{"id":150692,"date":"2026-09-18T11:04:08","date_gmt":"2026-09-18T11:04:08","guid":{"rendered":"https:\/\/sii.pl\/news-feed\/ai-center-of-excellence-jak-skalowac-ai-z-korzyscia-dla-biznesu\/"},"modified":"2026-09-18T11:17:06","modified_gmt":"2026-09-18T11:17:06","slug":"ai-center-of-excellence-how-to-scale-ai-for-business-value","status":"publish","type":"post","link":"https:\/\/sii.pl\/en\/news-feed\/ai-center-of-excellence-how-to-scale-ai-for-business-value\/","title":{"rendered":"AI Center of Excellence: How to\u00a0scale\u00a0AI for business value?"},"content":{"rendered":"\n<div class=\"wp-block-sii-nsw-container container container-6cc4db56-adc9-4c0b-9bdb-613d32d78d1c\"><style type=\"text\/css\">.container-6cc4db56-adc9-4c0b-9bdb-613d32d78d1c {  }\n                         @media screen and (max-width: 991px) { .container-6cc4db56-adc9-4c0b-9bdb-613d32d78d1c {  } }<\/style>\n<p><strong>More than 40% of projects involving AI agents could be discontinued by 2027 due to rising costs and unclear business value<\/strong>\u00b9<strong>. So, how can AI be scaled effectively? In this article, we explore how Sii Poland\u2019s AI Center of Excellence combines proven solutions, expertise, and governance. Read on to discover how to move from experimentation to scaling AI.<\/strong>&nbsp;<\/p>\n\n\n\n<p>This shows that simply launching more AI initiatives is not enough. Organizations need to <strong>select solutions that deliver real value, validate them in practice, and apply the lessons learned across the business<\/strong>. Otherwise, even successful implementations remain isolated use cases rather than contributing to broader AI capabilities.&nbsp;<\/p>\n\n\n\n<p>Sii Poland\u2019s answer to this challenge is its <strong>AI Center of Excellence (AI CoE)<\/strong>, which draws on experience from various projects and delivery areas to develop solutions, skills, and standards that can be applied in future implementations.&nbsp;<\/p>\n\n\n\n<p>In our previous articles, we explained how the <a href=\"https:\/\/sii.pl\/en\/news-feed\/ai-native-delivery-framework-how-sii-poland-responds-to-market-forecasts\/\" target=\"_blank\" rel=\"noreferrer noopener\">AI-Native Delivery Framework<\/a>, <a href=\"https:\/\/sii.pl\/en\/news-feed\/ai-native-sdlc-in-practice-from-ai-tools-to-measurable-delivery\/\" target=\"_blank\" rel=\"noreferrer noopener\">AI-native SDLC<\/a>, and <a href=\"https:\/\/sii.pl\/en\/news-feed\/how-to-scale-ai-safely-in-enterprise-projects-siis-approach-to-ai-governance\/\" target=\"_blank\" rel=\"noreferrer noopener\">AI governance<\/a> support the effective and secure use of AI in delivery. <strong>The AI CoE brings these elements together into a model that evolves with every new project.<\/strong><\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>A unified approach to scaling AI<\/strong>&nbsp;<\/h3>\n\n\n\n<p>Sii Poland recommends specific solutions and ways of applying AI based on the business objective, technology environment, available data, and risk level. The AI CoE also helps determine which skills teams need, where common standards are required, and which security and control measures should guide the use of AI.&nbsp;<\/p>\n\n\n\n<p>Sii Poland\u2019s AI CoE approach is therefore built on three pillars: <strong>proven solutions, specialists prepared to work with AI, and shared principles for using it safely and responsibly.<\/strong>&nbsp;<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Proven solutions accelerate implementation<\/strong>&nbsp;<\/h3>\n\n\n\n<p>Not every solution developed during a project should immediately become a standard. The AI CoE assesses whether it can deliver value in other use cases, be safely integrated into different environments, and produce measurable results.&nbsp;<\/p>\n\n\n\n<p>This is how Sii Poland develops architectural patterns, modules for recurring tasks, and context packages that provide models with the knowledge they need. Some may eventually evolve into standalone products. One example is <strong>AI Buddy<\/strong>, which helps organize fragmented project documentation and requirements and makes the relevant context available to development and testing teams.&nbsp;<\/p>\n\n\n\n<p>For clients, this means a shorter path from need to solution. Teams can reuse suitable, previously validated components instead of spending time solving problems that have already been thoroughly addressed.&nbsp;<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Scaling AI requires well-prepared people<\/strong>&nbsp;<\/h3>\n\n\n\n<p>Effective AI adoption also depends on the skills of the people who use the technology and take responsibility for the quality of its outputs.&nbsp;<\/p>\n\n\n\n<p>Sii experts are prepared to use AI effectively and safely within the client\u2019s environment. They combine role-specific skills with an understanding of the project context, while the Sii AI Center of Excellence supports their development by incorporating the latest practices and lessons learned from delivery.&nbsp;<\/p>\n\n\n\n<p>Before joining a project, they also become familiar with the client\u2019s environment, including its tools, ways of working, quality-control mechanisms, and governance requirements.&nbsp;<\/p>\n\n\n\n<p><strong>Core AI knowledge \u2192 role-specific skills \u2192 client project preparation<\/strong>&nbsp;<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Security and control built into the process<\/strong>&nbsp;<\/h3>\n\n\n\n<p>Scaling AI requires clear answers to fundamental questions: which data can be used, how model outputs should be assessed, and who is responsible for validating them.&nbsp;<\/p>\n\n\n\n<p>Governance is therefore an integral part of the AI CoE. Shared principles cover data security, testing, quality control, human validation of results, and criteria for approving solutions for use. They also reflect the requirements of the EU AI Act, GDPR, NIS2, and the ISO 27001 and ISO 27018 standards. As a result, teams do not need to redefine these principles for every project, while clients retain control over how AI is used in their environments.&nbsp;<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>What does the client gain?<\/strong>&nbsp;<\/h3>\n\n\n\n<p>Combining ready-to-use solutions, well-prepared specialists, and shared principles delivers tangible benefits:&nbsp;<\/p>\n\n\n\n<ul class=\"wp-block-list is-style-checked-gradient\">\n<li><strong>Faster progress from need to implementation<\/strong> through validated solutions and practices&nbsp;<\/li>\n\n\n\n<li><strong>Consistent quality<\/strong> through shared standards, testing, and human validation&nbsp;<\/li>\n\n\n\n<li><strong>Smoother system handovers<\/strong> through AI-assisted analysis of code, documentation, and requirements during knowledge transfer&nbsp;<\/li>\n\n\n\n<li><strong>Better-prepared teams<\/strong> with skills tailored to their roles and the client\u2019s environment&nbsp;<\/li>\n\n\n\n<li><strong>Greater control over AI use<\/strong> through clearly defined rules for models, data, and code&nbsp;<\/li>\n<\/ul>\n\n\n\n<blockquote class=\"wp-block-quote is-style-nsw-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p>\u201cOne of the greatest challenges in scaling AI across a large organization is the varying maturity of individual teams\u2014in terms of their skills, how they use AI, and the quality of the solutions they adopt. The AI CoE draws on experience from multiple projects to systematically raise this level across the organization,\u201d says <strong>Sebastian Ci\u0119szczyk, AI Center of Excellence Lead at Sii Poland.<\/strong>&nbsp;<\/p>\n<\/blockquote>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>From experimentation to scale<\/strong>&nbsp;<\/h3>\n\n\n\n<p>The AI CoE transfers proven solutions, knowledge, and standards between projects, so teams do not have to start from scratch. This enables clients to scale AI faster, with greater predictability and control.&nbsp;<\/p>\n\n\n\n<p><strong>Want to explore how to scale AI across your organization? <\/strong><a href=\"https:\/\/sii.pl\/en\/contact-us\/\" target=\"_blank\" rel=\"noopener\" title=\"\"><strong>Talk to Sii Poland\u2019s experts.<\/strong>&nbsp;<\/a><\/p>\n\n\n\n<p class=\"has-nsw-p-6-font-size\">\u00b9 Source: Gartner, \u201eGartner Predicts Over 40% of Agentic AI Projects Will Be Canceled by End of 2027\u201d&nbsp;<\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":131,"featured_media":150682,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"tags":[],"class_list":["post-150692","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry"],"acf":[],"aioseo_notices":[],"featured_media_url":"https:\/\/sii.pl\/wp-content\/uploads\/2026\/09\/Sebastian-Cieszczyk-Pressroom-AI.jpg","category_names":[],"_links":{"self":[{"href":"https:\/\/sii.pl\/en\/wp-json\/wp\/v2\/posts\/150692","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sii.pl\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/sii.pl\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/sii.pl\/en\/wp-json\/wp\/v2\/users\/131"}],"replies":[{"embeddable":true,"href":"https:\/\/sii.pl\/en\/wp-json\/wp\/v2\/comments?post=150692"}],"version-history":[{"count":3,"href":"https:\/\/sii.pl\/en\/wp-json\/wp\/v2\/posts\/150692\/revisions"}],"predecessor-version":[{"id":150703,"href":"https:\/\/sii.pl\/en\/wp-json\/wp\/v2\/posts\/150692\/revisions\/150703"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/sii.pl\/en\/wp-json\/wp\/v2\/media\/150682"}],"wp:attachment":[{"href":"https:\/\/sii.pl\/en\/wp-json\/wp\/v2\/media?parent=150692"}],"wp:term":[{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/sii.pl\/en\/wp-json\/wp\/v2\/tags?post=150692"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}