{"id":2817,"date":"2026-02-25T17:08:43","date_gmt":"2026-02-25T20:08:43","guid":{"rendered":"https:\/\/suricata.la\/banco-santander-and-its-1-billion-business-value-in-ai\/"},"modified":"2026-02-26T03:02:24","modified_gmt":"2026-02-26T06:02:24","slug":"banco-santander-and-its-1-billion-business-value-in-ai","status":"publish","type":"post","link":"https:\/\/suricata.la\/en\/banco-santander-and-its-1-billion-business-value-in-ai\/","title":{"rendered":"Banco Santander and its business value of 1,000 million in AI"},"content":{"rendered":"<h2>Table of Contents<\/h2>\n<ul>\n<li><a href=\"#preve-1000-millones-en-valor-por-ia\">1. Forecasts \u20ac1 billion in value from AI<\/a>\n<\/li>\n<li><a href=\"#generacion-de-valor-empresarial\">2. Enterprise value generation<\/a>\n<\/li>\n<li><a href=\"#expansion-de-la-base-de-clientes-hasta-210-millones\">3. Expansion of the customer base to 210 million<\/a>\n<\/li>\n<li><a href=\"#contribucion-de-la-inteligencia-artificial-al-crecimiento-de-ingresos\">4. Contribution of artificial intelligence to revenue growth<\/a>\n<\/li>\n<li><a href=\"#acuerdo-con-openai-para-implementar-chatgpt-enterprise\">5. Agreement with OpenAI to implement ChatGPT Enterprise<\/a>\n<\/li>\n<li><a href=\"#ahorros-generados-por-iniciativas-de-ia-en-2024\">6. Savings generated by AI initiatives in 2024<\/a>\n<\/li>\n<li><a href=\"#uso-de-copilotos-de-ia-en-el-centro-de-contacto\">7. Use of AI copilots in the contact center<\/a>\n<\/li>\n<li><a href=\"#plan-de-capacitacion-en-ia-para-empleados\">8. AI training plan for employees<\/a>\n<\/li>\n<li><a href=\"#santander-en-la-era-de-la-ia\">9. Santander in the AI era<\/a>\n<ul>\n<li><a href=\"#impacto-en-el-sector-bancario\">9.1 Impact on the banking sector<\/a><\/li>\n<li><a href=\"#oportunidades-y-retos\">9.2 Opportunities and challenges<\/a><\/li>\n<\/ul>\n<\/li>\n<li><a href=\"#transformacion-en-el-sector-financiero\">10. Transformation in the financial sector<\/a>\n<ul>\n<li><a href=\"#la-inversion-en-ia-como-motor-de-crecimiento\">10.1 Investment in AI as a Growth Engine<\/a><\/li>\n<li><a href=\"#estrategias-de-personalizacion-y-eficiencia\">10.2 Personalization and Efficiency Strategies<\/a><\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<div class=\"ai-article\">\n<h2 id=\"preve-1000-millones-en-valor-por-ia\">Forecasts \u20ac1 billion in value from AI<\/h2>\n<ul>\n<li>Banco Santander estimates it will generate <strong>\u20ac1 billion in additional enterprise value<\/strong> over the next two years thanks to investments in <strong>data and artificial intelligence<\/strong>.  <\/li>\n<li>The bank wants to increase its customer base from <strong>180 million to more than 210 million<\/strong> by the end of <strong>2028<\/strong>.  <\/li>\n<li>AI is integrated as a central lever of its <strong>ONE Transformation<\/strong> strategy, with a focus on <strong>hyper-personalization<\/strong>, commercial productivity, and process <strong>automation<\/strong>.  <\/li>\n<li>Santander has signed an agreement with <strong>OpenAI<\/strong> to roll out <strong>ChatGPT Enterprise<\/strong>, initially for <strong>15,000 employees<\/strong>.  <\/li>\n<li>In <strong>2024<\/strong>, AI initiatives would already have generated <strong>more than \u20ac200 million in savings<\/strong>, with copilots supporting <strong>more than 40%<\/strong> of contact center interactions.<\/li>\n<\/ul>\n<blockquote>\n<p><strong>Enterprise value with data and AI<\/strong><br \/>\n&#8211; What \u201centerprise value\u201d means here: in the coverage it is described as a combination of <strong>more revenue<\/strong> and <strong>lower costs<\/strong>, attributable to investments in <strong>data and AI<\/strong>.<br \/>\n&#8211; Time horizon: Finextra frames it as <strong>additional value over the next two years<\/strong>; in parallel, other coverage based on Reuters frames it as an ambition to <strong>exceed \u20ac1 billion annually by 2028<\/strong> (a target, not a result).<br \/>\n&#8211; Source of the figure: <strong>Finextra<\/strong> (editorial note on Santander\u2019s plan) and <strong>Reuters<\/strong> (via TradingView) report the expectation\/target in those terms.<\/p>\n<\/blockquote>\n<h2 id=\"generacion-de-valor-empresarial\">Enterprise value generation<\/h2>\n<p>Banco Santander has put a number on its bet on artificial intelligence: it expects to obtain that additional enterprise value through investments in <strong>AI<\/strong>. According to what was published by Finextra, the expectation is set in <strong>the next two years<\/strong> and combines <strong>revenue growth<\/strong> and <strong>cost reduction<\/strong>. The objective combines two classic impact pathways: <strong>boosting revenue<\/strong> and <strong>reducing costs<\/strong>.<\/p>\n<p>The institution presents itself as a \u201cglobal digital bank with branches\u201d and argues that efficiency improvements will come from <strong>simplifying products and processes<\/strong>, greater collaboration among global businesses, and scaling <strong>common technology platforms<\/strong>.<\/p>\n<blockquote>\n<p><strong>Enterprise value levers<\/strong><br \/>\nHow enterprise value is usually \u201cmade up\u201d in data and AI initiatives (and how it fits with what Santander describes):<br \/>\n1) Revenue (growth)<br \/>\n&#8211; Levers: hyper-personalization, better conversion, greater<\/p>\n<\/blockquote>\n<\/div>\n<p>linkage, better service.<br \/>\n&#8211; Signals: more completed digital sign-ups, more products per customer, greater channel usage, better retention.<br \/>\n2) Costs (efficiency)<br \/>\n&#8211; Levers: end-to-end automation, process simplification, common platforms, frontline productivity.<br \/>\n&#8211; Signals: less time per transaction, less rework\/errors, more self-service, more interactions resolved at first contact.<br \/>\n3) Deployment speed (scale)<br \/>\n&#8211; Levers: component reuse, data governance, collaboration across geographies.<br \/>\n&#8211; Signals: time to production release, adoption by teams, consistency across countries\/units.<\/p>\n<\/blockquote>\n<h2 id=\"expansion-de-la-base-de-clientes-hasta-210-millones\">Expansion of the customer base to 210 million<\/h2>\n<p>Santander\u2019s growth plan involves expanding its base from <strong>180 million<\/strong> customers to <strong>more than 210 million<\/strong> by the end of <strong>2028<\/strong>. The thesis is that a more digital\u2014and more personalized\u2014experience can accelerate acquisition and engagement, especially if friction is reduced in sign-ups, service, and day-to-day operations.<\/p>\n<p>In parallel, the bank aims to sustain <strong>mid-single-digit<\/strong> revenue growth and to reduce total costs every year, with a view to exceeding <strong>\u20ac20 billion in profit<\/strong> in 2028.<\/p>\n<blockquote>\n<p><strong>From goal to action<\/strong><br \/>\nA practical path to connect the \u201c+30M customers\u201d goal with actions (and checkpoints):<br \/>\n1) Acquisition<br \/>\n&#8211; Action: segmentation and more relevant messaging (offers\/context).<br \/>\n&#8211; Checkpoint: acquisition cost and conversion rate by channel.<br \/>\n2) Onboarding (sign-up)<br \/>\n&#8211; Action: reduce steps and time; smart assistance for frequent questions.<br \/>\n&#8211; Checkpoint: % of sign-ups completed and average sign-up time; main drop-off points.<br \/>\n3) Engagement (recurring use)<br \/>\n&#8211; Action: hyper-personalized journeys (alerts, recommendations, next-best-action).<br \/>\n&#8211; Checkpoint: monthly activity, products per customer, 90\/180-day retention.<br \/>\n4) Service and trust<br \/>\n&#8211; Action: faster and more consistent service (copilots + analytics) without losing human control.<br \/>\n&#8211; Checkpoint: first-contact resolution, complaints about \u201cmisunderstandings\u201d, satisfaction\/NPS.<\/p>\n<\/blockquote>\n<h2 id=\"contribucion-de-la-inteligencia-artificial-al-crecimiento-de-ingresos\">Contribution of artificial intelligence to revenue growth<\/h2>\n<p>Santander positions data and AI as a pillar of its <strong>ONE Transformation<\/strong> strategy, \u201cfully integrated\u201d into the businesses. The stated focus is on:<\/p>\n<ul>\n<li><strong>Hyper-personalized customer journeys<\/strong>, with recommendations and offers better tailored to the context.<\/li>\n<li><strong>Frontline productivity<\/strong> (sales and service teams) through AI-assisted tools.<\/li>\n<li><strong>End-to-end automation<\/strong> of processes, to reduce time, errorsand manual tasks.<\/li>\n<\/ul>\n<p>In retail and business banking, the promise of AI is not just efficiency: it is also <strong>better conversion<\/strong>, greater use of digital channels, and a more continuous relationship with the customer based on behavioral signals and needs.<\/p>\n<blockquote>\n<p><strong>Revenue impact indicators<\/strong><br \/>\n\u201cGrounded\u201d indicators to see whether AI is driving revenue (beyond the talk):<br \/>\n&#8211; Hyper-personalization<br \/>\n  &#8211; \u2610 Increases offer conversion (by segment\/channel)<br \/>\n  &#8211; \u2610 Boosts activation of digital functionalities (recurring use)<br \/>\n  &#8211; \u2610 Improves retention (churn) in cohorts exposed to personalized journeys<br \/>\n&#8211; Front-line productivity<br \/>\n  &#8211; \u2610 Less time per interaction (without quality dropping)<br \/>\n  &#8211; \u2610 More sales\/cases handled per hour in commercial teams<br \/>\n  &#8211; \u2610 Fewer escalations and less \u201crework\u201d due to incomplete information<br \/>\n&#8211; End-to-end automation<br \/>\n  &#8211; \u2610 More processes completed without manual intervention<br \/>\n  &#8211; \u2610 Fewer operational errors and fewer repeated incidents<br \/>\n  &#8211; \u2610 Measurable reduction in unit costs per transaction<\/p>\n<\/blockquote>\n<h2 id=\"acuerdo-con-openai-para-implementar-chatgpt-enterprise\">Agreement with OpenAI to implement ChatGPT Enterprise<\/h2>\n<p>In August, Santander reached an agreement with <strong>OpenAI<\/strong> to roll out <strong>ChatGPT Enterprise<\/strong>. The launch began with an initial group of <strong>15,000 employees<\/strong>, framed within the ambition to become an \u201c<strong>AI-native<\/strong>\u201d bank\u2014that is, with AI structurally integrated into operations and decision-making.<\/p>\n<p>The move aligns with an industry trend: moving from isolated pilots to <strong>enterprise tools<\/strong> with cross-cutting reach (operations, technology, business, customer service, and internal support).<\/p>\n<blockquote>\n<p><strong>Internal rollout of ChatGPT Enterprise<\/strong><br \/>\n&#8211; Reported initial scope: deployment of <strong>ChatGPT Enterprise<\/strong> for a first group of <strong>15,000 employees<\/strong>.<br \/>\n&#8211; Why it matters for \u201cAI-native\u201d: it is not just a customer-facing chatbot; it is often used as an internal tool for <strong>search and synthesis<\/strong>, <strong>drafting<\/strong>, <strong>analysis support<\/strong>, <strong>assistance with procedures<\/strong>, and <strong>task acceleration<\/strong> across multiple areas.<br \/>\n&#8211; Context of the statement: Finextra\u2019s coverage frames it within the ONE Transformation strategy; and Santander has also publicly communicated its collaboration with OpenAI as part of its data and AI approach.<\/p>\n<\/blockquote>\n<h2 id=\"ahorros-generados-por-iniciativas-de-ia-en-2024\">Savings generated by AI initiatives in 2024<\/h2>\n<p>According to the bank, in <strong>2024<\/strong> AI initiatives generated <strong>more than 200 million euros in savings<\/strong> (a figure attributed in the coverage to statements by the chief data and AI officer, Ricardo Mart\u00edn Manj\u00f3n). The figure suggests that<\/p>\n<p>part of the return is already materializing before the 2028 horizon, especially in automation and operations support.<\/p>\n<p>The data also reinforces the argument that AI is not limited to \u201cflashy\u201d generative AI use cases: savings usually come from <strong>process optimization<\/strong>, applied analytics, and automation of repetitive tasks.<\/p>\n<blockquote>\n<p><strong>Savings from Data &amp; AI 2024<\/strong><br \/>\n&#8211; Reported metric: <strong>\u201cmore than 200 million euros in savings\u201d in 2024<\/strong>, attributed in the coverage to Ricardo Mart\u00edn Manj\u00f3n (Chief Data &amp; AI Officer), a role directly responsible for data and AI initiatives.<br \/>\n&#8211; Where those savings typically materialize (consistent with what is described in the article):<br \/>\n  &#8211; automation of repetitive tasks and reduction of cycle times,<br \/>\n  &#8211; fewer errors\/rework thanks to better information capture,<br \/>\n  &#8211; assistance to agents\/teams to resolve faster.<br \/>\n&#8211; Important for interpreting the figure: it is presented as a <strong>reported internal result<\/strong> in press coverage; it does not publicly break down what portion corresponds to each initiative.<\/p>\n<\/blockquote>\n<h2 id=\"uso-de-copilotos-de-ia-en-el-centro-de-contacto\">Use of AI copilots in the contact center<\/h2>\n<p>Santander states that <strong>AI copilots<\/strong> already support <strong>more than 40%<\/strong> of interactions in its <strong>contact center<\/strong>, one of the areas where automation and real-time assistance often have a direct impact on response and resolution times.<\/p>\n<p>In Spain, the institution uses <strong>Speech Analytics<\/strong> to process <strong>10 million voice calls a year<\/strong>. Among the cited operational effects: the system <strong>auto-completes<\/strong> records in the CRM, improves service, and frees up <strong>more than 100,000 hours per year<\/strong> for higher-value tasks.<\/p>\n<blockquote>\n<p><strong>Integrated Customer Service Flow<\/strong><br \/>\nTypical \u201ccopilot + voice analytics + CRM\u201d flow (and checkpoints for it to work in production):<br \/>\n1) Interaction intake<br \/>\n&#8211; Call\/inquiry enters the contact center.<br \/>\n&#8211; Checkpoint: correct identification of the reason (intent) and the customer.<br \/>\n2) Real-time assistance<br \/>\n&#8211; The copilot suggests responses, steps, and relevant data during the conversation.<br \/>\n&#8211; Checkpoint: the agent validates; if confidence is low, procedural guidance and escalation are prioritized.<br \/>\n3) Speech Analytics and extraction<br \/>\n&#8211; The conversation is analyzed to detect topics, compliance, and key data.<br \/>\n&#8211; Checkpoint: correct extraction rate (avoid erroneous \u201cauto-fills\u201d).<br \/>\n4) CRM logging<br \/>\n&#8211; Auto-completion of fields and a summary of the interaction.<br \/>\n&#8211; Checkpoint: quality audit (samples), reduction of rework, and consistency of the record.<br \/>\n5) Operational outcome<br \/>\n&#8211; Less after-call time and more hours freed up (such as the <strong>+100,000 hours\/year<\/strong> cited).<br \/>\n&#8211; Checkpoint: real improvement in resolution and satisfaction, notonly in speed.<\/p>\n<\/blockquote>\n<h2 id=\"plan-de-capacitacion-en-ia-para-empleados\">AI training plan for employees<\/h2>\n<p>To sustain the scaling leap, Santander has begun to <strong>adapt AI training<\/strong> by role and market. In addition, it plans to launch this year a <strong>mandatory AI training plan for the entire workforce<\/strong>, with the aim of standardizing the use of tools and raising internal capabilities.<\/p>\n<p>Training becomes critical in an \u201cAI-native\u201d model: it is not enough to deploy technology; routines, criteria, and ways of working must change across commercial, operational, and support areas.<\/p>\n<blockquote>\n<p><strong>Training and adoption by roles<\/strong><br \/>\n\u201cBy role\u201d training checklist (what it should cover and how to measure adoption):<br \/>\n&#8211; Customer service\/contact center teams<br \/>\n  &#8211; \u2610 Use of the copilot (search, scripts, summaries)<br \/>\n  &#8211; \u2610 Verification criteria before logging in CRM<br \/>\n  &#8211; \u2610 Metric: reduction in after-call work without a drop in quality<br \/>\n&#8211; Sales teams<br \/>\n  &#8211; \u2610 Interpretation of recommendations (next-best-action) and limits<br \/>\n  &#8211; \u2610 Assisted writing and proposal preparation<br \/>\n  &#8211; \u2610 Metric: conversion and productivity per hour<br \/>\n&#8211; Operations\/back office<br \/>\n  &#8211; \u2610 Task automation and exception handling<br \/>\n  &#8211; \u2610 Quality control and change traceability<br \/>\n  &#8211; \u2610 Metric: cycle times, errors, and rework<br \/>\n&#8211; Technology\/data<br \/>\n  &#8211; \u2610 Data best practices, model evaluation, and monitoring<br \/>\n  &#8211; \u2610 Security and access control in corporate tools<br \/>\n  &#8211; \u2610 Metric: incidents, deployment times, and compliance with internal standards<\/p>\n<\/blockquote>\n<h2 id=\"santander-en-la-era-de-la-ia\">Santander in the AI era<\/h2>\n<h3 id=\"impacto-en-el-sector-bancario\">Impact on the banking sector<\/h3>\n<p>Santander\u2019s roadmap reflects a broader transformation of the sector: banking is increasingly competing on <strong>digital experience<\/strong>, <strong>operational efficiency<\/strong>, and the ability to turn data into decisions. In that context, AI becomes infrastructure: a cross-cutting component that affects everything from customer service to back-office automation.<\/p>\n<p>If the bank manages to scale common platforms and standardize processes, it can gain deployment speed and consistency across geographies, a key factor for a global group.<\/p>\n<h3 id=\"oportunidades-y-retos\">Opportunities and challenges<\/h3>\n<p>The opportunity is clear: <strong>more productivity<\/strong>, <strong>better service<\/strong>, and <strong>growth<\/strong> supported by personalization. The challenge is clear too: moving from use cases to <strong>sustained transformation<\/strong>, with real adoption by teams, integration with existing systems, and rigorous measurement of impact.<\/p>\n<p>The goal of <strong>\u20ac1 billion<\/strong> in business value ultimately serves as a litmus test: if AI is integrated into the business, the return should<\/p>\n<p>to be seen both in the income statement and in the customer experience.<\/p>\n<blockquote>\n<p><strong>Scaling AI: opportunities and challenges<\/strong><br \/>\nOpportunities vs. practical challenges when scaling AI in a global bank:<br \/>\n&#8211; Opportunity: efficiency and recurring savings<br \/>\n  &#8211; Trade-off: if you automate \u201ctoo fast,\u201d exceptions and rework increase; data\/CRM quality becomes critical.<br \/>\n&#8211; Opportunity: better experience and personalization<br \/>\n  &#8211; Trade-off: personalization requires strong data governance and consistency across channels; otherwise, the customer perceives inconsistent messages.<br \/>\n&#8211; Opportunity: team productivity (copilots)<br \/>\n  &#8211; Trade-off: real adoption depends on training, incentives, and trust; without metrics, usage may remain \u201ccuriosity.\u201d<br \/>\n&#8211; Opportunity: common platforms and scale<br \/>\n  &#8211; Trade-off: integrating with existing systems can be the bottleneck; value arrives when the change is reflected in end-to-end processes.<\/p>\n<\/blockquote>\n<h2 id=\"transformacion-en-el-sector-financiero\">Transformation in the financial sector<\/h2>\n<h3 id=\"la-inversion-en-ia-como-motor-de-crecimiento\">Investment in AI as a Growth Engine<\/h3>\n<p>Santander\u2019s strategy suggests that investment in AI is no longer justified only by innovation, but by <strong>profitable growth<\/strong>: acquisition, greater engagement, and efficiency. The bank aims for AI to be a scale multiplier, not a set of isolated tools.<\/p>\n<h3 id=\"estrategias-de-personalizacion-y-eficiencia\">Personalization and Efficiency Strategies<\/h3>\n<p>The combination of <strong>hyper-personalization<\/strong> and <strong>automation<\/strong> points to a model where the customer receives faster, more relevant responses, while the institution reduces unit costs. In banking, that equation usually translates into improvements in the efficiency ratio and the ability to compete on price and service.<\/p>\n<h3 id=\"el-futuro-de-la-banca-un-enfoque-basado-en-datos\">The Future of Banking: A Data-Driven Approach<\/h3>\n<p>The underlying message is that the banking of the future will be <strong>data-driven<\/strong>: more predictive decisions, more automated processes, and more conversational channels. Santander is betting that this change, well executed, can become a competitive advantage measurable in customers, revenue, and costs.<\/p>\n<p>\u201cBanco Santander and its \u20ac1 billion business value in AI\u201d shows how AI stops being a pilot and becomes a measurable lever for efficiency, automation, and a better experience. At Suricata Cx we closely follow these types of deployments because they confirm that real value appears when AI is integrated into omnichannel operations with human oversight and clear metrics, not just in the technology itself.<\/p>\n<p><strong>Scope of this note:<\/strong> the article summarizes what Finextra reported on objectives, figures, and lines of work (ONE Transformation, the rollout of ChatGPT Enterprise, and the cited operational metrics). The reading is\n<\/p>\n<p>focuses on which specific levers are mentioned (automation, frontline productivity, and voice analytics) and on how they connect to measurable outcomes.<\/p>\n<p><em>The figures and targets mentioned are understood as publicly shared goals or expectations, not as guaranteed results. Some operational metrics are cited as they have been communicated and may vary or be updated with new disclosures. In corporate AI, the final impact is uncertain and often depends on actual adoption, integration with processes, and data quality.<\/em><\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>Table of contents 1. Forecasts 1 billion in value from AI 2. Business value generation 3. Expansion of the customer base to 210 million 4. Contribution of artificial intelligence to revenue growth 5. Agreement with OpenAI to implement ChatGPT Enterprise 6. Savings generated by AI initiatives in 2024 7. Use of AI copilots in &#8230; <\/p>\n<div><a href=\"https:\/\/suricata.la\/banco-santander-y-su-valor-empresarial-de-1-000-millones-en-ia\/\" class=\"more-link\">Read More<\/a><\/div>\n","protected":false},"author":2,"featured_media":2808,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_yoast_wpseo_metadesc":"","content_central_editor_notes":"","yoast_description":"","footnotes":""},"categories":[48],"tags":[1425,1421,1427,1426,1428],"class_list":["post-2817","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-noticias","tag-ai-investment","tag-banco-santander","tag-customer-base-expansion","tag-enterprise-value","tag-financial-sector-transformation"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.3 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Banco Santander and its business value of 1,000 million in AI - Suricata Cx<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/suricata.la\/en\/banco-santander-and-its-1-billion-business-value-in-ai\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Banco Santander and its business value of 1,000 million in AI - Suricata Cx\" \/>\n<meta property=\"og:description\" content=\"Table of contents 1. Forecasts 1 billion in value from AI 2. Business value generation 3. Expansion of the customer base to 210 million 4. Contribution of artificial intelligence to revenue growth 5. Agreement with OpenAI to implement ChatGPT Enterprise 6. Savings generated by AI initiatives in 2024 7. 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