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 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:
1. Identify business processes where AI can create value
Start by taking inventory of your organization’s most important processes. Focus on finding areas where AI can make a real difference – in efficiency, accuracy, or customer value.
Ask yourselves questions like:
• Where are there large amounts of data that aren’t being used effectively?
• Which work tasks are repetitive, rule-based, and manual?
• Where are many routine decisions made that could be automated?
• Where do we interact with customers – 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).
2. Build a sustainable and accessible data foundation
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:
• Ensuring access to the right data in the right format, from the right sources
• Understanding how data is collected, stored, and quality assured
• Eliminating silos and creating seamless data flows
• Establishing clear data governance – with responsibilities, roles, and security policies
This step is often the most time-consuming, but also the most crucial. AI without reliable data is like an engine without fuel.
3. Establish strategy, responsibility, and governance for AI
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:
• What is the primary problem or opportunity AI should address?
• How do we define success – in ROI, precision, savings, customer satisfaction?
• Who owns the AI initiatives? What interaction is required between IT, business, and data analysis?
• What does our policy for responsible and ethical AI look like?
Consider forming a cross-functional AI council or team, where business leaders, technical specialists, and management participate in governance and prioritization.
4. Start small – test, learn, adjust, and scale up
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:
• Predictive demand analysis in the supply chain
• AI-based customer service bot handling common questions
• Document classification with NLP in legal or HR
• Recommendation systems in e-commerce or service sales
The goal is to quickly demonstrate value, learn from results, improve the model, and then scale up to other processes or business areas.
5. Integrate AI into the business model and company culture
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:
• AI becomes a fundamental requirement in product development and service design
• Decision-making is supported by AI-generated insights in real-time
• Employees are trained in data understanding and AI competency
• AI is viewed as a strategic asset – not a technical cost item
An AI First organization combines technology, data, business, and culture in an intelligent ecosystem – where learning, adaptation, and automation are built into the core.
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.
Related Content:
AI First – a strategy for data-driven growth and innovation – News – Implema
Why data is the key to succeeding with AI in business systems – News – Implema
How AI is transforming business systems – News – Implema
