When I look at today’s fastest‑growing startups and boldest enterprises, a clear trait stands out: Plenty of them are AI-first. Might be because AI is “the thing” now and everybody want a piece of the pie, but what does that really mean and how can traditional businesses emulate their success?
In this article, I’ll unpack what makes AI-first companies tick and highlight lessons that startups and enterprises alike can put into action, without requiring encyclopaedic technical knowledge.
AI-First Is a Mindset, Not Just a Label
An AI-first company isn’t simply one that deploys a few bots or automates a process. It sees AI as the centrepiece of its strategy and operations. As Pranav Prakash puts it: “it infuses intelligence into every layer of the organisation, from product design and internal operations to customer experience and business strategy.” getnoan medium
Consider Duolingo: its CEO Luis von Ahn emphasised that being AI-first isn’t about replacing human roles but boosting efficiency and creativity, embedding AI scrutiny into performance reviews and workflows. ft
That’s an intentional culture shift, not a bolt-on tool.
Leadership That Walks the Talk
AI-first leadership doesn’t just talk strategy, they demonstrate it daily. Medium contributor on AI-first transformation described how leaders use AI themselves, like drafting emails with generative tools every morning to build fluency and credibility. linkedin
It’s no surprise that Harvard Business Review notes executive sponsorship as a key driver for real AI success. hbr
If leaders aren’t using AI tools themselves, how can the rest of the organisation take them seriously? This reminds me of the NORAI Connect courses, free top-of-the-line AI education the leadership can use to first upskill themselves for knowing what they’re talking about and then upskilling their entire teams to get things done more efficiently. Start here.
Data & Experimentation as Core Capabilities
AI-first companies view data not as an asset, but they treat it as infrastructure. Bessemer’s “six imperatives” argue that true AI-first firms build a “data advantage”, one rooted in smart collection, synthesis and model development. bvp
Take Owkin, the French biotech startup, for example: It uses federated learning so sensitive healthcare data remains local, yet models still gain strength from multiple institutions. wikipedia
This isn’t just smart tech, it’s a cornerstone of their business model.
Embed AI, Don’t Bolt It On
AI-first companies integrate intelligence within their DNA, not just as add-ons. Sherpa.ai, a Spanish startup, embeds its conversational assistants across devices, including cars and smartphones, making AI a seamless part of daily life. wikipedia
By contrast, legacy firms often layer AI on top, bolted-on and siloed, which limits impact. AI-first ventures design systems around AI, and this makes all the difference.
Invest in Talent & Collaboration
Building an AI-first org demands diverse talent, not just data scientists, but cross-functional teams that combine domain expertise with AI fluency. Connexions between tech, product, marketing, legal, these are the places where real innovation happens.
Both startups and enterprises need to invest in upskilling and breaking down silos, or risk creating AI ghettos that don’t scale.
Launch with Small Bets, Scale with Evidence
AI-first agility doesn’t mean rushing blind. Many successful AI-native companies begin with small, focused pilots, then scale once they’ve proven value. businessinsider bvp
Moveworks, for example, started by automating internal IT support. That niche success laid the groundwork for expanding into customer HR and finance workflows. wikipedia
Enterprises should follow suit: Pilot -> learn -> scale.
Focus on Real-World Outcomes, Not Buzz
AI-first companies obsess over measurable value, improved efficiency, faster workflows, stronger user experiences. Harvard Business Review highlights that firms succeeding with AI define data governance, cross-functional communication and clear ROI pathways. In EU, we also need to think about the GDPR and the EU AI Act.
AI isn’t magic, it’s a tool. And CEOs and founders who treat it as such avoid hype and deliver results.
Governance & Ethics by Default
Ethical, secure AI isn’t optional, it’s essential. AI-first builders bake governance into design: privacy controls, explainability, bias checks. Platforms like NORAI RAG Bot are built with full GDPR and the EU AU Act compliance in mind so customers can trust them from the start. NORAI RAG Bot
By doing this ahead of regulatory pressure, AI-first firms gain credibility and long-term resilience.
Build Moats Around Data & Models
AI-first companies stand out when their data advantage translates into defensible models. For instance, Cohere trains LLMs on internal datasets to craft tailored enterprise solutions, creating intellectual property buttons competitors can’t press.
Enterprises that tie AI to proprietary first-party data – be it customer engagement, operations, or vertical-specific metrics – can replicate this moat.
Embrace Responsibly and Iterate
Finally, being AI-first means embracing agile, iterative cycles: Test, measure, learn. Many AI-native firms publicise their missteps, adjust and share learnings, as do platforms like Duolingo, Shopify and Box. washingtonpost
Enterprises must build in feedback loops and tolerances for small-scale failure. That’s how you accelerate innovation responsibly.
Final Thoughts
Most startups chase AI to fuel growth; enterprises adopt it to stay relevant. But those who truly behave like AI-first firms, by embedding intelligence, culture, data strategy and governance, are the ones who will reshape industries.
I’m convinced that blending the agility of startups with the rigor of enterprises can create the next wave of AI leaders, right here in Europe. It’s within our grasp.
Let’s learn from those who forged the AI-first path – and build our own.
Victor A. Lausas
Chief Executive Officer
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