands through prompts, how they interact within those experiences, and how their journeys move between AI assistants and conventional websites.

The capability allows brands to answer questions that were previously unanswerable: which prompts generate conversions, whether customers return through AI assistant channels, and how journeys that begin inside a large language model ultimately resolve. Accor, the hospitality group, is among the early adopters.

“Being a first mover on ChatGPT allows us to redefine digital hospitality,” said Yassine Hachem, senior vice president of e-commerce and customer engagement at Accor. “Partnering with Contentsquare ensures we understand these new AI behaviors from day one.”

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LLM Traffic and Conversation Intelligence

Beyond ChatGPT-specific analytics, Contentsquare is also introducing tools to measure LLM-driven traffic more broadly — giving organizations visibility into whether visitors arriving at their websites are human or AI-driven, and how those two types of traffic behave differently in terms of navigation and conversion.

A separate addition, built on Contentsquare’s recent acquisition of Loris, brings conversation intelligence into the platform. The tool captures customer conversations across support tickets, phone calls and in-product chats, enriches them with signals from reviews and social posts, and connects conversational data with digital behavior and business outcomes. The goal is to give brands a unified view of what customers are saying, where they encounter friction and which changes will have the greatest impact on loyalty.

Alexandra Alessi, vice president of brand e-commerce at hair care company Olaplex, said the platform’s AI capabilities had contributed to a 31% improvement in conversion rates and allowed the company to make its redesign process data-driven rather than subjective.

Contentsquare also announced that its experience data is now accessible through the Model Context Protocol, making it available within AI assistants, including Anthropic’s Claude, Cursor and Microsoft Copilot — allowing teams to query analytics data in plain language without switching between systems.

The announcements were made at CX Circle London, the first stop of Contentsquare’s global conference tour.

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