As artificial intelligence (AI) matures, the concept of “AI First” has gained traction in both the tech world and broader business strategy discussions. It is no longer just a technical choice but a business strategy that fundamentally affects how organizations design their products, processes, and decision models. But what exactly does an AI First strategy entail, which companies is it a good strategy for, and when is the right time to invest? Here, Implema’s Pernilla Öhlén shares her insights.
“AI First” as a strategic principle or explicit business strategy is a concept that began to gain widespread attention around 2016–2017, partly due to Google’s declaration of moving from mobile first to AI first. This marked a shift:
• From developing products for smartphones to developing products where AI is the core.
• From adding AI features as an afterthought to designing entire solutions from the ground up with AI as the driving force.
Since then, more companies and organizations—not just in tech—have started using AI First as an overarching strategy for innovation, competitive advantage, and efficiency.
What Does AI First Mean?
AI First means that AI is not a complement but a starting point. It’s not just about having AI as a feature but building the business around it. Instead of asking “How can we add AI to our product?”, AI First organizations ask:
“If we start with AI, how would we design this service, business model, or process chain?”
This means AI is used to:
• Automate complex or repetitive decisions
• Generate insights from large datasets (prediction, classification, segmentation)
• Create adaptive, self-learning systems
• Drive product development through iterative data analysis and model improvement
Technically, this often involves an ecosystem of machine learning (ML), NLP, image recognition, generative models (like GPT), and integrated MLOps frameworks.
Examples of AI First in Practice
• Google was among the first to proclaim an AI First strategy where search, translation, advertising, and product recommendations are all based on AI models.
• Spotify builds its recommendation systems on deep learning and uses AI in product development, user analysis, and advertising.
• Tesla develops self-learning models for autonomous driving and fleet intelligence, where each car contributes data to a central model.
Which Companies should Consider AI First?
AI First suits organizations that:
• Have large amounts of data: historical, real-time, or user-generated
• Operate in digital environments where decisions and interactions can be continuously optimized
• Have processes with high demands for scalability and automation
• Want to drive differentiation through intelligent, adaptive functionality
Industries where AI First can be particularly relevant include:
• Financial services (risk analysis, fraud detection, investment strategies)
• E-commerce and marketing (personalized experiences, churn prediction)
• Healthcare (diagnostics, resource optimization)
• Logistics and manufacturing (predictive maintenance, optimization)
• Media/entertainment (generative content, user engagement)
When is AI First the Right Strategy?
AI First is particularly suitable for companies in a fast-changing, data-rich environment where competitive advantages are built through faster decisions, automation, and customer adaptation. For AI First to deliver real value, it requires not only access to data but also a business logic that can leverage continuous optimization and learning.
The strategy suits organizations that:
• Are already digital or in the process of digital transformation
• Have the ability to collect, structure, and analyze data in real-time
• Want to differentiate through intelligent functionality rather than price or volume
• Have the conditions to work iteratively and cross-functionally—between IT, business, and data analysis
For smaller organizations or companies in more stable, low-change industries, AI First can also be relevant—but often as a focused initiative rather than a comprehensive strategic shift.
Challenges
Implementing AI First is not just a technical journey; it’s an organizational change, where companies need to:
• Ensure data quality and governance
• Build internal AI competence and cross-functional teams
• Establish a responsible AI policy around ethics, transparency, and bias
• Integrate AI into both the business model and tech stack—not as an isolated initiative
AI First – a Future Strategy that Requires Maturity
AI First is not a trend but an expression of a fundamental shift in how some companies build competitive advantages. For organizations with the right data foundation, tech ambition, and willingness to change, AI First can mean a significant step in the business itself with faster innovation and stronger customer relationships. But it also requires clear goals, responsible AI leadership, and the ability to operationalize advanced technology into real business value—and not least, order and high quality in their data.
