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In the financial sector, the adoption of artificial intelligence is entering a more mature phase, in which the ability to rethink work, skills, and operating models is becoming increasingly urgent. AI—Digital Workforce embodies this very evolution: the gradual integration of human capital, digital tools, intelligent automation, and decision-support systems into the processes of financial institutions.
Banks, insurance companies, and other market participants are currently operating in an environment characterized by increasing competitive pressure, rapid technological advancement, regulatory complexity, and the need to improve productivity and service quality. In this context, AI is playing an increasingly significant role in internal operations, collaboration models, talent management, and the employee experience.
The real shift lies in the transition from a model of isolated experimentation to a broader transformation of the workforce. AI is being integrated into teams in the form of virtual assistants, knowledge management tools, personalized learning platforms, analytics for skills management, and automation solutions to support operational activities. The value generated, however, depends on the ability of institutions to integrate technology, governance, data, and organizational culture into a coherent framework.
From Process Digitization to the Augmented Workforce
AI—Digital Workforce ranks among the most significant internal trends for the financial sector, as it directly impacts the organizational models and internal capabilities of financial institutions. Its adoption changes the way people access information, make decisions, collaborate with other departments, and develop new skills. This trend is therefore not limited to improving operational efficiency; it also affects the quality of work and the organization’s ability to adapt to a constantly evolving environment.
According to experts’ assessment of Digital Trends 2026, AI—Digital Workforce—has a significant impact, particularly in areas where data infrastructure, digital skills, and sufficiently mature processes are already in place. “ Advanced Analytics ” and “Digital Claims” emerge as areas particularly affected by this trend: the former due to greater familiarity with data, models, and analytical tools; the latter due to the potential of automation and AI to reduce processing times, optimize processes, and improve the customer experience.
The path to adoption, however, varies. The most advanced sectors are already experimenting with forms of human-machine collaboration, while other functions have yet to establish the enabling conditions: data quality, system interoperability, widespread digital skills, security safeguards, and AI governance models. In this sense, readiness does not depend solely on technological availability, but on the organizational capacity to support change.
Metaskills become the true driver of scale
One of the most significant findings to emerge from the research Cetif concerns the role of skills in the transformation of the workforce. The widespread adoption of AI requires skills that go beyond the purely technical: metaskills—that is, the ability to learn continuously, understand the context, and critically interpret the outputs generated by intelligent systems—are becoming central.
The HR function is playing an increasingly strategic role in this process, contributing to the design of work models, the mapping of skills, and the facilitation of transformation. Eighty-seven percent of professionals specializing in HR data analytics work within the HR department—a figure that signals the gradual internalization of analytical skills in people management and strengthens the ability to interpret HR data in a way that more closely aligns with organizational dynamics.
The organizational model is also shifting toward more integrated structures. In fact, 63% of organizations have introduced cross-functional teams, confirming the gradual breakdown of traditional silos. HR governance, coordination, change management, and digital initiatives are becoming areas where different areas of expertise must work together to integrate AI into organizational processes.
Governance, culture, and organizational flexibility drive adoption
AI—the Digital Workforce—requires leadership capable of guiding change, collaborative organizational models, and appropriate governance mechanisms. This transformation involves a reevaluation of roles, responsibilities, oversight mechanisms, and the ways in which people interact with intelligent systems.
IT spending in the HR sector is projected to grow by 5% in 2025, confirming that digital investments in HR are increasingly driven by business cases, expected ROI, and economic sustainability. This figure indicates a more measured phase of adoption, in which HR technologies, AI, and GenAI are also evaluated based on their ability to generate organizational value over time.
Governance therefore becomes an enabling condition. Clear policies, data quality control, defined accountability for AI-generated outputs, and change management strategies that make adoption understandable and feasible for people are needed. Without these elements, AI risks remaining confined to local initiatives, with a limited impact on operational models.
GenAI Enters the Software Factory
Another area of evolution concerns the application of GenAI to software development. The CIO Strategy HUB reports that GenAI is making its way into the software development process, particularly to support the evolution of the application portfolio. The areas of greatest value are application maintenance, technical documentation, and new development, where AI can reduce the loss of know-how, accelerate operational activities, and strengthen oversight of legacy systems.
This insight is particularly relevant to the financial sector, which is characterized by complex architectures, core systems that are still heavily staffed, and growing pressure to modernize applications. In the context of application maintenance, GenAI can aid in understanding layered systems and facilitate corrective or evolutionary interventions. In technical documentation, it helps make knowledge more accessible and transferable. In new development projects, it enables greater speed in coding, testing, and validation activities.
The managerial implication is clear: AI—the Digital Workforce—also pertains to an organization’s technological capabilities. Developers, business analysts, and IT teams are evolving toward roles that are more focused on supervision, orchestration, and quality control, while GenAI is becoming an integrated tool within delivery processes.
Toward a More Adaptable, Informed, and Data-Driven Workforce
In the medium to long term, the AI Digital Workforce will be increasingly linked to financial institutions’ ability to build skill-centric organizations, where roles, career paths, and learning models evolve alongside technology. The adoption of AI must be approached as a transformation journey that involves processes, governance, leadership, and corporate culture.
The most mature organizations will be those capable of balancing automation and human judgment, productivity and accountability, and personalization and control. The widespread adoption of AI in internal processes will require clear safeguards regarding security, privacy, data quality, transparency of outputs, and human oversight—especially in contexts that affect people, customers, and sensitive decisions.
AI—the Digital Workforce—thus ushers in a new era in which competitive advantage will depend on the ability to make artificial intelligence an integral part of daily work. AI has already become part of the team in many organizations: the challenge will be to transform it into a stable, well-governed capability focused on generating sustainable value.