The financial agenda for 2026 is already taking shape around three priorities: AI impact, digital quality, and internal capabilities.
Showing the real impact of AI, protecting the quality of digital products while accelerating time to market, and empowering people with new capabilities. At Abstracta, we see these as three of the priorities that will carry the most weight in the financial agenda for 2026.
The financial conversation around 2026 has already changed its tone. Grandiose announcements have lost momentum. In their place, a more concrete demand is emerging: to justify investments, sustain software quality in complex environments, and help people work better with artificial intelligence.
At that intersection, several discussions are unfolding at once:
- How can organizations adopt AI without getting stuck in endless pilots?
- How can they accelerate delivery without opening the door to more defects, more rework, and more strain?
- How can they strengthen internal capabilities so technology doesn’t depend on just a few people or isolated tools?
To help read that landscape, we brought together the perspectives of Gustavo Rodríguez Pintado, Manager in the Testing and Implementation area at Banco República (BROU), and Mario Ernst, CEO and founder at Evolution Labs. We also added the perspective of Matías Reina, Co-CEO at Abstracta.
Priorities in the Financial Industry for 2026
Among the issues shaping the financial agenda for 2026, three are starting to stand out: AI’s impact on the business, the quality of digital products, and people empowered with new capabilities.
1. Adopting AI while showing business impact
The first priority is no longer simply to adopt AI, but to be able to demonstrate what it is useful for and what results it drives.
Over the past few months, many organizations have moved forward with pilots, assistants, licenses, and different kinds of experiments. Now a new stage is beginning. The discussion is turning toward impact: where value is created, which initiatives are worth scaling, and how to prevent investment from growing faster than results.
Mario Ernst points precisely to that issue: “The main question I get from different organizations is: Where does AI add value? Where should AI be incorporated so that it is actually worth it? It takes sensitivity to clearly understand the pain points in the market today and the inefficiencies they are generating.”
Mario emphasizes: “The first thing that needs to be structured is a plan for scaling projects with artificial intelligence.”
Gustavo Rodríguez Pintado adds a more operational perspective, closely tied to what it means to bring these capabilities into production:
“There is no doubt that implementing generative AI functions in production processes will be a major challenge in 2026, both in terms of installation, configuration, implementation, and control.”
That nuance matters, because an organization may have enthusiasm, budget, and competitive pressure, and still lack a clear adoption logic. When that happens, AI becomes a collection of scattered efforts. There is movement, but it becomes difficult to show results.
For Matías Reina, the priority is to break that cycle early: AI adoption needs metrics, context, and a visible connection to business goals. When that relationship is missing, investment loses strength and the conversation quickly becomes hollow.
At Abstracta, we see this tension up close in teams that want to improve delivery with AI, but are still defining where it makes sense to apply it, how to govern it, and which indicators can help measure its real impact. At that point, the conversation stops being narrowly technological and moves into the territory of business, visibility, and decision-making.
2. Protecting the Quality of Digital Products While Accelerating Time to Market
The second priority was already on the table, but in 2026 companies are beginning to understand the weight it truly carries. “It is easy to accelerate time to market with AI. The real issue is preserving or improving quality,” Matías Reina stresses.
In the financial sector, accelerating delivery or pushing AI-driven automation without protecting quality can damage the experience and force organizations to correct course. Production defects appear, support teams come under pressure, integrations fail, more time is spent fixing than moving forward. And the experience suffers exactly where the business needs stability and trust in technology.
As one example, in 2025 Klarna’s CEO admitted to Reuters that fintech had gone too far in its use of AI to cut costs and was redirecting that strategy to improve its services and products.
Gustavo Rodríguez Pintado describes one of the layers where this type of tension becomes most visible:
“From a technology perspective, some priorities remain constant even if they are not new. One example is the increasing automation of connections with vendors and government agencies to speed up procedures, verify the identity of individuals and companies, and eliminate the exchange of physical documents.”
That point has significant depth and is closely tied to product quality. Every new interconnection promises greater agility, but it also adds dependency between systems, cross-validation processes, sensitive data, and greater exposure to risk.
In those contexts, software quality begins to influence daily operations, delivery speed, and the ability to sustain change without damaging the user experience.
Mario Ernst adds another key point when he shifts the conversation toward the customer: “The solution is not in us, no matter how powerful we may be. The solution lies in understanding the customer.”
Quality is also at stake there. When the focus gets trapped in deadlines, tools, or internal urgency, teams can lose sight of what truly affects users, which frictions deserve the most attention, and which changes need stronger validation before reaching production.
Gustavo adds a warning that is especially relevant for banking. When what is moving is money, the quality of the digital product is also measured in process reliability, security, and the trust the institution is able to sustain while introducing new technologies.
“The asset in motion is money, so AI-driven automated actions leave no room for the well-known AI ‘hallucinations.’ What will prevail is security over speed in incorporating these new technologies into production processes, since trust in its working processes is the most valuable capital a bank possesses.”
For Matías Reina, this tension will take a central place throughout 2026: “Accelerating time to market cannot become an invitation to live with more fragility. Protecting software quality is part of how the business is protected while moving faster, especially in organizations where every digital product touches revenue, operational risk, or customer experience.”
3. Empowering People with AI
The third priority cuts across the other two. AI does not deliver results simply because it is available. Simply put, it takes judgment to use it, context to integrate it into daily work, and internal capabilities so that it does not end up reduced to a noisy tool with little real effect.
“People are key. For them to feel motivated and empowered is essential for projects to succeed,” Matías Reina highlights.
Mario Ernst points out: “We need a mindset shift to create new competencies. Today, a lot of AI licenses are being rolled out massively across institutions and countries, but people don’t know what to do with them.”
Gustavo Rodríguez Pintado adds a signal that is appearing more and more often on the technology agenda:
“There is a lot of discussion about the implementation of specialized agents that help and enhance people’s work, both on the business side and in internal processes. AI presents tremendous opportunities to improve the efficiency of many institutions’ processes. BROU is working on understanding the scope and uses of AI, as well as identifying strong use cases for its implementation.”
All of these perspectives fit together well, and all are necessary to truly empower people in their use of AI. On the one hand, a basic need remains open: helping people understand where to use these capabilities, with what criteria, and to solve real problems. On the other hand, interest is growing in specialized agents and more concrete applications.
These principles also shape how we approach Abstracta Intelligence, our AI platform for enterprise environments, and Tero, our open-source framework for context-aware agents. The goal is to help teams build capacity, gain visibility, and improve productivity across real QA and engineering workflows.
“AI can unlock concrete improvements in efficiency, but that doesn’t justify rushed adoption,” Matías emphasizes. “Empowering people with AI means providing context, strengthening judgment, and supporting work with secure systems inside real workflows, where decisions still remain in human hands.”
A More Demanding Agenda, and a Far Less Symbolic One
These three priorities reinforce one another:
AI needs visible impact to sustain credibility and investment.
Speed needs quality so it does not lead to more errors and higher operating costs.
People need context, judgment, and new capabilities for technology to make a concrete contribution to their work.
That is why the 2026 financial agenda is beginning to take shape around a much higher bar. It is no longer enough to test, announce, or simply show activity. Today, the focus is shifting toward results, execution, and internal capability.
At Abstracta, we work precisely at that intersection. We support organizations where software quality has a direct impact on the business, risk, and customer experience. We do it through a combination of human expertise, AI agents, governance, and visibility applied to concrete delivery challenges.
The agenda is already underway. The difference will lie in who manages to turn these priorities into decisions that are better connected to the business and into a stronger foundation for continued growth.
About Abstracta
With nearly 2 decades of experience and a global presence, Abstracta is a technology company that helps organizations deliver high-quality software faster by combining AI-powered quality engineering with deep human expertise.
Our expertise spans across industries. We believe that actively bonding ties propels us further and helps us enhance our clients’ software. That’s why we’ve built robust partnerships with industry leaders, Microsoft, Datadog, Tricentis, Perforce BlazeMeter, Saucelabs, and PractiTest, to provide the latest in cutting-edge technology.
If you’re looking for a partner in financial software development, we invite you to explore our solutions and case studies.
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