AI is no longer just about efficiency: according to McKinsey, it can double R&D speed and unlock up to half a trillion dollars a year globally, with a double-digit EBIT lift for banks that lead adoption. Three levers (ideation, simulation, optimization) and four strategic steps.
In Latin America's fast-moving financial sector, digital transformation in banking is no longer optional. The pressure to innovate is relentless. Yet many leaders face a frustrating reality: bringing new products to market is an increasingly slow, costly and risky process. According to a compelling McKinsey article , Research and Development (R&D) productivity is in decline. But Artificial Intelligence (AI) has arrived to radically change this paradigm. AI is no longer just for automating processes; it is the strategic engine that can double the pace of innovation , unlocking up to half a trillion dollars in annual value globally. For banks in LATAM, understanding and applying this revolution is key to survival and leadership. Key takeaways for financial leaders: The problem: Traditional innovation is increasingly expensive and slow (Eroom's Law). The solution: AI can double R&D speed, generating more and better financial products. The impact: The economic potential translates into a double-digit EBIT increase for the companies that lead adoption. The strategy: Success depends not only on technology, but on redesigning the organization and choosing the right partners. How to break innovation barriers at your bank with AI? McKinsey identifies three practical ways AI revolutionizes the product life cycle. Let's see how they apply directly to the challenges of a bank in our region. 1. Exponential idea generation: beyond the conventional Human teams, however talented, tend to think within certain frames; that's why when we want to come up with new ideas we talk about "thinking outside the box." Generative AI breaks these frames, exploring a universe of possibilities. Imagine using AI to: Design hyper-personalized investment portfolios for new customer segments. Create alternative credit scoring models for the unbanked population, a key market in LATAM. Generate dozens of interface and user experience prototypes for your next app, identifying the most intuitive and effective one before a designer draws the first line. To go deeper on this point, it's crucial to know how to build a successful banking channel, where user experience is everything. As McKinsey shows, AI can propose "outside the box" solutions, similar to AlphaGo's famous "Move 37," which defied human logic and won the game. 2. Accelerated evaluation: simulate success to minimize risk Launching a failed product is a hard blow, and not only financially. AI makes it possible to create "digital twins" of your customers and markets to test ideas in a virtual, safe and ultra-fast environment. Instead of months of market research, you can simulate in hours: The adoption of a new insurance product. The impact of a new fee structure on customer retention. The market's reaction to a new digital marketing campaign. This ability to "test before building" dramatically accelerates time-to-market and helps prevent the hidden costs in software development that arise from planning errors and late validation. 3. Smart optimization of R&D operations AI also acts as a tireless research assistant for your strategic teams: It analyzes thousands of interactions (chats, calls, reviews) to detect patterns and unmet needs that turn into opportunities. It synthesizes market intelligence so your product managers make data-driven decisions, not gut-driven ones. It automates the creation of regulatory documentation , freeing your team to focus on innovation. From theory to practice: 4 strategic steps to capitalize on AI Technology is only one piece of the puzzle. Capturing its real value demands deep organizational change. McKinsey highlights four keys: Move decisively and scale: endless pilots don't generate profitability. It's essential to have a strategy to quickly scale successful AI initiatives across the entire operation. Redesign the organization for agility: adopting AI requires more than a new department . It demands redesigning workflows, breaking silos and fostering a culture of agile experimentation. Build a core competency in AI models: your bank will need to make strategic decisions about which technology to develop in-house and which to acquire. This dichotomy is similar to the choice between custom vs. off-the-shelf software, where personalization and control are fundamental to competitive advantage. Choose the right technology partner: the complexity of AI in the financial sector makes choosing an ally critical. You don't need a vendor, you need a technology partner that understands your business challenges, speaks the same language as banking and has the technical expertise to build robust, secure solutions. The future of banking is built today The era of incremental innovation is coming to an end. Artificial Intelligence offers an unprecedented opportunity for Latin American banks to make a quantum leap, creating products faster, making smarter decisions and consolidating their leadership in a constantly evolving market. At Delto we build the future of banking. We are not just developers; we are the strategic partners that help the most important financial institutions in LATAM turn the potential of AI into a real competitive advantage. Contact one of our specialists and let's start designing your AI innovation roadmap.
How does AI impact innovation at banks? According to McKinsey, AI can double R&D speed and unlock up to half a trillion dollars in annual value globally. For banks, that translates into a double-digit EBIT increase for those who lead adoption, creating more and better financial products in less time.
Where in the product cycle does AI apply in banking? Across three levers: exponential idea generation (e.g. alternative credit scoring for the unbanked population), accelerated evaluation with digital twins that simulate adoption in hours instead of months, and optimization of R&D operations by analyzing thousands of interactions to detect opportunities.
Beyond technology, what's needed to capture AI's value? Four strategic steps: move decisively and scale beyond endless pilots, redesign the organization for agility by breaking silos, build a core competency in AI models by deciding what to build and what to acquire, and choose the right technology partner that understands banking.