#151: Short Takes: What changes when a fintech becomes AI-first?
W FINTECHS NEWSLETTTER #151
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We are facing a structural shift in how companies operate, scale, and compete, driven by the widespread adoption of artificial intelligence as a core organizational element. In edition 141, I wrote that the next major neobank might have fewer employees than Nubank, which currently operates with fewer than 8,000 people, compared to traditional banks that still have over 50,000 employees. This is because AI agents are changing the way traditional and operational tasks are performed.
In a recent report by BCG, the consultancy showed how “AI-first” models are enabling AI to reach the core of business operations, achieving unprecedented levels of productivity, efficiency, and growth through lean teams, accessible technologies, and a complete reconfiguration of operational and financial models. Unlike incremental digitization, being AI-first means redesigning processes, reallocating investments, decentralizing technological decision-making, and fostering an organizational culture based on speed, adaptability, and collaboration between humans and autonomous agents. This has allowed fintechs and startups to overcome historical barriers with an agility that challenges traditional models.
The report begins by warning that the democratization of artificial intelligence, driven by the availability of foundational models and autonomous agents, is lowering the barriers to entry for new players and redistributing competitive power. Companies that once relied on large structures and human capital are being outperformed by lean, highly specialized teams that scale impressively using AI-based tools. Cases like Cursor, which reached $100 million in annual recurring revenue with fewer than 20 people in just two years, illustrate this radical shift.
The new economic equation of AI-first companies is marked by a sharp decline in the cost of knowledge-intensive tasks and faster delivery times. While a market research report that used to take hours can now be done in minutes, the cost of a one-off consulting task can drop from $5 to $0.30 with AI agents. This creates a scenario in which value per employee increases dramatically, with some AI-first companies generating up to 35 times more revenue per employee than their traditional counterparts.
Another key point is the transformation of the operational model. Unlike traditional digitization, which automates parts of existing processes, the AI-first approach requires a complete rebuild of operations around AI agents. The hierarchical structure becomes flatter, workflows are optimized through the reuse of AI-based processes, and the role of IT changes radically—from a support function to the strategic foundation that sustains the development and governance of AI solutions.
Team composition also changes. The report shows that AI-first companies operate with 50% to 70% fewer employees, but offer salaries 1.5 to 2 times higher for the most qualified talent. The new workforce focuses less on execution and more on judgment, strategy, and collaboration with AI. The competition for specialized talent intensifies, and the productivity of top performers is amplified by tools that eliminate repetitive tasks and support complex decision-making.
Another factor being accelerated is the redefinition of sources of competitive advantage. In a world saturated with AI-generated content, what sets a company apart is no longer scale or output, but brand trust, direct access to customers, proprietary data, and talent capable of unlocking AI’s full potential. The ownership of assets like patents, relationships, and human expertise becomes more valuable than physical infrastructure or large operational teams.
Fintechs, in particular, are proving to be fertile ground for adopting the AI-first approach. The report highlights a U.S.-based fintech that already allocates more than 14% of its revenue to technology investments, surpassing even major digital platforms. The ability to personalize offers in real time, accurately predict risk, and automate customer interactions makes fintechs natural contenders for leadership in this new paradigm.
Another fintech mentioned reached $10 million in ARR with just nine employees in two years. This shows that in the financial sector, where compliance and user experience are fundamental, AI enables agile operations without compromising regulatory requirements. Solutions based on autonomous agents can scale support, detect fraud, orchestrate operations, and generate predictive insights for strategic decisions.
The journey to becoming an AI-first organization begins with five essential steps. The first is to create an AI-centered business agenda with clear value-driven goals. Next, leaders must adopt AI in their daily routines, not just as users but as examples of transformation within the company. The third step is to anticipate workforce impacts and invest in upskilling. Then, demonstrate results in high-value initiatives to build momentum and internal support. Finally, resources should be reallocated to what works, ensuring sustainability and scalability.
The recommended tech structure involves a multi-layered architecture: infrastructure, data, AI, transactions, and a smart business layer. IT maintains the framework, but business units take the lead in executing solutions, with the autonomy to test, iterate, and scale.
The presence of artificial intelligence will become increasingly natural in the daily operations of companies. Instead of large operational structures, teams will be smaller and more specialized, focusing on system integration, efficient data usage, and the automation of routine tasks. AI will come to be seen as a common tool, used in decision-making processes, customer service, fraud prevention, and product adjustments. This transition will not be immediate or uniform, but it will likely consolidate gradually as tools become more accessible, costs more predictable, and use cases clearer within operations.
Until the next!
Walter Pereira
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Disclaimer: The opinions expressed here are solely the responsibility of the author, Walter Pereira, and do not necessarily reflect the views of the sponsors, partners, or clients of W Fintechs.