{"id":6251,"date":"2026-04-02T17:09:54","date_gmt":"2026-04-02T17:09:54","guid":{"rendered":"https:\/\/globalnewstoday.uk\/index.php\/2026\/04\/02\/the-enterprise-ai-readiness-gap-what-company-data-reveals-about-the-real-barrier-to-scale-pymnts-com\/"},"modified":"2026-04-02T17:09:54","modified_gmt":"2026-04-02T17:09:54","slug":"the-enterprise-ai-readiness-gap-what-company-data-reveals-about-the-real-barrier-to-scale-pymnts-com","status":"publish","type":"post","link":"https:\/\/globalnewstoday.uk\/index.php\/2026\/04\/02\/the-enterprise-ai-readiness-gap-what-company-data-reveals-about-the-real-barrier-to-scale-pymnts-com\/","title":{"rendered":"The Enterprise AI Readiness Gap: What Company Data Reveals About the Real Barrier to Scale &#8211; PYMNTS.com"},"content":{"rendered":"<p>                   More than seven in 10 executives at enterprise-level firms blame their companies, and not AI itself, for slowing the technology&#8217;s roll out across business operations.                <br \/>               <span class='fs-3 fw-bold'>Get Unlimited Access<\/span>               <br \/>               Complete the form below for free, unlimited access to all our Data Studies, Trackers, and PYMNTS Intelligence reports.           <br \/>           Thank you for registering. Please confirm your email to view all our Trackers.       <\/p>\n<p><span class=\"wpcf7-form-control-wrap\" data-name=\"firstName\"><input size=\"40\" maxlength=\"400\" class=\"wpcf7-form-control wpcf7-text wpcf7-validates-as-required form-control border-secondary\" id=\"firstName\" aria-required=\"true\" aria-invalid=\"false\" placeholder=\"First Name*\" value=\"\" type=\"text\" name=\"firstName\" \/><\/span> \t\t\t<br \/><span class=\"wpcf7-form-control-wrap\" data-name=\"lastName\"><input size=\"40\" maxlength=\"400\" class=\"wpcf7-form-control wpcf7-text wpcf7-validates-as-required form-control border-secondary\" id=\"lastName\" aria-required=\"true\" aria-invalid=\"false\" placeholder=\"Last Name*\" value=\"\" type=\"text\" name=\"lastName\" \/><\/span> \t\t\t<br \/><span class=\"wpcf7-form-control-wrap\" data-name=\"YourTitle\"><input size=\"40\" maxlength=\"400\" class=\"wpcf7-form-control wpcf7-text wpcf7-validates-as-required form-control border-secondary\" id=\"inputTitle\" aria-required=\"true\" aria-invalid=\"false\" placeholder=\"Title*\" value=\"\" type=\"text\" name=\"YourTitle\" \/><\/span> \t\t\t<br \/><span class=\"wpcf7-form-control-wrap\" data-name=\"YourCompany\"><input size=\"40\" maxlength=\"400\" class=\"wpcf7-form-control wpcf7-text wpcf7-validates-as-required form-control border-secondary\" id=\"inputCompany\" aria-required=\"true\" aria-invalid=\"false\" placeholder=\"Company*\" value=\"\" type=\"text\" name=\"YourCompany\" \/><\/span> \t\t\t<br \/><span class=\"wpcf7-form-control-wrap\" data-name=\"YourEmail\"><input size=\"40\" maxlength=\"400\" class=\"wpcf7-form-control wpcf7-email wpcf7-validates-as-required wpcf7-text wpcf7-validates-as-email form-control border-secondary\" id=\"inputEmail\" aria-required=\"true\" aria-invalid=\"false\" placeholder=\"Work Email*\" value=\"\" type=\"email\" name=\"YourEmail\" \/><\/span> \t\t\t<br \/><span class=\"wpcf7-form-control-wrap\" data-name=\"YourCountry\"><input size=\"40\" maxlength=\"400\" class=\"wpcf7-form-control wpcf7-text wpcf7-validates-as-required form-control border-secondary\" id=\"inputCountry\" aria-required=\"true\" aria-invalid=\"false\" placeholder=\"Country*\" value=\"\" type=\"text\" name=\"YourCountry\" \/><\/span> \t\t\t<br \/><span class=\"wpcf7-form-control-wrap\" data-name=\"newsLetterChoice\"><span class=\"wpcf7-form-control wpcf7-checkbox me-1\" id=\"checkNewsletter\"><span class=\"wpcf7-list-item first last\"><input type=\"checkbox\" name=\"newsLetterChoice[]\" value=\"yes\" checked=\"checked\" \/><span class=\"wpcf7-list-item-label\">yes<\/span><\/span><\/span><\/span><span class=\"small\">Subscribe to our daily newsletter, PYMNTS Today.<\/span> \t\t\t<br \/>By completing this form, you agree to receive marketing communications from PYMNTS and to the sharing of your information with our sponsor, if applicable, in accordance with our <a class=\"fw-bold\" href=\"https:\/\/pymnts-com-develop.go-vip.net\/privacy-policy\/\">Privacy Policy<\/a> and <a class=\"fw-bold\" href=\"https:\/\/pymnts-com-develop.go-vip.net\/terms-conditions\/\">Terms and Conditions<\/a>. \t\t\t<br \/><input id='hiddenPath' type='hidden' name='path' value='' \/><input type='hidden' name='userDeviceId' id='userDeviceId' \/><input type='hidden' name='pageTitle' id='pageTitle' \/> \t\t\t<br \/><input class=\"wpcf7-form-control wpcf7-submit has-spinner btn btn-dark text-uppercase py-2 px-5 small\" id=\"theSubmitButton\" type=\"submit\" value=\"Submit\" \/> \t<br \/><label>&#916;<textarea name=\"_wpcf7_ak_hp_textarea\" cols=\"45\" rows=\"8\" maxlength=\"100\"><\/textarea><\/label><input type=\"hidden\" id=\"ak_js_1\" name=\"_wpcf7_ak_js\" value=\"238\"\/><script>document.getElementById( \"ak_js_1\" ).setAttribute( \"value\", ( new Date() ).getTime() );<\/script><br \/>Decision makers at major businesses already know that artificial intelligence can deliver value. Skepticism among leaders at enterprises (companies with $1 billion or more in annual revenue) has basically <a href=\"https:\/\/www.pymnts.com\/study_posts\/agentic-ai-breaks-out-of-the-sandbox\" target=\"_blank\" rel=\"noopener\">evaporated<\/a>. Now new data points to the next obstacle: an organization\u2019s internal readiness.<br \/>The latest edition of the <b><strong>PYMNTS Intelligence Enterprise AI Report<\/strong><\/b> finds that more than seven in 10 executives at enterprise-level firms say internal constraints are holding back AI\u2019s performance within their organizations. Conversely, only 11% blame the technology itself.<br \/>That means there\u2019s a long way to go before enterprises are ready to use AI to its full potential. The typical executive surveyed said their business was facing four to five organizational barriers simultaneously. These include the quality of internal data, budget constraints and issues with internal approvals. There\u2019s no quick fix. Unlocking the possibilities of AI will require addressing the tangle of these interconnected organizational barriers.<br \/>Amid the internal speed bumps, there\u2019s a huge gap between executives\u2019 confidence in their firms\u2019 AI capabilities and the realities they\u2019re facing. Ninety-nine percent of executives say their data governance standards and processes (the guardrails and ethical standards used to guide the development and deployment of data that\u2019s often highly sensitive) support their use of enterprise AI. At the same time, 85% say their data is at least somewhat fragmented, making it tough to scale the technology. In principle, these companies have governance frameworks in place, but their overall infrastructure isn\u2019t ready just yet to pave the way for organization-wide deployment.<br \/>This disconnect shows up in how enterprises are using the technology right now. AI is deeply embedded in data and technology functions, but it\u2019s still in the early stages in other key areas such as human resources and finance. To push the value of enterprise AI to the next level, the technology\u00a0must be scalable. For that to happen, enterprises need to connect the dots, align their teams and build infrastructure across the entire organization.    <\/p>\n<div class=\"branded-divider\"><svg preserveaspectratio=\"none\" width=\"1439\" height=\"2\" viewbox=\"0 0 1439 2\" fill=\"none\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"> <g clip-path=\"url(#clip0_2397_2574)\"> <mask id=\"mask0_2397_2574\" style=\"mask-type:luminance\" maskunits=\"userSpaceOnUse\" x=\"0\" y=\"0\" width=\"1440\" height=\"2\"> <path d=\"M1440 0H0V2H1440V0Z\" fill=\"white\"\/> <\/mask> <g mask=\"url(#mask0_2397_2574)\"> <path d=\"M1440 0H0V2H1440V0Z\" fill=\"url(#paint0_linear_2397_2574)\"\/> <\/g> <\/g> <defs> <lineargradient id=\"paint0_linear_2397_2574\" x1=\"5.10048\" y1=\"0.997057\" x2=\"1439.97\" y2=\"0.967508\" gradientunits=\"userSpaceOnUse\"> <stop stop-color=\"white\"\/> <stop offset=\"0.51\" stop-color=\"#00A469\"\/> <stop offset=\"1\" stop-color=\"white\"\/> <\/lineargradient> <clippath id=\"clip0_2397_2574\"> <rect width=\"1440\" height=\"2\" fill=\"white\"\/> <\/clippath> <\/defs> <\/svg> <\/div>\n<p>More than seven in 10 executives say that constraints within their organizations limit their firms\u2019 AI performance more than technology itself. Only 11% say the technology\u2019s functionality and accuracy are the main sticking points.<br \/>No single factor stands out as AI\u2019s biggest obstacle. Instead, data quality (63%), governance processes (48%), budget constraints (49%) and unclear process ownership structures (46%) are all holding back the technology\u2019s deployment. Executives cite four to five barriers on average, and when forced to pick their biggest obstacle, their answers are spread almost evenly across six core categories.<br \/>Nearly all (99%) executives are very or extremely confident that their data governance supports enterprise-level AI. Yet 85% admit their data is fragmented or only moderately integrated and 63% cite data quality as a barrier. Theoretically, they have the systems in place, but it will be tough to leverage them until all the dots are connected.    <\/p>\n<div class=\"branded-divider\"><svg preserveaspectratio=\"none\" width=\"1439\" height=\"2\" viewbox=\"0 0 1439 2\" fill=\"none\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"> <g clip-path=\"url(#clip0_2397_2574)\"> <mask id=\"mask0_2397_2574\" style=\"mask-type:luminance\" maskunits=\"userSpaceOnUse\" x=\"0\" y=\"0\" width=\"1440\" height=\"2\"> <path d=\"M1440 0H0V2H1440V0Z\" fill=\"white\"\/> <\/mask> <g mask=\"url(#mask0_2397_2574)\"> <path d=\"M1440 0H0V2H1440V0Z\" fill=\"url(#paint0_linear_2397_2574)\"\/> <\/g> <\/g> <defs> <lineargradient id=\"paint0_linear_2397_2574\" x1=\"5.10048\" y1=\"0.997057\" x2=\"1439.97\" y2=\"0.967508\" gradientunits=\"userSpaceOnUse\"> <stop stop-color=\"white\"\/> <stop offset=\"0.51\" stop-color=\"#00A469\"\/> <stop offset=\"1\" stop-color=\"white\"\/> <\/lineargradient> <clippath id=\"clip0_2397_2574\"> <rect width=\"1440\" height=\"2\" fill=\"white\"\/> <\/clippath> <\/defs> <\/svg> <\/div>\n<p>We asked executives how new forms of AI have performed within their organizations compared with their expectations across a range of business goals. For every objective measured, a majority report that AI has performed better than expected.<br \/><iframe id=\"datawrapper-chart-LRrd2\" style=\"width: 0; min-width: 100% !important; border: none;\" title=\"Has New AI Outperformed Expectations?\" src=\"https:\/\/datawrapper.dwcdn.net\/LRrd2\/2\/\" height=\"496\" frameborder=\"0\" scrolling=\"no\" aria-label=\"Bar Chart\" data-external=\"1\"><\/iframe><script type=\"text\/javascript\">window.addEventListener(\"message\",function(a){if(void 0!==a.data[\"datawrapper-height\"]){var e=document.querySelectorAll(\"iframe\");for(var t in a.data[\"datawrapper-height\"])for(var r,i=0;r=e[i];i++)if(r.contentWindow===a.source){var d=a.data[\"datawrapper-height\"][t]+\"px\";r.style.height=d}}});<\/script><br \/><b><strong>The baseline:<\/strong><\/b> Across all business goals measured, not a single one fell below 62% of better-than-expected performance. AI is broadly delivering on its promises, which means organizational barriers are the main issue now.    <\/p>\n<div class=\"branded-divider\"><svg preserveaspectratio=\"none\" width=\"1439\" height=\"2\" viewbox=\"0 0 1439 2\" fill=\"none\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"> <g clip-path=\"url(#clip0_2397_2574)\"> <mask id=\"mask0_2397_2574\" style=\"mask-type:luminance\" maskunits=\"userSpaceOnUse\" x=\"0\" y=\"0\" width=\"1440\" height=\"2\"> <path d=\"M1440 0H0V2H1440V0Z\" fill=\"white\"\/> <\/mask> <g mask=\"url(#mask0_2397_2574)\"> <path d=\"M1440 0H0V2H1440V0Z\" fill=\"url(#paint0_linear_2397_2574)\"\/> <\/g> <\/g> <defs> <lineargradient id=\"paint0_linear_2397_2574\" x1=\"5.10048\" y1=\"0.997057\" x2=\"1439.97\" y2=\"0.967508\" gradientunits=\"userSpaceOnUse\"> <stop stop-color=\"white\"\/> <stop offset=\"0.51\" stop-color=\"#00A469\"\/> <stop offset=\"1\" stop-color=\"white\"\/> <\/lineargradient> <clippath id=\"clip0_2397_2574\"> <rect width=\"1440\" height=\"2\" fill=\"white\"\/> <\/clippath> <\/defs> <\/svg> <\/div>\n<p>As PYMNTS Intelligence <a href=\"https:\/\/www.pymnts.com\/study_posts\/how-agentic-ai-went-from-zero-to-cfo-test-runs-in-90-days\/\" target=\"_blank\" rel=\"noopener\">reported<\/a> last September, the question of whether AI technologies can add value to enterprises has already been answered. This month\u2019s data quantifies that conclusion. When asked whether organizational readiness or AI technology capabilities are the greater constraint on AI performance, 71% of senior technology executives point to their own organizations. Only 11% blame the technology.<br \/>This 7-to-1 ratio represents a decisive shift. The constraint is now the ability of billion-dollar organizations to rewire their people, processes and data readiness to harness what AI can do.<br \/><iframe id=\"datawrapper-chart-VAadM\" style=\"width: 0; min-width: 100% !important; border: none;\" title=\"What Limits AI Performance More?\" src=\"https:\/\/datawrapper.dwcdn.net\/VAadM\/2\/\" height=\"291\" frameborder=\"0\" scrolling=\"no\" aria-label=\"Bar Chart\" data-external=\"1\"><\/iframe><script type=\"text\/javascript\">window.addEventListener(\"message\",function(a){if(void 0!==a.data[\"datawrapper-height\"]){var e=document.querySelectorAll(\"iframe\");for(var t in a.data[\"datawrapper-height\"])for(var r,i=0;r=e[i];i++)if(r.contentWindow===a.source){var d=a.data[\"datawrapper-height\"][t]+\"px\";r.style.height=d}}});<\/script>    <\/p>\n<div class=\"branded-divider\"><svg preserveaspectratio=\"none\" width=\"1439\" height=\"2\" viewbox=\"0 0 1439 2\" fill=\"none\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"> <g clip-path=\"url(#clip0_2397_2574)\"> <mask id=\"mask0_2397_2574\" style=\"mask-type:luminance\" maskunits=\"userSpaceOnUse\" x=\"0\" y=\"0\" width=\"1440\" height=\"2\"> <path d=\"M1440 0H0V2H1440V0Z\" fill=\"white\"\/> <\/mask> <g mask=\"url(#mask0_2397_2574)\"> <path d=\"M1440 0H0V2H1440V0Z\" fill=\"url(#paint0_linear_2397_2574)\"\/> <\/g> <\/g> <defs> <lineargradient id=\"paint0_linear_2397_2574\" x1=\"5.10048\" y1=\"0.997057\" x2=\"1439.97\" y2=\"0.967508\" gradientunits=\"userSpaceOnUse\"> <stop stop-color=\"white\"\/> <stop offset=\"0.51\" stop-color=\"#00A469\"\/> <stop offset=\"1\" stop-color=\"white\"\/> <\/lineargradient> <clippath id=\"clip0_2397_2574\"> <rect width=\"1440\" height=\"2\" fill=\"white\"\/> <\/clippath> <\/defs> <\/svg> <\/div>\n<p>Enterprises face a tangle of interconnected, deeply rooted issues. When asked to identify organizational barriers limiting AI performance, executives didn\u2019t point to a single topic. Instead, they cited an average of four to five barriers. Data quality (63%), budget limitations (49%) and governance processes (48%) were the three most common bottlenecks.<br \/>Meanwhile, the barriers that rank lowest tell an important story of their own. Few leaders are held back by a lack of trust in AI models (17%) or the need for clear evidence of return on investment (9%). Evidently, enterprises are confident in AI\u2019s capabilities but are struggling with the organizational infrastructure needed to scale them.<br \/><iframe id=\"datawrapper-chart-d2PDC\" style=\"width: 0; min-width: 100% !important; border: none;\" title=\"Organizational Barriers Limiting AI Performance\" src=\"https:\/\/datawrapper.dwcdn.net\/d2PDC\/2\/\" height=\"691\" frameborder=\"0\" scrolling=\"no\" aria-label=\"Bar Chart\" data-external=\"1\"><\/iframe><script type=\"text\/javascript\">window.addEventListener(\"message\",function(a){if(void 0!==a.data[\"datawrapper-height\"]){var e=document.querySelectorAll(\"iframe\");for(var t in a.data[\"datawrapper-height\"])for(var r,i=0;r=e[i];i++)if(r.contentWindow===a.source){var d=a.data[\"datawrapper-height\"][t]+\"px\";r.style.height=d}}});<\/script>    <\/p>\n<div class=\"branded-divider\"><svg preserveaspectratio=\"none\" width=\"1439\" height=\"2\" viewbox=\"0 0 1439 2\" fill=\"none\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"> <g clip-path=\"url(#clip0_2397_2574)\"> <mask id=\"mask0_2397_2574\" style=\"mask-type:luminance\" maskunits=\"userSpaceOnUse\" x=\"0\" y=\"0\" width=\"1440\" height=\"2\"> <path d=\"M1440 0H0V2H1440V0Z\" fill=\"white\"\/> <\/mask> <g mask=\"url(#mask0_2397_2574)\"> <path d=\"M1440 0H0V2H1440V0Z\" fill=\"url(#paint0_linear_2397_2574)\"\/> <\/g> <\/g> <defs> <lineargradient id=\"paint0_linear_2397_2574\" x1=\"5.10048\" y1=\"0.997057\" x2=\"1439.97\" y2=\"0.967508\" gradientunits=\"userSpaceOnUse\"> <stop stop-color=\"white\"\/> <stop offset=\"0.51\" stop-color=\"#00A469\"\/> <stop offset=\"1\" stop-color=\"white\"\/> <\/lineargradient> <clippath id=\"clip0_2397_2574\"> <rect width=\"1440\" height=\"2\" fill=\"white\"\/> <\/clippath> <\/defs> <\/svg> <\/div>\n<p>When executives were asked to identify the single most limiting factor, there was no consensus.<br \/>The most common barrier reported is integration with existing systems and workflows, but only 19% cited it as their biggest issue. The next-most cited hurdle was data quality, at 14%. Another common obstacle is change management (meaning the internal organizational work required to shift how people, teams and workflows operate around AI), at 12%. Tied with change management is confusion around internal ownership and accountability. Budget constraints also garnered 12%. Rounding out the top-most common difficulties, business-technology misalignment garnered 11%.<br \/>The relatively even dispersion across the top six categories, all falling between 11% and 19%, confirms there is no one silver-bullet solution. That\u2019s because these barriers are interconnected. Fragmented data can\u2019t be resolved without better integration, which requires governance coordination; governance, in turn, depends on clear ownership of AI-enabled processes.<br \/><iframe id=\"datawrapper-chart-32351\" style=\"width: 0; min-width: 100% !important; border: none;\" title=\"Single Most Limiting Organizational Barrier\" src=\"https:\/\/datawrapper.dwcdn.net\/32351\/3\/\" height=\"564\" frameborder=\"0\" scrolling=\"no\" aria-label=\"Bar Chart\" data-external=\"1\"><\/iframe><script type=\"text\/javascript\">window.addEventListener(\"message\",function(a){if(void 0!==a.data[\"datawrapper-height\"]){var e=document.querySelectorAll(\"iframe\");for(var t in a.data[\"datawrapper-height\"])for(var r,i=0;r=e[i];i++)if(r.contentWindow===a.source){var d=a.data[\"datawrapper-height\"][t]+\"px\";r.style.height=d}}});<\/script><br \/>Data quality and fragmentation is the most common barrier, but issues with integration are the most limiting. Improving the data doesn\u2019t help much if that information can\u2019t flow between systems.    <\/p>\n<div class=\"branded-divider\"><svg preserveaspectratio=\"none\" width=\"1439\" height=\"2\" viewbox=\"0 0 1439 2\" fill=\"none\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"> <g clip-path=\"url(#clip0_2397_2574)\"> <mask id=\"mask0_2397_2574\" style=\"mask-type:luminance\" maskunits=\"userSpaceOnUse\" x=\"0\" y=\"0\" width=\"1440\" height=\"2\"> <path d=\"M1440 0H0V2H1440V0Z\" fill=\"white\"\/> <\/mask> <g mask=\"url(#mask0_2397_2574)\"> <path d=\"M1440 0H0V2H1440V0Z\" fill=\"url(#paint0_linear_2397_2574)\"\/> <\/g> <\/g> <defs> <lineargradient id=\"paint0_linear_2397_2574\" x1=\"5.10048\" y1=\"0.997057\" x2=\"1439.97\" y2=\"0.967508\" gradientunits=\"userSpaceOnUse\"> <stop stop-color=\"white\"\/> <stop offset=\"0.51\" stop-color=\"#00A469\"\/> <stop offset=\"1\" stop-color=\"white\"\/> <\/lineargradient> <clippath id=\"clip0_2397_2574\"> <rect width=\"1440\" height=\"2\" fill=\"white\"\/> <\/clippath> <\/defs> <\/svg> <\/div>\n<p>AI has gained a foothold, but there\u2019s a long way to go. The distance between the demand for and the reality of AI is apparent in the uneven way firms have rolled out the technology across different business functions. Advanced forms of AI, including large language models (LLMs) and agentic AI, are deeply embedded in only one area: data and technology. Specifically, 37% of enterprise leaders report fully integrating next-gen AI into their daily operations in this area, and another 46% have broadly deployed the technology for this function.<br \/>Enterprise processes related to growth and revenue (13% deeply embedded, 46% broadly deployed) and product and customer experience (11% and 34%, respectively) have achieved meaningful operational scale. But the majority of the enterprise remains in early stages. Payments and finance functions are further along than some areas, but have a long way to go, with 18% of firms embedding or broadly deploying next-gen AI in this area.<br \/>By contrast, HR and workforce management, corporate strategy, risk and compliance, and supply chain management are in the nascent stages. The adoption of next-generation AI tools is largely confined to the early, exploratory phase or limited pilots. These areas are where the readiness barriers are most visible.<br \/><iframe id=\"datawrapper-chart-Peeh4\" style=\"width: 0; min-width: 100% !important; border: none;\" title=\"Next-Generation AI Adoption by Business Function\" src=\"https:\/\/datawrapper.dwcdn.net\/Peeh4\/2\/\" height=\"464\" frameborder=\"0\" scrolling=\"no\" aria-label=\"Stacked Bars\" data-external=\"1\"><\/iframe><script type=\"text\/javascript\">window.addEventListener(\"message\",function(a){if(void 0!==a.data[\"datawrapper-height\"]){var e=document.querySelectorAll(\"iframe\");for(var t in a.data[\"datawrapper-height\"])for(var r,i=0;r=e[i];i++)if(r.contentWindow===a.source){var d=a.data[\"datawrapper-height\"][t]+\"px\";r.style.height=d}}});<\/script>    <\/p>\n<div class=\"branded-divider\"><svg preserveaspectratio=\"none\" width=\"1439\" height=\"2\" viewbox=\"0 0 1439 2\" fill=\"none\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"> <g clip-path=\"url(#clip0_2397_2574)\"> <mask id=\"mask0_2397_2574\" style=\"mask-type:luminance\" maskunits=\"userSpaceOnUse\" x=\"0\" y=\"0\" width=\"1440\" height=\"2\"> <path d=\"M1440 0H0V2H1440V0Z\" fill=\"white\"\/> <\/mask> <g mask=\"url(#mask0_2397_2574)\"> <path d=\"M1440 0H0V2H1440V0Z\" fill=\"url(#paint0_linear_2397_2574)\"\/> <\/g> <\/g> <defs> <lineargradient id=\"paint0_linear_2397_2574\" x1=\"5.10048\" y1=\"0.997057\" x2=\"1439.97\" y2=\"0.967508\" gradientunits=\"userSpaceOnUse\"> <stop stop-color=\"white\"\/> <stop offset=\"0.51\" stop-color=\"#00A469\"\/> <stop offset=\"1\" stop-color=\"white\"\/> <\/lineargradient> <clippath id=\"clip0_2397_2574\"> <rect width=\"1440\" height=\"2\" fill=\"white\"\/> <\/clippath> <\/defs> <\/svg> <\/div>\n<p>Leaders are confident they\u2019re ready for enterprise AI, but the infrastructure isn\u2019t there yet. By their own account, enterprises have put in the work to ensure strong AI governance systems are in place. But they haven\u2019t organized their data in ways that would enable them to fully leverage those AI capabilities.<br \/>Nearly all (99%) senior technology leaders say they\u2019re very or extremely confident that their data governance is sufficient to support enterprise-scale AI.<br \/>However, they\u2019re less sure about their data infrastructure. Only 15% describe their data as mostly integrated, with few silos. Nearly two in three (65%) say their data is moderately integrated but that hurdles remain. Another 20% acknowledge data fragmentation across teams and systems. Overall, 63% of leaders cite data quality as an organizational barrier.<br \/>Clearly, data systems haven\u2019t caught up. Firms\u2019 confidence in their governance frameworks may mask the true scale of the integration challenges ahead.<br \/><iframe id=\"datawrapper-chart-5ZdHz\" style=\"width: 0; min-width: 100% !important; border: none;\" title=\"What the Data Shows\u2014Actual Data Environment Maturity\" src=\"https:\/\/datawrapper.dwcdn.net\/5ZdHz\/2\/\" height=\"291\" frameborder=\"0\" scrolling=\"no\" aria-label=\"Bar Chart\" data-external=\"1\"><\/iframe><script type=\"text\/javascript\">window.addEventListener(\"message\",function(a){if(void 0!==a.data[\"datawrapper-height\"]){var e=document.querySelectorAll(\"iframe\");for(var t in a.data[\"datawrapper-height\"])for(var r,i=0;r=e[i];i++)if(r.contentWindow===a.source){var d=a.data[\"datawrapper-height\"][t]+\"px\";r.style.height=d}}});<\/script><br \/><strong><b>Readiness gap:<\/b><\/strong> Firms may feel ready for AI because they\u2019re confident their governance frameworks are in place. But fragmented data systems suggest many aren\u2019t yet equipped to scale AI across the enterprise.    <\/p>\n<div class=\"branded-divider\"><svg preserveaspectratio=\"none\" width=\"1439\" height=\"2\" viewbox=\"0 0 1439 2\" fill=\"none\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"> <g clip-path=\"url(#clip0_2397_2574)\"> <mask id=\"mask0_2397_2574\" style=\"mask-type:luminance\" maskunits=\"userSpaceOnUse\" x=\"0\" y=\"0\" width=\"1440\" height=\"2\"> <path d=\"M1440 0H0V2H1440V0Z\" fill=\"white\"\/> <\/mask> <g mask=\"url(#mask0_2397_2574)\"> <path d=\"M1440 0H0V2H1440V0Z\" fill=\"url(#paint0_linear_2397_2574)\"\/> <\/g> <\/g> <defs> <lineargradient id=\"paint0_linear_2397_2574\" x1=\"5.10048\" y1=\"0.997057\" x2=\"1439.97\" y2=\"0.967508\" gradientunits=\"userSpaceOnUse\"> <stop stop-color=\"white\"\/> <stop offset=\"0.51\" stop-color=\"#00A469\"\/> <stop offset=\"1\" stop-color=\"white\"\/> <\/lineargradient> <clippath id=\"clip0_2397_2574\"> <rect width=\"1440\" height=\"2\" fill=\"white\"\/> <\/clippath> <\/defs> <\/svg> <\/div>\n<p>It\u2019s clear that AI technology has, in a sense, outpaced the organizations deploying it. Leaders are nearly unanimous that AI delivers value, yet 71% acknowledge their own readiness is the binding constraint.<br \/>The gap between confidence and reality is stark, with firms feeling good about their governance capabilities even as 85% of data environments remain fragmented or only moderately integrated. Closing this gap requires moving beyond governance frameworks and policy documentation toward operational integration: the practical work of connecting disparate systems and teams so that AI can scale across the entire organization.<br \/>To achieve real AI readiness, enterprises must:    <\/p>\n<div class=\"branded-divider\"><svg preserveaspectratio=\"none\" width=\"1439\" height=\"2\" viewbox=\"0 0 1439 2\" fill=\"none\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"> <g clip-path=\"url(#clip0_2397_2574)\"> <mask id=\"mask0_2397_2574\" style=\"mask-type:luminance\" maskunits=\"userSpaceOnUse\" x=\"0\" y=\"0\" width=\"1440\" height=\"2\"> <path d=\"M1440 0H0V2H1440V0Z\" fill=\"white\"\/> <\/mask> <g mask=\"url(#mask0_2397_2574)\"> <path d=\"M1440 0H0V2H1440V0Z\" fill=\"url(#paint0_linear_2397_2574)\"\/> <\/g> <\/g> <defs> <lineargradient id=\"paint0_linear_2397_2574\" x1=\"5.10048\" y1=\"0.997057\" x2=\"1439.97\" y2=\"0.967508\" gradientunits=\"userSpaceOnUse\"> <stop stop-color=\"white\"\/> <stop offset=\"0.51\" stop-color=\"#00A469\"\/> <stop offset=\"1\" stop-color=\"white\"\/> <\/lineargradient> <clippath id=\"clip0_2397_2574\"> <rect width=\"1440\" height=\"2\" fill=\"white\"\/> <\/clippath> <\/defs> <\/svg> <\/div>\n<p>PYMNTS Intelligence surveyed 65 verified senior technology executives at U.S.-based enterprises with at least $1 billion in annual revenue from Feb. 12\u201327, 2026. The companies span retail, manufacturing, wholesale, distribution, eCommerce and marketplace sectors. All respondents are primary decision-makers or the most knowledgeable about AI strategy, adoption and operations.<br \/><b><strong>Industry breakdown:<\/strong><\/b><br \/><b><strong>Annual revenue:<\/strong><\/b><br \/>                           PYMNTS Intelligence is a leading global data and analytics platform that uses proprietary data and methods to provide actionable insights on what\u2019s now and what\u2019s next in payments, commerce and the digital economy. Its team of data scientists include leading economists, econometricians, survey experts, financial analysts and marketing scientists with deep experience in the application of data to the issues that define the future of the digital transformation of the global economy. This multi-lingual team has conducted original data collection and analysis in more than three dozen global markets for some of the world\u2019s leading publicly traded and privately held firms.  <\/p>\n<p>  The PYMNTS Intelligence team that produced this report:  <\/p>\n<p>  John Gaffney: Chief Content Officer  <br \/>  Lynnley Browning: Managing Editor  <br \/>  Matthew Albrecht, Ph.D.: Senior Research Analyst                        <br \/>                           We are interested in your feedback on this report. If you have questions                           or                           comments, or if you would like to subscribe to this report, please email                           us at                           feedback@pymnts.com.                         <\/p>\n<p><a href=\"https:\/\/news.google.com\/rss\/articles\/CBMiwwFBVV95cUxQZGxGNHhmenc1bHJJZmZzeExvaU9SY1c0dGtaV1JsX3hJZkpKbktqQ3l5SUpxRUd1Z0REcUFtaUxNNGdlNGRfR2pXZzVPMWw4MUJRdzBUZm1MSzhCRnlmYnB1WVQ1Y0VCNWhXeExIN0JSNjF4Q3FNSXpRMEJmS0ROU1pHc3VxaVVIZVR5UWo3OGNuaXZrZTJwY0RTc1N3cFB3WTJuNmpma25JOG1yZjFSVlI1YUxrc1VMWkJ6ZVRsQzBsWnM?oc=5\">source<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>More than seven in 10 executives at enterprise-level firms blame their companies, and not AI itself, for slowing the technology&#8217;s roll out across business operations. Get Unlimited Access Complete the form below for free, unlimited access to all our Data Studies, Trackers, and PYMNTS Intelligence reports. Thank you for registering. 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