{"id":44657,"date":"2025-07-14T10:36:13","date_gmt":"2025-07-14T08:36:13","guid":{"rendered":"https:\/\/implema.se\/uncategorized\/five-steps-to-get-started-with-an-ai-first-strategy\/"},"modified":"2026-02-16T11:05:14","modified_gmt":"2026-02-16T10:05:14","slug":"five-steps-to-get-started-with-an-ai-first-strategy","status":"publish","type":"post","link":"https:\/\/implema.se\/en\/news\/five-steps-to-get-started-with-an-ai-first-strategy\/","title":{"rendered":"Five Steps to get Started with an AI First Strategy"},"content":{"rendered":"\n

Working AI First means that artificial intelligence is not seen as a business add-on, but as an integrated engine for innovation, automation, and decision support. But how do you go from vision to practice? Here, Implema’s Jesper Lindsten shares concrete steps that organizations can take to begin their journey toward an AI First strategy. <\/strong>Many organizations want to use AI but get stuck in unclear ambitions or technology-centered pilot projects without clear business connections. Instead, an AI First strategy requires a long-term and strategic approach where data, business needs, and technical solutions work together from the start. Here are five steps to help you move from insight to actual change: <\/p>\n\n

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1. Identify business processes where AI can create value <\/h3>\n\n

Start by taking inventory of your organization’s most important processes. Focus on finding areas where AI can make a real difference \u2013 in efficiency, accuracy, or customer value. Ask yourselves questions like:\u2022 Where are there large amounts of data that aren’t being used effectively?\u2022 Which work tasks are repetitive, rule-based, and manual?\u2022 Where are many routine decisions made that could be automated?\u2022 Where do we interact with customers \u2013 and can AI contribute to faster, more personalized service?Example areas could be customer support (AI-driven assistants), marketing (segmentation and predictive analysis), logistics (demand forecasting), or document management (NLP-based interpretation of contracts or reports).<\/p>\n\n

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2. Build a sustainable and accessible data foundation<\/h3>\n\n

AI requires data, but not just any data. It must be relevant, accessible, organized, and understandable. Many AI projects fail because the data foundation is fragmented or insufficient. Start by:\u2022 Ensuring access to the right data in the right format, from the right sources\u2022 Understanding how data is collected, stored, and quality assured\u2022 Eliminating silos and creating seamless data flows\u2022 Establishing clear data governance \u2013 with responsibilities, roles, and security policies<\/p>\n\n

This step is often the most time-consuming, but also the most crucial. AI without reliable data is like an engine without fuel. <\/p>\n\n

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3. Establish strategy, responsibility, and governance for AI <\/h3>\n\n

For AI to have real impact, it needs to have a place in the organization’s overall strategy, not just in the IT department. This means that management and business need to be as involved as data and technology experts. Ask clear questions:\u2022 What is the primary problem or opportunity AI should address?\u2022 How do we define success \u2013 in ROI, precision, savings, customer satisfaction?\u2022 Who owns the AI initiatives? What interaction is required between IT, business, and data analysis? \u2022 What does our policy for responsible and ethical AI look like?<\/p>\n\n

Consider forming a cross-functional AI council or team, where business leaders, technical specialists, and management participate in governance and prioritization.<\/p>\n\n

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4. Start small – test, learn, adjust, and scale up <\/h3>\n\n

Going AI First isn’t about building a huge project from the start. On the contrary. Start small, focused, and measurable. Identify an area with a clearly defined problem and sufficient data maturity. Examples of pilot projects:\u2022 Predictive demand analysis in the supply chain\u2022 AI-based customer service bot handling common questions\u2022 Document classification with NLP in legal or HR\u2022 Recommendation systems in e-commerce or service sales<\/p>\n\n

The goal is to quickly demonstrate value, learn from results, improve the model, and then scale up to other processes or business areas.<\/p>\n\n

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5. Integrate AI into the business model and company culture<\/h3>\n\n

In the long term, the goal is for AI to not just exist in specific projects, but to become a natural part of how the company operates and thinks. This requires both technical and cultural integration. This includes:\u2022 AI becomes a fundamental requirement in product development and service design\u2022 Decision-making is supported by AI-generated insights in real-time\u2022 Employees are trained in data understanding and AI competency\u2022 AI is viewed as a strategic asset \u2013 not a technical cost item<\/p>\n\n

An AI First organization combines technology, data, business, and culture in an intelligent ecosystem \u2013 where learning, adaptation, and automation are built into the core.<\/p>\n\n

AI First is not just a technical strategy, it’s a new way of thinking, organizing, and developing. By gradually building the right data foundation, choosing relevant use cases, involving the right competencies, and integrating AI into core operations, companies can take a decisive step toward intelligent, scalable, and future-proof business development. <\/p>\n\n

Related Content: AI First \u2013 a strategy for data-driven growth and innovation – News – Implema<\/a>Why data is the key to succeeding with AI in business systems – News – Implema<\/a>How AI is transforming business systems – News – Implema<\/a><\/p>\n\n

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Working AI First means that artificial intelligence is not seen as a business add-on, but as an integrated engine for innovation, automation, and decision support. But how do you go from vision to practice? Here, Implema’s Jesper Lindsten shares concrete steps that organizations can take to begin their journey toward an AI First strategy. Many […]<\/p>\n","protected":false},"author":22,"featured_media":39860,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","footnotes":""},"categories":[2518],"tags":[2583],"class_list":["post-44657","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news","tag-ai"],"acf":[],"_links":{"self":[{"href":"https:\/\/implema.se\/en\/wp-json\/wp\/v2\/posts\/44657","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/implema.se\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/implema.se\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/implema.se\/en\/wp-json\/wp\/v2\/users\/22"}],"replies":[{"embeddable":true,"href":"https:\/\/implema.se\/en\/wp-json\/wp\/v2\/comments?post=44657"}],"version-history":[{"count":0,"href":"https:\/\/implema.se\/en\/wp-json\/wp\/v2\/posts\/44657\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/implema.se\/en\/wp-json\/wp\/v2\/media\/39860"}],"wp:attachment":[{"href":"https:\/\/implema.se\/en\/wp-json\/wp\/v2\/media?parent=44657"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/implema.se\/en\/wp-json\/wp\/v2\/categories?post=44657"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/implema.se\/en\/wp-json\/wp\/v2\/tags?post=44657"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}