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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Sprinklr is rolling out its Spring ’26 Release — a broad update to its AI-powered customer experience platform that touches service, marketing, insights, and platform governance. The throughline across nearly every new capability is the same: giving enterprise teams more visibility into what their AI is deciding, and more confidence that it is deciding correctly.

For a company that has staked its identity on AI-native customer experience management, the update reflects a maturing conversation in the market. Deploying AI agents is no longer the hard part. Trusting them at scale is.

The Trust Problem in AI-Powered Service

The most significant addition in this release is Autonomous Evaluation, a new capability within Sprinklr Service that provides explainable logs and test-backed validation for AI agents operating autonomously in contact center environments.

The logic is straightforward and overdue. As AI agents take on more customer interactions without human intervention, the organizations deploying them have limited visibility into why a particular decision was made, what went wrong in a failed interaction, or whether agent behavior is drifting from intended parameters. Autonomous Evaluation is designed to make that behavior legible — providing the audit trails and test infrastructure that allow teams to understand, refine, and scale agents with some degree of confidence.

Alongside this, Agent Copilot has been upgraded to operate proactively, surfacing real-time guidance during live customer interactions rather than waiting to be consulted. The focus is on core service metrics: first call resolution and average handle time — two indicators that contact center leaders actually manage to.

Operational improvements round out the service updates, including automatic shift bidding for agent scheduling and unified KPI dashboards that aim to shorten the distance between insight and action.

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Listening With Less Noise

On the insights side, Sprinklr is addressing a problem that has quietly undermined social listening for years: the signal-to-noise ratio.

AI Topics now use generative AI enrichments to filter out irrelevant mentions and surface only the conversations that are genuinely meaningful to a brand. The distinction between a brand being mentioned and a brand being discussed in a way that matters is one most listening tools have struggled to make reliably. The update is an attempt to close that gap.

Customer profiles are also being consolidated across channels, giving teams a single, coherent view of each customer rather than fragmented data spread across disconnected dashboards. Global Web Survey support has been expanded with one-click localization, stronger governance controls, and intelligent sampling — improvements aimed at producing cleaner, more representative feedback at scale.

Action Plans, previously limited to parts of the platform, now extend across the full Insights suite, allowing teams to assign tasks, set owners, and track progress on insights without leaving Sprinklr.

Marketing and Social Updates

For marketing teams, the release adds access to TikTok’s Commercial Music Library, enabling compliant video content creation without the licensing friction that has complicated branded content on the platform. Integration with Canva’s Digital Asset Management system streamlines how creative assets move into Sprinklr workflows while maintaining brand governance.

Performance analytics have also been upgraded. Automated root-cause analysis now flags and explains sudden shifts in campaign results, and a unified dashboard compares pre- and post-boost metrics in a single view. LinkedIn seller profile tracking gives social selling teams visibility into performance data that was previously difficult to surface in one place.

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Platform and Governance

At the infrastructure level, Sprinklr is introducing bulk testing and AI telemetry within AI+ Studio, allowing enterprises to evaluate AI performance across large volumes of interactions rather than sampling spot-checks. Additional updates include unified integration management through the Sprinklr Marketplace, automated data ingestion via CRON-based scheduling, and an updated compliance framework under DRP 2.0.

“As AI Agents resolve more customer issues autonomously, we’re giving teams the transparent, test-backed validation they need to trust and scale them,” said Karthik Suri, Chief Product and Corporate Strategy Officer at Sprinklr. “These advancements help brands turn automation into measurable impact — and ultimately into more seamless, personalized moments of customer delight.”

The Spring ’26 Release is available now.

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The slow death of the cookie has been anticipated for years. The operational reality of advertising without it has arrived faster than most campaigns were built to handle.

MINT Square, the programmatic advertising specialist, is announcing a strategic partnership with Digiseg, a provider of ID-independent audience targeting, designed to give advertisers a scalable alternative to identifier-dependent targeting — one that works even in browsers and environments where traditional signals are unavailable or restricted.

The partnership takes effect in early 2026 and will be available to MINT Square clients across all markets.

The Problem It’s Solving

Addressability across the open web has become increasingly inconsistent. Cookies are blocked or deprecated across major browsers. Personal identifiers are increasingly restricted in regulatory environments. For advertisers trying to plan and execute campaigns with any reliability, the result is a patchwork of signal availability that traditional targeting infrastructure was not designed to navigate.

The MINT Square and Digiseg partnership sidesteps that problem by anchoring audience targeting in household-level characteristics rather than individual identifiers. Segments are built from real-world attributes — income bands, homeownership status, life stage — derived from household data rather than cookies, device IDs, or any form of personal tracking.

Those segments are accessible through Adform and The Trade Desk, integrating directly into existing advertiser workflows without requiring new infrastructure.

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Privacy-Safe by Architecture, Not by Workaround

The distinction Digiseg draws is architectural. Its audience segments are not constructed by anonymizing personal data after the fact — they are built from the ground up without personal identifiers. Digiseg’s Insights Platform extends this further, offering campaign performance analysis and audience understanding without touching personal data at any stage.

For advertisers operating under tightening privacy regulations across multiple jurisdictions, that is a meaningful difference. Compliance is not a retroactive adjustment; it is the baseline condition of how the targeting works.

“ID-independent targeting is now a commercial necessity,” said Christoph Berg, CEO of MINT Square. “This collaboration allows our clients to plan and activate with confidence across all major browsers, while benefiting from lower costs and a simplified route to market.”

Part of a Broader Ecosystem Play

Digiseg joins MINT Square’s newly launched membership-based offering, a tiered access model designed to lower the barriers to entry for advertisers and agencies navigating the programmatic landscape. The structure introduces three membership tiers, each providing access to a suite of targeting, contextual, and data capabilities — with Digiseg’s audiences now included as a core component.

For MINT Square, the partnership is one piece of a larger push to build a comprehensive ecosystem of premium publishers and targeting partners. Additional collaborations are expected to be announced throughout the year.

“MINT Square’s deep knowledge of the programmatic ecosystem and strong activation capabilities across markets make it a natural partner,” said Ulrik Krag Morell, CEO of Digiseg. “Together, we can make it easier for advertisers to adopt ID-independent targeting at scale, without compromising on reach or effectiveness.”

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Customer feedback has a timing problem. By the time a survey reaches someone’s inbox, the experience it’s asking about is already days old — and response rates reflect that. SurveyMonkey is addressing that gap directly with a new capability that automates SMS survey invites triggered by real-time events inside Salesforce.

The feature, now generally available on Salesforce AppExchange, allows customer experience and support teams to send personalized text message survey invites the moment a key event occurs — a support case closes, a transaction completes, a service interaction ends. No manual outreach. No delay.

Why SMS, and Why Now

The shift to mobile as the primary survey channel is not a forecast. It has already happened.

According to SurveyMonkey’s own 2025 data, 58 percent of global survey responses are now captured on mobile devices. Switching from a web link to an SMS invite has been shown to improve survey completion rates by 33 percent. Open rates on text messages consistently outperform email by a significant margin.

For teams whose feedback programs still rely primarily on email, those numbers represent a structural gap in how much signal they are actually capturing — and when.

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What the Integration Does

The SurveyMonkey-Salesforce integration already allowed teams to collect and act on customer feedback within their existing CRM workflows. The SMS capability extends that in four specific ways.

Automated outreach removes the manual step entirely. Teams configure workflows that trigger personalized SMS invites when defined Salesforce events occur, so feedback is requested while the experience is still immediate. Personalization is handled at scale by pulling Salesforce data fields — customer names, case numbers, transaction details — directly into the message text, without manual effort per send.

Responses map automatically back to the appropriate Salesforce objects, keeping customer records current and giving teams the data they need to route, escalate, or resolve issues based on what customers are actually reporting in real time.

“Customer insight is only useful if it’s captured in the moment,” said Cormac Kelly, vice president of customer success at SurveyMonkey. “This shift ensures feedback isn’t just a lagging report, but a real-time signal that allows teams to act while the experience is still top-of-mind.”

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Forty-one percent of Americans are now shopping on TikTok Shop, up sharply from 30 percent a year ago, according to a new survey from ecommerce marketing platform Omnisend. The findings arrive as major brands accelerate their presence on the platform, raising a pointed question for retailers: Is TikTok Shop the next mainstream retail channel?

The data suggests it may already be.

Tariffs Are Not Slowing Chinese Marketplaces Down

Across every Chinese marketplace tracked in the survey, shopping activity increased over the past year — despite an ongoing tariff environment and rising consumer awareness of trade policy.

Temu usage climbed 9 percent, from 53 percent of respondents last year to 58 percent today. Shein saw a 23 percent increase, rising from 40 percent to 49 percent. AliExpress grew 38 percent, from 26 percent to 36 percent.

The growth is notable given that 46 percent of U.S. consumers now say they support tariffs — up 35 percent from last year, when only 34 percent said the same. Consumer sentiment and consumer behavior, it appears, are moving in opposite directions.

The explanation may lie in where price increases are actually landing. Seventy-four percent of respondents said they are spending more online this year than last. But the increases are being felt most acutely on Amazon, where 57 percent of shoppers reported higher prices, and on Walmart, where 48 percent said the same. By contrast, only 12 percent of TikTok Shop users reported price increases, along with 25 percent on Temu, 19 percent on Shein, and 12 percent on AliExpress.

“Price increases are finding their way to U.S. shoppers via stores with traditional business models like Amazon and Walmart,” said Mary Bauer, ecommerce expert at Omnisend. “However, since many Chinese sellers operate in a manufacturer-to-consumer model, even with tariffs, many are able to keep prices competitive. As shoppers seek value, stores like Temu and Shein continue to appeal to U.S. consumers.”

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Social Commerce Is Redrawing the Retail Map

The TikTok Shop numbers reflect something broader than platform preference. They reflect a structural shift in how discovery and purchase are collapsing into a single moment — one that traditional retail formats were not designed to capture.

“The blending of e-commerce and social media continues to redefine what we consider to be a modern storefront,” Bauer said. “Regardless of the channel, the customer journey remains at the forefront, meaning always-on brands have unique opportunities to attract shoppers.”

What Retailers Should Do Now

For brands trying to navigate this shift, Omnisend outlines several strategic adjustments worth considering.

Expanding to TikTok Shop and other Chinese marketplaces is worth evaluating for brands in a position to do so. The ability for a consumer to move from entertainment to purchase in a single scroll represents a meaningful conversion opportunity that traditional channels cannot replicate.

Email marketing remains one of the highest-ROI channels available. One in ten shoppers who click on a marketing email makes a purchase — a conversion rate that holds up against any social platform.

Brands can also bridge the two worlds by converting social-referred web traffic into email subscribers through targeted signup prompts, creating a pipeline from social discovery to owned-channel loyalty.

Finally, in an environment where price sensitivity is elevated, promoting tangible value — free shipping, flexible returns, product quality guarantees — at every stage of the customer journey is no longer optional. It is the baseline expectation.

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Most AI tools charge for access. HubSpot is now charging for outcomes.

Effective April 14, 2026, HubSpot is moving two of its Breeze AI agents to outcome-based pricing — a model that bills customers only when the agent delivers a defined result, not simply when it is used. The change applies to Customer Agent and Prospecting Agent, and it represents one of the more direct acknowledgments yet from a major software vendor that the current model of charging for AI potential has a credibility problem.

What Changes and How It’s Priced

Customer Agent, which HubSpot says resolves 65 percent of customer conversations and has cut resolution time by 39 percent across 8,000 customers, is moving from $1.00 per conversation to $0.50 per resolved conversation. The distinction is meaningful: under the old model, an unresolved conversation costs the same as a resolved one. Under the new model, customers pay only when the agent actually closes the issue.

Prospecting Agent is shifting from a recurring monthly charge per enrolled contact to $1 per qualified lead handed off to a sales team. Again, the trigger is outcome, not activity — customers pay when a prospect is qualified and passed to their team, not when outreach is simply initiated.

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Why HubSpot Believes Its Agents Can Back This Up

Outcome-based pricing is a credible offer only if the product consistently delivers outcomes. HubSpot’s argument is that its agents have a structural advantage over generic AI tools: access to the customer data already stored inside the HubSpot platform. That context — CRM history, conversation records, contact activity — is what generic tools lack and what HubSpot believes makes its agents meaningfully more accurate and effective in practice.

“Businesses are being asked to make big bets on AI right now. Too often, that means paying for potential rather than performance,” said Jon Dick, Chief Customer Officer at HubSpot. “Outcome-based pricing removes that risk. You pay when it works, full stop.”

The Broader Signal

The pricing shift reflects a tension that has been building across the enterprise software market since AI features became standard. Vendors have largely charged for AI access — seats, tokens, usage tiers — regardless of whether those capabilities produced measurable results for customers. For buyers under pressure to justify AI investment, that model is increasingly difficult to defend internally.

HubSpot is not the first company to experiment with outcome-based pricing for AI agents, but the move by a platform of its scale — and the specificity of the metrics it is tying pricing to — makes it worth watching. If the model holds, it puts meaningful pressure on competitors still charging for potential rather than performance.

The new pricing takes effect April 14, 2026.

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Sprinklr, an enterprise customer experience software company, and CreatorIQ, a creator marketing platform, announced Wednesday a partnership that links creator campaign data with corporate social media management systems, allowing brands to measure influencer, paid, and organic social performance from a single dashboard.

The deal addresses a persistent pain point for large marketing organizations: performance data from creator campaigns typically sits in a separate system from paid and owned social media metrics, making it difficult to assess the combined return on investment across channels.

Creator marketing has grown rapidly in recent years. Average brand investment in the category rose 171 percent year over year in 2025, according to figures cited by the companies, and more than half of marketers now use creator-produced content in both paid and organic campaigns.

Under the partnership, CreatorIQ’s proprietary data infrastructure — which the company says processes 123 million creator posts per day — will feed directly into Sprinklr’s social media reporting environment. The integration is designed to let brands evaluate creator performance alongside their broader social and paid media activity and draw clearer connections between creator spending and revenue outcomes.

“Integrating CreatorIQ data directly into Sprinklr gives brands one connected environment for strategy, execution, and measurement across creator, paid, and owned channels,” said Anish Chadda, vice president of product management at Sprinklr.

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Tim Sovay, chief partnerships officer at CreatorIQ, said creator marketing data has historically been siloed from the broader systems enterprises use to manage social media and customer experience. “Together, we’re delivering the unified, enterprise-grade intelligence required for AI-driven marketing and full-funnel growth,” he said.

The integration will also allow brands to boost high-performing creator content directly through Sprinklr’s paid media tools, reducing the need to manually export data between platforms.

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Salesforce on Wednesday unveiled a new set of features for Slackbot, the built-in assistant inside its Slack messaging platform, in a push to make Slack the central interface through which employees interact with the company’s broader suite of enterprise software.

The updates are designed to address a problem that has grown more acute as AI tools have proliferated across corporate technology stacks: intelligence is abundant, but it remains scattered. Each application tends to have its own assistant, each workflow its own interface. Salesforce is betting that consolidating those interactions inside Slack will reduce friction and translate AI capabilities into action more reliably.

Among the most practical additions is automated meeting intelligence. Slackbot can now listen to meetings, generate summaries, and capture action items — then act on them directly, updating records in Salesforce’s customer relationship management software without requiring a user to manually log notes afterward. The feature targets a persistent gap in sales and marketing teams, where insights from conversations frequently go unrecorded or unacted upon.

A second update addresses the problem of lost context when moving between applications. Slackbot can now follow users across tools, carrying knowledge of their ongoing conversations, calendar activity, and deal history with it. The aim is to eliminate the need to restate context each time an employee switches platforms.

Salesforce is also introducing what it calls “AI skills” — reusable, shareable sets of instructions that define how a specific task should be completed. Once a skill is created, it can be distributed across a team and triggered automatically when Slackbot identifies a matching request. Rather than requiring each employee to craft their own prompts, teams can standardize how AI handles recurring tasks such as campaign reporting or internal communications.

Underpinning the updates is a broader architectural ambition. Through integrations and a Model Context Protocol client, Slackbot can route requests to other enterprise tools and agents — from CRM systems to productivity applications — without requiring users to know which underlying system is handling the work. Salesforce is positioning Slack as a consistent interface sitting above a fragmented set of back-end tools.

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The company is also targeting smaller teams with a lightweight CRM feature that tracks customer interactions directly from Slack conversations, automatically updating contacts, deals, and notes. For larger organizations already using Salesforce’s Customer 360 platform, the same capability functions as a conversational layer, allowing employees to update records or trigger workflows without leaving Slack.

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Hightouch, a customer data platform company, announced Thursday that it has built direct data onboarding integrations with The Trade Desk and Yahoo DSP, allowing media networks to list and monetize audiences in self-service ad marketplaces without paying fees to a secondary intermediary.

Under the traditional onboarding model, media networks must route audiences through a third-party marketplace before those audiences reach a demand-side platform, or DSP. That process layers an additional revenue-share fee on top of the DSP’s own charges. By connecting directly to both platforms, Hightouch said the new integrations would roughly halve total marketplace fees for media networks.

Ian Maier, general manager of ad tech at Hightouch, said direct relationships between data providers and DSPs are preferable when the value added by intermediaries is unclear. “The job of monetization is to quickly curate these audiences,” he said. “The CDP platform is where it should happen.”

The integrations are part of Hightouch’s Match Booster product, its data onboarding tool, which the company says can expand audience size and extend reach to additional households with a single click. Audiences are made available within hours of onboarding, the company said, and the system automates consent management to ensure that opt-outs and data deletion requests are honored across all marketplace listings.

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Hightouch also offers audience-building tools that allow non-technical users to construct audience segments from purchase data, machine learning model scores, and consent flags without engineering support. A listing management feature lets users grant or revoke advertiser access, set pricing rates, and manage public or private listings from a single dashboard.

The company has built its platform around the premise that marketing technology stacks do not require separate systems for data storage and activation — and that data onboarding should be no different.

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SAP has agreed to acquire Reltio, a cloud-native data management company, in a move designed to address what the enterprise software giant identifies as the most consequential obstacle to AI adoption at scale: not the quality of AI models, but the quality of the data those models are asked to work with.

The transaction is expected to close in the second or third quarter of 2026, subject to regulatory approvals. Financial terms were not disclosed.

The Problem SAP Is Buying Its Way Out Of

Enterprise AI deployments are failing not because organisations lack access to capable models, but because the data feeding those models is fragmented, inconsistent, and ungoverned. Customer records live in one system, supplier data in another, product information in a third — each maintained independently, each drifting from the others over time. When an AI agent attempts to act on that information, the errors compound.

Reltio was built to solve precisely that problem. Its platform uses AI-based entity resolution to identify and merge related records from different systems and formats into a single, reliable view — what the company calls a trusted system of context. The result is a consistent, unified picture of customers, products, suppliers, locations, and employees that spans both SAP and non-SAP applications.

That last detail matters. Most large enterprises do not run exclusively on SAP. They operate across heterogeneous technology landscapes accumulated over decades of acquisitions, migrations, and technology decisions made by different teams at different times. Reltio was designed for that reality from the outset.

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What SAP Gets

SAP’s strategic ambition is to evolve its Business Data Cloud into a fully interoperable enterprise data platform capable of powering AI agents across the entire organisation. Reltio accelerates that roadmap by adding capabilities SAP would otherwise have had to build — including AI-based entity resolution, an Intelligent Data Graph for creating richer connected context across enterprise data, industry-specific accelerators for life sciences, healthcare, and financial services, and a low-latency delivery architecture built for modern AI workflows.

The acquisition also strengthens SAP’s position in the market for agentic AI infrastructure. SAP’s Joule and Joule Agents — its AI assistant and autonomous agent products — require high-quality, contextually rich data to function reliably. Reltio provides the foundation that makes that reliability achievable at enterprise scale.

“Enterprise AI needs trusted context that is open and interoperable across the heterogeneous IT landscapes our customers run,” said Manish Sood, Reltio’s founder. “This combination accelerates our ability to deliver Reltio as the system of context across SAP and non-SAP environments.”

What Changes — and What Does Not

SAP has been explicit that Reltio will remain available as a standalone offering following the close of the transaction, with a flexible commercial model allowing customers to purchase it independently or alongside other SAP products. That commitment is not incidental. Many of Reltio’s existing customers are not primarily SAP shops, and any signal that the product would be folded exclusively into the SAP ecosystem would risk disrupting a customer base that has relied on Reltio precisely because of its vendor-agnostic architecture.

For Reltio’s partner ecosystem — which has played a significant role in deploying the platform across complex enterprise environments — SAP has indicated that those relationships remain valued and intact.

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The Larger Stakes

The acquisition is a concrete expression of a thesis that is gaining traction across the enterprise technology industry: that the competitive battleground for AI is shifting from model capability to data infrastructure. The organisations that can unify, cleanse, and govern their data at scale will extract disproportionate value from AI. Those who cannot will find that better models do not solve a fundamentally data quality problem.

SAP, with more than 400 million users across its customer base, is positioning itself as the company that solves that problem for the enterprise—and paying for Reltio’s 15 years of focused work to get there faster.

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