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\r\n\r\n\r\nThe post Anthropic Turns Claude Into a Front Door for Daily Apps appeared first on PYMNTS.com.
\n", "content_text": "Anthropic introduced consumer app connectors for Claude, letting users link the artificial intelligence (AI) assistant directly to services they use every day.\r\n\t\r\n\t\t\r\n\t\r\n\r\n\r\n\t\nThe integrations include Spotify, Uber, Uber Eats, Instacart, Intuit TurboTax, Intuit Credit Karma, TripAdvisor, Booking.com, AllTrails, Audible, Resy, StubHub, Taskrabbit, Thumbtack and Viator. Since launching in July, the Claude connector directory has grown to over 200 integrations, with users frequently connecting multiple apps and using them together inside a single conversation, according to Anthropic.\nThe move brings Claude into direct competition with OpenAI and Google. As CNET reported, both Gemini and ChatGPT already allow third-party app connections, with ChatGPT linking to tools like Canva and Zillow. Historically, Claude\u2019s connectors have been work-focused. This launch is geared toward daily consumer use.\nThe architecture of Claude\u2019s connector system differs from how competitors have approached third-party integrations. Gemini connects primarily to Google\u2019s own app ecosystem. ChatGPT relies on users selecting tools manually.\nIn contrast, Claude now suggests the right app based on what the user is doing inside the conversation, whether finding a reservation, adding to a grocery cart or identifying a flight, working from the user\u2019s stated preferences and conversational context, according to Anthropic. The user doesn\u2019t browse or select. When more than one connected app could help, Claude shows both and lets the user choose.\nClaude is ad-free and plans to stay that way. There are no paid placements or sponsored answers in conversations, and when two connectors could help, both are shown ranked by usefulness to the user, the company said. Before completing any booking or purchase, Claude is designed to check with the user first.\nThat confirmation-before-action design matters for enterprise and consumer trust alike. The model recommends. The user decides. The app executes.\nAI as a Transaction and Commerce Layer\nThe connector model turns Claude into an orchestration layer above individual apps. A conversation about weekend plans can produce a hiking recommendation from AllTrails, a restaurant booking from Resy, a grocery order from Instacart, and a ride from Uber without the user switching between apps. Each step represents a potential transaction.\nThe grocery use case illustrates the complexity that makes this harder than it appears. Instacart\u2019s CTO Anirban Kundu described the challenge in a company post published alongside the launch. Grocery involves tens of thousands of SKUs per store, inventory availability that changes by the minute, and deeply personal household preferences. Most AI grocery experiences fail because they surface plausible-sounding results that don\u2019t reflect what\u2019s actually in stock, what things cost at a specific store, or what a household actually buys.\nWhat makes Instacart\u2019s integration viable inside Claude is its underlying infrastructure: data pipelines into more than 2,200 retail banners, a product catalog with more than 2 billion items, and a personalization engine called Smart Shop that tracks how preferences shift by season, occasion and household. When a user builds a grocery cart inside Claude, it connects to real inventory from nearby stores. Any cart built in the conversation syncs automatically to the user\u2019s Instacart account.\nCompetitive Landscape and Platform Implications\nThe connector expansion puts Anthropic on the same consumer terrain as OpenAI and Google at a moment when all three are competing to become the default layer for everyday decisions. The execution model is where Anthropic is making its bet. Suggestion-driven discovery inside a conversation is a different distribution model than a tool store where users choose integrations manually.\nThe more apps a user connects, the more Claude can do. Claude suggests relevant connectors as the user works, which can be installed with a click on desktop or a few taps on mobile, according to Anthropic. Once connected, a service is available in every conversation.\nConnectors are available across all Claude plans. Mobile access is currently in beta. Anthropic said it will continue expanding the directory and invited developers to submit their products for inclusion.\nFor all PYMNTS AI and digital transformation coverage, subscribe to the daily\u00a0AI and Digital Transformation Newsletters.\n\r\n\r\nThe post Anthropic Turns Claude Into a Front Door for Daily Apps appeared first on PYMNTS.com.", "date_published": "2026-04-24T12:57:05-04:00", "date_modified": "2026-04-24T12:57:05-04:00", "authors": [ { "name": "PYMNTS", "url": "https://www.pymnts.com/author/pymnts/", "avatar": "https://secure.gravatar.com/avatar/679fcf5c2ed5358e99e8e23b22e3b5d761e37bdb76fa7b0e13d8ecd9ff01bf88?s=512&d=blank&r=g" } ], "author": { "name": "PYMNTS", "url": "https://www.pymnts.com/author/pymnts/", "avatar": "https://secure.gravatar.com/avatar/679fcf5c2ed5358e99e8e23b22e3b5d761e37bdb76fa7b0e13d8ecd9ff01bf88?s=512&d=blank&r=g" }, "image": "https://www.pymnts.com/wp-content/uploads/2026/04/Claude-Anthropic-AI-assistants-apps.jpeg", "tags": [ "AI", "Anthropic", "Claude", "Connected Economy", "digital transformation", "News", "PYMNTS News", "Artificial Intelligence" ] }, { "id": "https://www.pymnts.com/?p=3627938", "url": "https://www.pymnts.com/news/artificial-intelligence/2026/how-ai-is-rewriting-credit-decisioning-in-real-time/", "title": "How AI Is Rewriting Credit Decisioning in Real Time", "content_html": "For decades, credit and payment decisions have relied on static scorecards and rigid \u201cif-then\u201d rules built for a slower payments environment. That model is now under strain. As transactions move across digital channels in real time, issuers must evaluate risk, intent and context instantly\u2014not after the fact.
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\r\n\r\n\r\nThe post How AI Is Rewriting Credit Decisioning in Real Time appeared first on PYMNTS.com.
\n", "content_text": "For decades, credit and payment decisions have relied on static scorecards and rigid \u201cif-then\u201d rules built for a slower payments environment. That model is now under strain. As transactions move across digital channels in real time, issuers must evaluate risk, intent and context instantly\u2014not after the fact.\r\n\t\r\n\t\t\r\n\t\r\n\r\n\r\n\t\nThis edition of a new series, \u201cThe ABCs of AI Credit: A Playbook for Issuers,\u201d shows that AI agents are emerging as the operating layer for this shift. Rather than acting as gatekeepers, these agents function as a cognitive layer embedded directly into payment flows, evaluating transactions in milliseconds using behavioral signals and real-time data. This enables more adaptive, context-aware transaction intelligence.\nThe impact on issuer performance is immediate. Traditional fraud controls often block legitimate transactions that fall outside predefined patterns, leading to false declines and lost revenue. AI agents analyze a broader set of signals to distinguish genuine behavioral changes from high-risk activity, improving authorization rates while keeping fraud in check.\nJust as important, they introduce flexibility into transaction logic. Instead of a binary \u201cyes\u201d or \u201cno,\u201d issuers can trigger real-time alerts, adjust limits dynamically or step up verification when needed\u2014balancing security with a smoother customer experience.\nThis shift also depends on infrastructure. Application programming interface (API)-first, real-time processing environments provide the foundation for activating intelligence at the point of transaction. Without that layer, AI-driven intelligence cannot operate at scale.\nUltimately, the playbook reframes how credit operates in live payment environments. Decisions no longer act as isolated checks\u2014they now determine whether customers can access and use their available funds in the moment. In an always-on economy, performance is shaped continuously, one transaction at a time.\n\n\n \n \n \n \n \n \n \n Download the Playbook\n \n The ABCs of AI Credit: A Playbook for Issuers\n \n \n \n \n \n \n \n \n \n \n \n \n\nAbout \u201cThe ABCs of AI Credit: A Playbook for Issuers\u201d\nThe inaugural edition of the \u201cABCs of AI Credit\u201d Playbook, a PYMNTS Intelligence collaboration with Thredd, provides issuers with a practical framework for deploying AI agents in transaction flows, outlining how to move from static rules to real-time transaction intelligence while improving approvals, reducing friction and protecting customer liquidity.\n\r\n\r\nThe post How AI Is Rewriting Credit Decisioning in Real Time appeared first on PYMNTS.com.", "date_published": "2026-04-13T04:01:11-04:00", "date_modified": "2026-04-12T21:33:27-04:00", "authors": [ { "name": "PYMNTS", "url": "https://www.pymnts.com/author/pymnts/", "avatar": "https://secure.gravatar.com/avatar/679fcf5c2ed5358e99e8e23b22e3b5d761e37bdb76fa7b0e13d8ecd9ff01bf88?s=512&d=blank&r=g" } ], "author": { "name": "PYMNTS", "url": "https://www.pymnts.com/author/pymnts/", "avatar": "https://secure.gravatar.com/avatar/679fcf5c2ed5358e99e8e23b22e3b5d761e37bdb76fa7b0e13d8ecd9ff01bf88?s=512&d=blank&r=g" }, "image": "https://www.pymnts.com/wp-content/uploads/2026/04/AI-Agents-Move-Credit-From-Static-Rules-to-Real-Time-Transaction-Intelligence-hero.jpg", "tags": [ "Agentic payments", "AI", "AI Agents", "APIs", "Artificial Intelligence", "cross-border payments", "Featured News", "Featurespace", "FinTech", "News", "Payments Intelligence", "PYMNTS Intelligence", "PYMNTS News", "PYMNTS Study", "Thredd" ] }, { "id": "https://www.pymnts.com/?p=3629175", "url": "https://www.pymnts.com/news/artificial-intelligence/2026/in-payments-the-ai-race-is-also-a-governance-test/", "title": "In Payments, the AI Race Is Also a Governance Test", "content_html": "
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\r\n\r\n\r\n![\"PYMNTS](\"https://www.pymnts.com/wp-content/uploads/2026/04/2026-Q1-eBook-DL-Button.jpg\")
The post In Payments, the AI Race Is Also a Governance Test appeared first on PYMNTS.com.
\n", "content_text": " \r\n\t\r\n\t\t\r\n\t\r\n\r\n\r\n\t\n\nArtificial intelligence is moving deeper into payments, and at an unusual pace. It\u2019s helping companies spot fraud, improve approvals, personalize offers, support compliance, manage risk and shape customer experiences in real time. That much is clear.\nWhat\u2019s less settled is the part that now matters most: Who is governing these systems, how that governance works in practice and what happens when AI begins influencing decisions faster than an organization can explain them.\nAcross the essays in this eBook, one message comes through with force. In payments, AI is no longer an experiment. Governance is the differentiator.\nThat\u2019s because the payments environment is one of the hardest places to get AI wrong. These systems do not live in a lab. They operate inside live transaction flows, fraud programs, onboarding journeys, identity checks, credit decisions and customer service interactions.\nA weak model can create problems. A weak governance structure can multiply them. At scale.\nThe risk is not only that AI makes a flawed call. It\u2019s that nobody is fully accountable for the outcome, nobody can trace the logic and nobody spots the drift until customers, regulators or partners do first.\nThe executives in this collection return again and again to a handful of hard truths. Governance tends to break down in the gaps between teams. Product may own the feature, engineering may own the model, compliance may own the policy and operations may own the day-to-day consequences, but the end-to-end accountability is often blurred.\nAt the same time, many AI systems depend on third-party models, vendors, data providers and external platforms. This means companies are being asked to govern not only what they build, but also what they rent, ingest and rely on. In that environment, governance can\u2019t be treated as a meeting, a checklist or a slide deck. It has to become an operating discipline.\nAnother theme runs through these pages: Speed is seductive, but speed without structure is expensive. Payments companies are under pressure to automate more, move faster and show returns quickly. Yet several contributors make the same point from different angles. The real work is not simply deploying AI. It\u2019s building the data foundations, oversight mechanisms, fallback plans, audit trails and human review points that let a company move quickly without losing control. In other words, the organizations that benefit most from AI may not be the ones that rush first, but the ones that prepare best.\nReaders should come away from this eBook with more than a warning. They should come away with a playbook. These essays offer a practical look at where governance breaks down, what strong organizations are doing earlier, which questions boards and CEOs should be asking now and how leading payments executives are thinking about explainability, accountability and trust in an AI-driven market. For leaders across banking, payments and FinTech, that has real value. It can help sharpen internal conversations, expose blind spots in current governance models, and frame AI not as a race to adopt the newest tool, but as a long-term test of institutional discipline.\nAI may be powering the next era of payments. But governance will decide which companies can scale it with confidence, which ones can defend it under scrutiny, and which ones can turn automation into a durable advantage. That is the conversation this eBook begins.\n\r\n\r\nThe post In Payments, the AI Race Is Also a Governance Test appeared first on PYMNTS.com.", "date_published": "2026-04-07T04:00:34-04:00", "date_modified": "2026-04-15T09:33:39-04:00", "authors": [ { "name": "PYMNTS", "url": "https://www.pymnts.com/author/pymnts/", "avatar": "https://secure.gravatar.com/avatar/679fcf5c2ed5358e99e8e23b22e3b5d761e37bdb76fa7b0e13d8ecd9ff01bf88?s=512&d=blank&r=g" } ], "author": { "name": "PYMNTS", "url": "https://www.pymnts.com/author/pymnts/", "avatar": "https://secure.gravatar.com/avatar/679fcf5c2ed5358e99e8e23b22e3b5d761e37bdb76fa7b0e13d8ecd9ff01bf88?s=512&d=blank&r=g" }, "image": "https://www.pymnts.com/wp-content/uploads/2026/04/In-Payments-the-AI-Race-Is-Also-a-Governance-Test-hero.jpg", "tags": [ "Agentic AI", "AI", "ebook", "governance", "Main Feature", "News", "PYMNTS News", "Artificial Intelligence" ] }, { "id": "https://www.pymnts.com/?p=3610424", "url": "https://www.pymnts.com/news/artificial-intelligence/2026/microsoft-expands-copilot-with-task-execution-feature/", "title": "Microsoft Expands Copilot With Task Execution Feature", "content_html": "Microsoft introduced Copilot Cowork on Monday (March 30), a new feature that allows its artificial intelligence (AI) assistant to carry out tasks across Microsoft 365 applications.
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\r\n\r\n\r\nThe post Microsoft Expands Copilot With Task Execution Feature appeared first on PYMNTS.com.
\n", "content_text": "Microsoft introduced Copilot Cowork on Monday (March 30), a new feature that allows its artificial intelligence (AI) assistant to carry out tasks across Microsoft 365 applications.\r\n\t\r\n\t\t\r\n\t\r\n\r\n\r\n\t\nAccording to the company\u2019s announcement, the update is being made available to a limited group of customers through its Frontier program, which gives early access to new AI tools.\nCopilot Cowork is designed to handle work that takes more than a single prompt. Instead of asking the assistant for one answer at a time, users can assign a task and have Copilot complete it across apps like Outlook, Teams and Excel. The system can gather information from emails, documents and chats, organize it and produce a finished output.\nMicrosoft said the feature is meant to help users manage ongoing work by keeping track of context across tasks. It allows Copilot to continue working on assignments over time, rather than treating each request as a separate interaction.\nAlongside Cowork, Microsoft introduced updates that allow Copilot to use more than one AI model when generating responses. With \u201cCritique,\u201d the program can produce an answer using one model and then review it using another before showing it to the user.\nThe goal is to improve accuracy and reduce errors by adding a second layer of review. Microsoft also introduced a feature called \u201cCouncil,\u201d which lets users compare responses from different models side by side.\nThese additions are intended to give users more visibility into how answers are generated and to provide a way to check results before using them.\nThe Copilot Cowork feature is currently limited to customers in Microsoft\u2019s Frontier program. This program allows select users to test new AI tools and provide feedback before they are released more widely.\nMicrosoft said it plans to expand access based on user feedback and testing results. The company has been steadily adding new features to Copilot as it looks to increase usage of its AI tools across its workplace software. The release follows Anthropic\u2019s introduction of its own \u201cCowork\u201d capability, which allows its Claude AI system to act as a collaborator across tasks, as PYMNTS reported in January.\nFor all PYMNTS digital transformation coverage, subscribe to the daily\u00a0Digital Transformation\u00a0Newsletter.\n\r\n\r\nThe post Microsoft Expands Copilot With Task Execution Feature appeared first on PYMNTS.com.", "date_published": "2026-03-30T16:14:23-04:00", "date_modified": "2026-03-30T16:14:23-04:00", "authors": [ { "name": "PYMNTS", "url": "https://www.pymnts.com/author/pymnts/", "avatar": "https://secure.gravatar.com/avatar/679fcf5c2ed5358e99e8e23b22e3b5d761e37bdb76fa7b0e13d8ecd9ff01bf88?s=512&d=blank&r=g" } ], "author": { "name": "PYMNTS", "url": "https://www.pymnts.com/author/pymnts/", "avatar": "https://secure.gravatar.com/avatar/679fcf5c2ed5358e99e8e23b22e3b5d761e37bdb76fa7b0e13d8ecd9ff01bf88?s=512&d=blank&r=g" }, "image": "https://www.pymnts.com/wp-content/uploads/2025/11/Microsoft-Copilot-4.jpg", "tags": [ "AI", "AI Assistants", "Copilot", "Microsoft", "News", "PYMNTS News", "What's Hot", "Artificial Intelligence" ] }, { "id": "https://www.pymnts.com/?p=3609227", "url": "https://www.pymnts.com/news/artificial-intelligence/2026/how-ai-killed-information-asymmetry-in-b2b-procurement/", "title": "How AI Killed Information Asymmetry in B2B Procurement", "content_html": "Artificial intelligence (AI) is transforming sales across the B2B ecosystem, but not the way companies think.
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\r\n\r\n\r\nThe post How AI Killed Information Asymmetry in B2B Procurement appeared first on PYMNTS.com.
\n", "content_text": "Artificial intelligence (AI) is transforming sales across the B2B ecosystem, but not the way companies think.\r\n\t\r\n\t\t\r\n\t\r\n\r\n\r\n\t\nWhile CEOs talk about AI\u2019s impact across automated outreach, predictive pipelines and agents that can draft proposals in seconds, what is vanishing isn\u2019t friction but the B2B sales process itself.\nProcurement leaders inside large organizations are faced with a declining volume of formal request for proposals (RFPs) and vendor calls that are happening later in the process, if at all. It\u2019s not that decisions are speeding up for procurement leaders, but the purchasing decision itself is frequently already made before the sale details ever reach their desk.\nProcurement teams equipped with AI-driven tools can now independently conduct market scans, compare vendors, evaluate pricing benchmarks and even simulate negotiation scenarios. What once required weeks of vendor engagement can now be executed in hours without a single sales call.\nThis transition from seller-led narratives to model-led decisions may fundamentally alter the role of sales.\nRead also:\u00a0It\u2019s Contract Renewal Season, Are Your B2B Payment Strategies Ready?\nThe Rise of Zero-Touch B2B Buying\nFor decades, procurement functioned as the institutional backbone of B2B commerce. It imposed order on a messy landscape of vendors, claims and pricing models by creating checkpoints that ensured decisions were deliberate and defensible. Just as importantly, it created access.\nSales teams knew where to enter the organization, who controlled budgets, and how decisions would unfold. Procurement was not just a control mechanism; it was a map.\nThat map is now out of date. The conditions that made procurement and sales checkpoints indispensable, like limited information, opaque pricing and high uncertainty, have been increasingly eroded by digital tools.\nPYMNTS Intelligence\u00a0data\u00a0in the report, \u201cThe Investment Impact of GenAI Operating Standards on Enterprise Adoption,\u201d a\u00a0PYMNTS Intelligence\u00a0study produced in collaboration with\u00a0Coupa, shows\u00a0that 75% of companies are now considering\u00a0using\u00a0AI in procurement.\nIn this emerging procurement landscape, access is determined less by relationships and more by data compatibility and system integration. Procurement AI relies on structured, reliable and comparable data. Vendors that provide clear pricing models, standardized specifications, performance metrics and interoperable data formats are far more likely to be included in automated evaluations.\n\u201cThe\u00a0friction points\u00a0tend to be in the operational environment,\u201d\u00a0Rene Stynen, senior vice president, EMEA, B2B Payments at\u00a0Boost Payment Solutions, told PYMNTS. \u201cSupplier enablement, onboarding complexity and the amount of data gathering required. Those things can become quite complex.\u201d\n\u201cThere are expectations both on buyer sides and supplier sides for things to become a little bit more digital and automated,\u201d Stynen added.\nSee also: Agentic Commerce Finds Traction in B2B Payments\u00a0\nThe End of Information Asymmetry\nA central pillar of traditional B2B sales has been information asymmetry. Sellers often possessed deeper knowledge about their products, pricing structures and market alternatives than buyers, enabling them to guide, and sometimes control, the decision-making process.\nAI effectively eliminates this asymmetry. Procurement teams now have access to aggregated market intelligence, real-time pricing benchmarks, peer reviews and predictive analytics. They can test assumptions, validate claims and explore alternatives without relying on vendor input.\nAs a result, the informational advantage that once justified extensive sales engagement has eroded. Vendors are no longer primary sources of insight; they are data points within a broader analytical framework. Tools that aggregate vendor performance data, simulate ROI scenarios and flag implementation risks are becoming embedded in everyday workflows. Instead of asking vendors for information, buyers are querying systems that already contain it.\nAnd as routine decisions become increasingly automated or self-directed, the role of sales may shift toward exceptions\u2014complex deployments, cross-functional alignment and bespoke solutions that cannot be easily standardized. The center of gravity moves from selling to enabling.\nAdditionally, on the buyer side, by the time procurement is involved, its role is often reduced to validation: checking compliance, finalizing contracts and processing payment.\nStill, AI in procurement is in its early innings, and there remains a lot of time on the game clock.\n\u201cIf you\u2019re\u00a0going to experiment\u00a0with agentic AI or any type of AI solutions, you want to focus on two things. One is the area where you\u2019re most likely to have success. And two, is there going to be a good return on that investment?\u201d\u00a0WEX Chief Digital Officer\u00a0Karen Stroup told PYMNTS in an earlier interview.\nFor all PYMNTS AI and B2B coverage, subscribe to the daily AI and B2B Newsletters.\n\r\n\r\nThe post How AI Killed Information Asymmetry in B2B Procurement appeared first on PYMNTS.com.", "date_published": "2026-03-30T11:33:26-04:00", "date_modified": "2026-03-30T11:33:26-04:00", "authors": [ { "name": "PYMNTS", "url": "https://www.pymnts.com/author/pymnts/", "avatar": "https://secure.gravatar.com/avatar/679fcf5c2ed5358e99e8e23b22e3b5d761e37bdb76fa7b0e13d8ecd9ff01bf88?s=512&d=blank&r=g" } ], "author": { "name": "PYMNTS", "url": "https://www.pymnts.com/author/pymnts/", "avatar": "https://secure.gravatar.com/avatar/679fcf5c2ed5358e99e8e23b22e3b5d761e37bdb76fa7b0e13d8ecd9ff01bf88?s=512&d=blank&r=g" }, "image": "https://www.pymnts.com/wp-content/uploads/2026/03/b2b-procurement-AI.jpeg", "tags": [ "AI", "B2B", "B2B Payments", "commercial payments", "digital transformation", "News", "procurement", "PYMNTS News", "Artificial Intelligence" ] }, { "id": "https://www.pymnts.com/?p=3584904", "url": "https://www.pymnts.com/news/artificial-intelligence/2026/80percent-of-acquirers-say-they-are-ready-for-agentic-commerce-but-merchants-lag/", "title": "Acquirers Say Risk and Readiness Are Slowing Agentic Commerce", "content_html": "\u201cHow Acquirers Prepare for Agentic Commerce\u201d examines how acquirers are preparing for the next phase of digital commerce as AI-powered agents take on a larger role in shopping and payments. Findings show that many industry players believe the core payments infrastructure is already strong enough to support agent-led transactions. At the same time, infrastructure readiness does not automatically mean the market is ready to move at full speed.
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\r\n\r\n\r\nThe post Acquirers Say Risk and Readiness Are Slowing Agentic Commerce appeared first on PYMNTS.com.
\n", "content_text": "\u201cHow Acquirers Prepare for Agentic Commerce\u201d examines how acquirers are preparing for the next phase of digital commerce as AI-powered agents take on a larger role in shopping and payments. Findings show that many industry players believe the core payments infrastructure is already strong enough to support agent-led transactions. At the same time, infrastructure readiness does not automatically mean the market is ready to move at full speed.\r\n\t\r\n\t\t\r\n\t\r\n\r\n\r\n\t\nA central finding is that progress now depends on solving practical problems that sit between technical capability and real-world adoption. Acquirers say merchants still face real obstacles, including integration costs, older systems and the work required to connect new tools to existing operations. The report also finds that trust remains a critical issue. As commerce becomes more automated, acquirers see fraud controls, identity verification and clear rules around responsibility as essential to wider adoption. Their message: While the opportunity is significant, scaling agentic commerce safely will require the payments ecosystem to align around standards, governance and risk management.\n\n\n \n \n \n \n \n \n \n Download the Report\n \n How Acquirers Prepare for Agentic Commerce\n \n \n \n \n \n \n \n \n \n \n \n \n\nIn \u201cHow Acquirers Prepare for Agentic Commerce,\u201d learn how:\n\nAcquirers view the current payment infrastructure as a starting point for agentic commerce. The study shows that many acquirers believe existing rails can support AI-enabled transactions. What comes next is the work of adapting that foundation to handle new forms of authorization, oversight and control.\nMerchant-side barriers could slow the market even as interest grows. Many acquirers say the challenge is no longer just about consumer demand or technical possibility. Merchants also need simpler deployment paths, lower implementation burdens and stronger support for connecting new capabilities to existing systems.\nTrust and accountability will shape how quickly agent-led payments scale. Acquirers see secure credentials, identity checks and fraud prevention as core requirements for broader agentic commerce adoption. They also make clear that clearer rules around liability and governance will help determine how confidently businesses move forward.\n\nAbout the Report\n\u201cHow Acquirers Prepare for Agentic Commerce,\u201d a PYMNTS Intelligence report in collaboration with Visa Acceptance Solutions, examines the critical factors that drive agentic commerce readiness, drawing on insights from a survey of 75 acquirers across Brazil, the UAE, and the U.S.\nComprising information collected from Jan. 16, 2026, to Jan. 30, 2026, the report provides a roadmap for acquirers (and ecosystem partners) seeking to enhance customer relationships and gain a competitive edge in the agentic commerce arena.\nThis research was independently designed, fielded, analyzed and written by PYMNTS Intelligence. Research partners provided funding support but exercised no control over methodology, data collection, findings or conclusions.\n\r\n\r\nThe post Acquirers Say Risk and Readiness Are Slowing Agentic Commerce appeared first on PYMNTS.com.", "date_published": "2026-03-24T04:00:35-04:00", "date_modified": "2026-03-24T18:44:57-04:00", "authors": [ { "name": "PYMNTS", "url": "https://www.pymnts.com/author/pymnts/", "avatar": "https://secure.gravatar.com/avatar/679fcf5c2ed5358e99e8e23b22e3b5d761e37bdb76fa7b0e13d8ecd9ff01bf88?s=512&d=blank&r=g" } ], "author": { "name": "PYMNTS", "url": "https://www.pymnts.com/author/pymnts/", "avatar": "https://secure.gravatar.com/avatar/679fcf5c2ed5358e99e8e23b22e3b5d761e37bdb76fa7b0e13d8ecd9ff01bf88?s=512&d=blank&r=g" }, "image": "https://www.pymnts.com/wp-content/uploads/2026/03/80-Percent-of-Acquirers-Say-They-Are-Ready-for-Agentic-Commerce-but-Merchants-Lag-hero.jpg", "tags": [ "acquirers", "agentic commerce", "featured insights", "Fraud Prevention", "identity verification", "Main Feature", "News", "omnichannel commerce", "Payments Intelligence", "PYMNTS Intelligence", "PYMNTS News", "PYMNTS Study", "Visa", "Visa Acceptance Solutions", "Artificial Intelligence" ] }, { "id": "https://www.pymnts.com/?p=3580191", "url": "https://www.pymnts.com/news/artificial-intelligence/2026/nvidia-ceo-jensen-huang-predicts-ai-tokens-will-become-a-standard-job-perk/", "title": "Nvidia\u00a0CEO Jensen Huang Predicts AI Tokens Will Become a Standard Job Perk", "content_html": "Nvidia\u00a0CEO\u00a0Jensen Huang\u2019s suggestion earlier this week that his company may pay engineers artificial intelligence (AI) tokens in addition to their base salary, is the latest example of his vision in which AI agents will play a key role in the workplace, CNBC reported\u00a0Friday (March 20).
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\r\n\r\n\r\nThe post Nvidia\u00a0CEO Jensen Huang Predicts AI Tokens Will Become a Standard Job Perk appeared first on PYMNTS.com.
\n", "content_text": "Nvidia\u00a0CEO\u00a0Jensen Huang\u2019s suggestion earlier this week that his company may pay engineers artificial intelligence (AI) tokens in addition to their base salary, is the latest example of his vision in which AI agents will play a key role in the workplace, CNBC reported\u00a0Friday (March 20).\r\n\t\r\n\t\t\r\n\t\r\n\r\n\r\n\t\nHuang said Monday (March 16) during his keynote address at the\u00a0Nvidia GTC AI Conference & Expo\u00a0that tokens are becoming a recruiting tool in Silicon Valley, according to the report.\u00a0Tokens\u00a0are units of data used by AI systems, the report said.\n\u201cI could totally imagine in the future every single engineer in our company will need an annual token budget,\u201d Huang said during his\u00a0keynote. \u201cThey\u2019re going to make a few hundred thousand dollars a year, their base pay, I\u2019m going to give them probably half of that on top of it as tokens so that they could be amplified 10x. Of course we would. It is now one of the recruiting tools in Silicon Valley \u2014 \u2018How many tokens comes along with my job?\u2019\u201d\nHuang told CNBC in February that a growing number of AI agents will work alongside Nvidia employees.\n\u201cI have 42,000 biological employees, and I\u2019m going to have hundreds of thousands of digital employees,\u201d he said, per the report.\nHuang also told CNBC in February that AI agents will have a beneficial impact on the software industry. Rather than reducing demand for the industry\u2019s products, AI agents will become its customers as they use programs,\u00a0tools\u00a0and computing resources, he said.\n\u201cThe number of C-compilers that we use, the number of Python programs that we have, the number of instances,\u00a0are\u00a0growing very, very fast \u2014 because the number of agents we have that use these tools\u00a0are\u00a0going up,\u201d Huang said, per the report.\nPYMNTS reported Thursday (March 19) that Huang said during his keynote address that companies will shift from\u00a0software\u00a0that enables employees to do work, to software that does the work itself, autonomously, through AI agents executing tasks without continuous human input.\nNvidia said in reports released earlier this month that AI is delivering measurable financial gains for businesses. Eighty-eight percent of organizations say AI has increased their annual revenue, and 87% say it has reduced their costs. In terms of\u00a0AI adoption, 64% of companies are actively using AI, while 28% are evaluating potential deployments.\n\r\n\r\nThe post Nvidia\u00a0CEO Jensen Huang Predicts AI Tokens Will Become a Standard Job Perk appeared first on PYMNTS.com.", "date_published": "2026-03-20T12:36:36-04:00", "date_modified": "2026-03-20T12:36:36-04:00", "authors": [ { "name": "PYMNTS", "url": "https://www.pymnts.com/author/pymnts/", "avatar": "https://secure.gravatar.com/avatar/679fcf5c2ed5358e99e8e23b22e3b5d761e37bdb76fa7b0e13d8ecd9ff01bf88?s=512&d=blank&r=g" } ], "author": { "name": "PYMNTS", "url": "https://www.pymnts.com/author/pymnts/", "avatar": "https://secure.gravatar.com/avatar/679fcf5c2ed5358e99e8e23b22e3b5d761e37bdb76fa7b0e13d8ecd9ff01bf88?s=512&d=blank&r=g" }, "image": "https://www.pymnts.com/wp-content/uploads/2024/11/Nvidia-Jensen-Huang-1.jpg", "tags": [ "AI", "digital transformation", "Jensen Huang", "News", "NVIDIA", "PYMNTS News", "What's Hot", "Artificial Intelligence" ] }, { "id": "https://www.pymnts.com/?p=3561379", "url": "https://www.pymnts.com/news/artificial-intelligence/2026/perplexity-computer-enterprise-completed-3-years-work-4-weeks/", "title": "Perplexity\u2019s Computer for Enterprise Completed 3.25 Years of Work in Four Weeks", "content_html": "Artificial intelligence companies are beginning to rethink what a computer actually is, and Perplexity is pushing that idea further with a new platform designed to let AI agents operate across a user\u2019s digital environment.
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\r\n\r\n\r\nThe post Perplexity\u2019s Computer for Enterprise Completed 3.25 Years of Work in Four Weeks appeared first on PYMNTS.com.
\n", "content_text": "Artificial intelligence companies are beginning to rethink what a computer actually is, and Perplexity is pushing that idea further with a new platform designed to let AI agents operate across a user\u2019s digital environment.\r\n\t\r\n\t\t\r\n\t\r\n\r\n\r\n\t\nThe company introduced Wednesday (March 11) Personal Computer, a system designed to allow AI agents to interact with files, applications and online services rather than simply responding to prompts in a chat interface. The system runs on Apple\u2019s Mac mini and can operate around the clock while connecting to a user\u2019s local applications and Perplexity\u2019s servers.\nPerplexity described the platform as a kind of digital proxy that works continuously on a user\u2019s behalf. Instead of waiting for instructions each time a task appears, the system can coordinate tools, files and workflows across devices while carrying out tasks in the background.\nThe launch expands the power of Perplexity Computer, which debuted last month, \u201cacross personal workflows, enterprise software, developer platforms and finance,\u201d Perplexity said in a statement. \u201cThe throughline is the same in each case: a system that can understand the goal, gather the right context, use the right tools, and carry the work forward. Everything is Computer.\u201d\nPersonal Computer operates within a controlled environment designed to limit risk. Sensitive actions require user approval, each session generates an audit trail, and a kill switch allows users to immediately stop activity.\nThe system is initially being rolled out through a limited waitlist and is expected to be offered through a subscription model priced at about $200 per month, Decoder reported Friday (March 13).\nBeyond individual users, the company is also positioning the technology for businesses through a version called Computer for Enterprise. In internal testing of more than 16,000 queries, measured against institutional benchmarks used by organizations such as McKinsey, Harvard, MIT and Boston Consulting Group, Perplexity said the system completed what it estimated to be 3.25 years of work in four weeks, saving roughly $1.6 million in labor costs.\nThe enterprise system connects directly to business software through application connectors, allowing teams to query platforms such as Snowflake, Salesforce and HubSpot while simultaneously analyzing other internal or external data sources.\nA financial analyst, for example, could ask the system to retrieve revenue data from Snowflake while combining it with market analysis, while a sales team could pull CRM data alongside competitive intelligence.\nThe platform runs on a secure foundation that includes SOC 2 Type II compliance, SAML single sign-on and audit logs, with each query executed inside its own isolated environment.\nDevelopers Turn to Mac Mini to Run AI Agents\nPerplexity\u2019s system runs on a dedicated Mac mini, although the hardware itself is not essential to the concept and could eventually be replaced by other always-on machines designed to host AI agents.\nMac minis have become increasingly popular among developers who want to run AI agents locally rather than relying entirely on cloud infrastructure, Tech Rader reported Feb. 17. Mac minis have become harder to obtain in certain markets as programmers buy the machines specifically to run AI agent systems.\nDeveloper communities are increasingly recommending Mac minis for running AI agents because they combine strong computing performance with relatively low energy consumption, open-source vector database Milvus said in a blog post. Developers often treat the device less like a personal computer and more like a small server dedicated to running AI workloads.\nThe growing use of Mac minis to run AI agents reflects a broader shift in how AI software operates. As new agent frameworks emerge, computers may evolve from tools used occasionally into environments where AI agents run continuously, managing tasks and interacting with digital systems throughout the day.\nFor all PYMNTS AI coverage, subscribe to the daily AI Newsletter.\n\r\n\r\nThe post Perplexity\u2019s Computer for Enterprise Completed 3.25 Years of Work in Four Weeks appeared first on PYMNTS.com.", "date_published": "2026-03-13T15:57:18-04:00", "date_modified": "2026-03-13T15:57:18-04:00", "authors": [ { "name": "PYMNTS", "url": "https://www.pymnts.com/author/pymnts/", "avatar": "https://secure.gravatar.com/avatar/679fcf5c2ed5358e99e8e23b22e3b5d761e37bdb76fa7b0e13d8ecd9ff01bf88?s=512&d=blank&r=g" } ], "author": { "name": "PYMNTS", "url": "https://www.pymnts.com/author/pymnts/", "avatar": "https://secure.gravatar.com/avatar/679fcf5c2ed5358e99e8e23b22e3b5d761e37bdb76fa7b0e13d8ecd9ff01bf88?s=512&d=blank&r=g" }, "image": "https://www.pymnts.com/wp-content/uploads/2025/03/Perplexity-1.jpg", "tags": [ "Apple", "Artificial Intelligence", "News", "Perplexity", "PYMNTS News" ] }, { "id": "https://www.pymnts.com/?p=3560690", "url": "https://www.pymnts.com/news/artificial-intelligence/2026/meta-avocado-delay-puts-135-billion-dollar-ai-bet-under-scrutiny/", "title": "Meta\u2019s Avocado Delay Puts $135 Billion AI Bet Under Scrutiny", "content_html": "\u00a0Meta delayed the launch of Avocado, its next-generation artificial intelligence model.
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\r\n\r\n\r\nThe post Meta\u2019s Avocado Delay Puts $135 Billion AI Bet Under Scrutiny appeared first on PYMNTS.com.
\n", "content_text": "\u00a0Meta delayed the launch of Avocado, its next-generation artificial intelligence model.\r\n\t\r\n\t\t\r\n\t\r\n\r\n\r\n\t\nThe company pushed the release to at least May from a planned debut this month, The New York Times reported Thursday (March 12).\nThe delay follows internal testing that showed the model trailing leading systems from Google, OpenAI and Anthropic in key areas, including logical reasoning, programming and writing, the report said.\nAvocado outperformed Meta\u2019s previous generation models and some earlier competing systems, The Information reported Feb. 4, but it failed to match the performance of Google\u2019s newest Gemini models, per the NYT report.\nThe gap matters because Avocado was not intended as an incremental upgrade. The model was designed to compete directly with frontier systems from OpenAI and Google and to serve as the centerpiece of Meta\u2019s next phase of AI development.\nInternally, the model had been framed as a major leap forward. Meta Superintelligence Labs Product Manager Megan Fu described Avocado in an internal memo as the company\u2019s most capable base model yet and suggested it had the potential to outperform rival systems once additional post-training improvements were applied, per the report from The Information.\nThe production version has not yet met that expectation.\nThe delay also follows a difficult year for Meta\u2019s AI efforts. The company\u2019s Llama 4 release last year failed to generate strong developer enthusiasm, prompting an internal restructuring across parts of the AI organization, The Information reported Aug. 15.\nAvocado had been positioned internally as the reset.\nThe Gemini Question and What It Signals\nThe more revealing development may be what Meta is considering in the interim.\nThe company\u2019s leadership discussed temporarily licensing Google\u2019s Gemini technology to power certain Meta products while Avocado is brought up to competitive performance, the NYT reported. No decision has been confirmed.\nMeta has spent years positioning open-source AI models such as Llama as a strategic alternative to proprietary systems from OpenAI and Google. Relying on Gemini, even temporarily, would invert that narrative and raise questions about how quickly Meta\u2019s internal models can reach the top tier of AI performance.\nThe episode also underscores a broader strategic shift underway inside the company. Meta\u2019s earlier AI strategy emphasized open development and ecosystem adoption through models such as Llama. Avocado represents a move toward more tightly controlled, commercial-grade systems that could power proprietary products.\nThat transition was already complex. A performance gap only complicates it further.\nMeta\u2019s broader AI roadmap remains active. A next-generation model codenamed Watermelon is under development, the NYT reported, along with an image and video generation system known internally as Mango.\nHowever, the timeline now appears less certain than the ambition.\nSpending Without a Cloud Business\nBeyond the technical delay, Avocado highlights a deeper financial question surrounding Meta\u2019s AI strategy.\nThe company outlined capital spending plans of between $115 billion and $135 billion for 2026 as it races to build data centers, chips and infrastructure capable of supporting increasingly powerful AI models.\nThat level of spending places Meta alongside the largest investors in AI infrastructure, including Amazon, Microsoft and Google.\nThe difference is that those companies operate cloud businesses that directly monetize the computing power they build. Training models for internal use is only one part of the equation. The same infrastructure can also be rented to thousands of enterprise customers.\nMeta does not have that outlet. Instead, the company\u2019s AI monetization strategy is expected to run primarily through improvements to its existing platforms. AI is already being used to refine ad targeting, generate content recommendations and power assistant tools across Facebook, Instagram and WhatsApp.\nThose applications could drive revenue growth by increasing engagement and advertising efficiency. However, they do not produce the same direct revenue stream as selling AI compute or model access through the cloud. During Meta\u2019s fourth-quarter earnings call, CEO Mark Zuckerberg positioned AI as the company\u2019s next long-term growth engine. Avocado was supposed to provide the near-term proof.\nFor all PYMNTS AI coverage, subscribe to the daily AI Newsletter.\n\r\n\r\nThe post Meta\u2019s Avocado Delay Puts $135 Billion AI Bet Under Scrutiny appeared first on PYMNTS.com.", "date_published": "2026-03-13T12:12:26-04:00", "date_modified": "2026-03-13T12:12:26-04:00", "authors": [ { "name": "PYMNTS", "url": "https://www.pymnts.com/author/pymnts/", "avatar": "https://secure.gravatar.com/avatar/679fcf5c2ed5358e99e8e23b22e3b5d761e37bdb76fa7b0e13d8ecd9ff01bf88?s=512&d=blank&r=g" } ], "author": { "name": "PYMNTS", "url": "https://www.pymnts.com/author/pymnts/", "avatar": "https://secure.gravatar.com/avatar/679fcf5c2ed5358e99e8e23b22e3b5d761e37bdb76fa7b0e13d8ecd9ff01bf88?s=512&d=blank&r=g" }, "image": "https://www.pymnts.com/wp-content/uploads/2023/10/Meta-AI.jpg", "tags": [ "Artificial Intelligence", "Innovation", "Meta", "News", "PYMNTS News", "Technology" ] }, { "id": "https://www.pymnts.com/?p=3535860", "url": "https://www.pymnts.com/news/artificial-intelligence/2026/42-percent-of-power-users-say-they-now-use-search-less/", "title": "42% of Power Users Say They Now Use Search Less", "content_html": "Artificial intelligence is starting to look less like a novelty and more like a new kind of personal assistant, one that people increasingly tap first when they need to do something, not just look something up.
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\r\n\r\n\r\nThe post 42% of Power Users Say They Now Use Search Less appeared first on PYMNTS.com.
\n", "content_text": "Artificial intelligence is starting to look less like a novelty and more like a new kind of personal assistant, one that people increasingly tap first when they need to do something, not just look something up.\r\n\t\r\n\t\t\r\n\t\r\n\r\n\r\n\t\nThat shift was at the center of \u201cHow AI Becomes the Place Consumers Start Everything,\u201d the December installment of PYMNTS Intelligence\u2019s Agentic AI Report series. The report was based on a survey of 2,113 U.S. adult consumers and tracked adoption across 54 personal-use tasks in nine areas of daily life, including shopping, finances, health, education and travel.\nWhat stood out was not just that people are using AI, but how they are using it. Consumers are turning to dedicated AI platforms to plan, learn, shop and decide, compressing the familiar search-to-purchase sequence into a more conversational flow where intent is stated once and then refined through follow-up prompts.\nKey findings from the report include:\n\nMore than 6 in 10 consumers used dedicated AI platforms in the past year.\nMore than one-third of Generation Z consumers and Power Users (consumers who perform 25 or more distinct tasks) turned to a dedicated AI platform as their first tool when tackling personal tasks.\nAmong consumers who primarily use dedicated AI platforms, 43% reported fully replacing older methods, and 42% said they now use search less.\n\nThe task-level story helps explain why the \u201cAI-first\u201d pattern is taking hold, the report said. People start with jobs where a conversational interface saves time, and the downside of a bad answer is limited. The report pointed to personal tasks as the main driver of use, and it described Power Users as extending AI across shopping discovery, planning, learning and wellness.\nThose are practical, repeatable activities, like drafting and polishing writing, mapping out an itinerary, summarizing information, narrowing product choices, or building a simple plan for a goal. Over time, those small wins create what the report called behavioral \u201cmuscle memory,\u201d which can make AI-mediated decisioning feel normal once trust and user experience improve.\nThe report also showed why not everyone is moving at the same speed. PYMNTS Intelligence grouped consumers by how many distinct AI tasks they perform and how complex those tasks are, yielding four personas, including holdouts, Light Users, Mainstream Users and Power Users. Power Users represented 11% of consumers, while holdouts represented 43% of consumers. Adoption also varied by age. Gen Z was the least resistant to trying the technology, with 71% having experimented with it.\nWhere the next wave gets interesting for payments is in the bridge from helping consumers decide to helping them pay.\nConsumers signaled that the most credible path is not storing a card inside an AI app. They lean toward familiar protections. One-third prefer linking a digital wallet to an AI platform for safer, easier payments, and the report said wallets could become the trust layer that makes AI-mediated commerce viable at scale.\nTrust remains the gating factor. Consumers cited privacy and misunderstandings as key barriers, which is why disclosures, user-controlled permissions and a way to escalate when a model is uncertain matter. The encouraging part is that the everyday tasks are already building momentum. This is how habits change, slowly and then quickly.\nFor all PYMNTS AI coverage, subscribe to the daily AI Newsletter.\nAt PYMNTS Intelligence, we work with businesses to uncover insights that fuel intelligent, data-driven discussions on changing customer expectations, a more connected economy and the strategic shifts necessary to achieve outcomes. With rigorous research methodologies and unwavering commitment to objective quality, we offer trusted data to grow your business. As our partner, you\u2019ll have access to our diverse team of PhDs, researchers, data analysts, number crunchers, subject matter veterans and editorial experts.\n\r\n\r\nThe post 42% of Power Users Say They Now Use Search Less appeared first on PYMNTS.com.", "date_published": "2026-03-13T04:00:05-04:00", "date_modified": "2026-03-12T21:54:30-04:00", "authors": [ { "name": "PYMNTS", "url": "https://www.pymnts.com/author/pymnts/", "avatar": "https://secure.gravatar.com/avatar/679fcf5c2ed5358e99e8e23b22e3b5d761e37bdb76fa7b0e13d8ecd9ff01bf88?s=512&d=blank&r=g" } ], "author": { "name": "PYMNTS", "url": "https://www.pymnts.com/author/pymnts/", "avatar": "https://secure.gravatar.com/avatar/679fcf5c2ed5358e99e8e23b22e3b5d761e37bdb76fa7b0e13d8ecd9ff01bf88?s=512&d=blank&r=g" }, "image": "https://www.pymnts.com/wp-content/uploads/2025/09/Google-Search-1.jpg", "tags": [ "Artificial Intelligence", "digital wallets", "ecommerce", "Featured News", "Mobile Wallets", "News", "PYMNTS News", "PYMNTS Study", "Retail" ] } ] }