The 2026 Financial Tech Shift:
How Financial Institutions Are Rebuilding Tech Capabilities During AI Modernization
Executive Summary
As financial institutions embrace AI at unprecedented speed, a new tension is emerging: rapid technological advancement is outpacing risk controls, governance frameworks, and measurement standards. While 78% of organizations report using AI in at least one business function, the breadth of deployment remains limited. Banks, in particular, stand to capture hundreds of billions in productivity and innovation value, yet many lack the infrastructure and readiness to scale responsibly.
The AI modernization wave brings a silent but rising consequence: operational and compliance debt. Many initiatives are moving ahead without clear oversight, measurable ROI, or alignment with long-term talent development. As agentic AI systems proliferate in banking, concerns about misapplied tools, governance gaps, and regulatory blind spots are growing.
This report explores the underlying drivers, risks, and workforce challenges of financial AI modernization, providing a forward-looking view of how institutions can build sustainable, compliant, and human-centered transformation strategies.
The New Era of AI Modernization in Finance
The period from 2024 onward has marked a pivotal shift in financial technology. Banks are accelerating their adoption of AI technologies, transitioning from legacy systems to modern, cloud-based infrastructures. Platforms such as Snowflake, Azure, and Databricks are replacing outdated mainframes, and AI is increasingly embedded in core functions such as credit risk analysis, fraud detection, and customer engagement.
Several key forces are driving this shift. Competitive pressures in the financial sector have intensified, especially as fintechs and digital-first banks scale rapidly. Regulatory expectations are evolving, with global bodies demanding more transparency, accountability, and responsiveness from AI-powered systems. Meanwhile, the maturity of cloud platforms has reduced barriers to implementation, making AI more accessible and operationally feasible.
Yet, this modernization has outpaced institutional readiness. The EY-Parthenon 2025 GenAI in Banking Survey found that 47% of banking organizations had implemented GenAI tools by 2025, up from just 10% in 2023. However, many of these implementations suffer from inadequate governance frameworks, fragmented data environments, and skills gaps. The Bank Director 2025 Technology Survey underscores this point, with one-third of bank leaders citing ineffective use of data as a major obstacle to success.
Furthermore, Gartner predicts that over 40% of agentic AI projects will be canceled by 2027, often due to hype-driven experimentation lacking real business value. McKinsey’s research reinforces the notion that while adoption is broad, true value realization is limited to organizations that invest in coordinated leadership, structured data strategies, and cross-functional talent development.
Where the Risk Is Shifting
AI has introduced a new spectrum of risk that financial institutions must urgently address. These include:
AI Risk: As AI models become more autonomous, the risks of model bias, lack of explainability, and algorithmic opacity are increasing. Gartner’s analysis warns that agentic AI, when deployed without oversight, can generate misleading or noncompliant outputs that harm trust and hinder scalability. Many banks are still in the process of aligning AI deployments with frameworks such as MRM 2.0 and the NIST AI Risk Management Framework.
Data Risk: A lack of clear data lineage, the rise of shadow datasets, and fragmented governance have compromised data reliability. These issues limit the effectiveness of AI models and create downstream compliance challenges. The EY-Parthenon survey indicates that regulatory and data compliance issues are among the top barriers to effective GenAI deployment.
Operational Risk: The proliferation of tools and vendors has led to operational fragmentation. Many financial institutions now operate complex, poorly integrated ecosystems, which increases the likelihood of project failure, redundancy, and cost overruns. According to Deloitte, only 18% of firms are using AI to optimize talent management, highlighting how even internal functions lag behind in modernization.
Compliance Drift: As AI outputs become more embedded in decision-making, accountability becomes blurred. The Bank Director survey shows that although 66% of respondents have drafted AI usage policies, few have implemented comprehensive governance frameworks. This gap increases the risk of noncompliance and limits the scalability of AI in regulated environments.
Data, Cloud & Tech Infrastructure: The New Foundation
Effective AI transformation is impossible without a strong data and cloud foundation. Many financial institutions are actively migrating to cloud-native platforms such as Snowflake, Databricks, and Azure to unify data environments and enable real-time insights. These moves are critical for enabling scalable, compliant, and performant AI systems.
Yet, migration alone is not enough. According to IBM’s "Voice of the Makers" report, many core modernization efforts stall due to poor data quality, fragmented architecture, and internal misalignment. Most financial institutions still lack unified governance and data quality frameworks, limiting their ability to operationalize AI consistently.
The Saxo Bank case study illustrates a successful transformation: by migrating to Snowflake and Databricks, the bank significantly reduced time-to-insight, improved data governance, and laid the groundwork for AI-enhanced services. This outcome underscores the core principle that AI maturity depends on a well-structured, governed data ecosystem.
McKinsey further emphasizes that AI value can be defined as the product of data maturity and governance readiness. Institutions that neglect these foundations often see elevated costs, delayed deployment, and compliance setbacks.
The Human Gap: Tech Workforce & Capability Rebuild
The transformation brought on by AI requires not just new tools, but a new workforce. As AI systems become integral to compliance, risk, and customer operations, financial institutions need talent who can bridge data science, regulatory understanding, and cloud infrastructure.
However, there is a clear human capability gap. The EY-Parthenon report notes that most banks lack integrated strategies for reskilling, hiring, or building internal academies. While some institutions have made significant investments in workforce development, such examples remain the exception.
The CFA Institute reports that by 2024, 87% of financial institutions had adopted skills-based hiring models. Still, many report difficulties in sourcing talent with AI governance, FinOps, and cloud-native skills. Furthermore, most internal upskilling efforts are reactive and disconnected from long-term talent strategy.
To address this, forward-thinking institutions are launching internal academies (as seen at Citi, TD, and RBC), partnering with workforce development firms, and focusing on building compliance-literate, tech-capable teams.
The Measurement Problem: What’s Missing
As AI modernization accelerates, financial institutions face a significant challenge: measuring what truly matters. Many continue to emphasize speed and volume of deployments rather than long-term, risk-adjusted outcomes.
Best-in-class organizations are beginning to adopt more meaningful KPIs, such as:
- Model bias detection and explainability coverage
- Talent readiness indicators (upskilling rates, internal conversion metrics)
- Risk-to-revenue ratios for modernization initiatives
Deloitte’s insights reveal that only a minority of firms evaluate AI through a performance and governance lens. Instead, deployments are often measured by timelines, with little attention to model performance, workforce readiness, or regulatory resilience.
A key recommendation is for CFOs, CHROs, and CIOs to align on shared transformation metrics that integrate risk, capability, and value. Without these, financial institutions risk falling into a pattern of unchecked experimentation.
Case Spotlights
North American Bank: This institution deployed GenAI to enhance customer service, reducing inquiry resolution times by 40%. However, compliance teams were unable to certify the models for full deployment due to ambiguous ownership of AI outputs. The result was a multi-month delay in production.
Saxo Bank: By consolidating legacy data platforms and migrating to Snowflake and Databricks, Saxo improved cost efficiency and data accessibility. This enabled faster deployment of AI analytics in customer-facing operations and credit decisioning.
Citigroup: In 2025, Citigroup launched internal AI tools across Hong Kong operations, with emphasis on employee training, regulatory transparency, and tool explainability. The initiative serves as a model for responsible AI deployment at scale.
Each spotlight illustrates a core lesson: without governance, measurement, and talent investment, technology cannot deliver sustainable value.
The Road Ahead: 2026–2028 Outlook
The next two years will be defined by institutional responses to the complex demands of AI transformation. We anticipate three major developments:
- Accountability and AI Risk Quantification: Boards will focus on quantifying AI-related risks, ensuring bias detection, and overseeing explainable models. Governance will shift from an IT concern to a strategic imperative.
- ROI Will Depend on People: As cloud and AI technologies become standardized, differentiation will come from the capability and governance literacy of the workforce, not the tools themselves.
- Talent Strategy Will Pivot to Governance Literacy: Training will expand beyond technical skills to include ethical AI use, regulatory fluency, and compliance ownership.
Strategio’s perspective is clear: modernization is not just about platforms, it is a human capability challenge. Success requires institutions to embed governance into talent, data, and culture.
Action Framework for Financial Institutions
Phase 1: Assess - Map AI and data maturity alongside governance readiness to gain risk visibility.
Phase 2: Align - Define business-aligned KPIs and ownership structures to ensure shared accountability.
Phase 3: Build - Develop cross-functional teams with expertise in tech, compliance, and data to ensure sustainable execution.
Phase 4: Measure - Track ROI, talent development, and risk-adjusted performance metrics to enable continuous improvement.
References / Data Sources
McKinsey: State of AI 2025; Banking on GenAI
Gartner: Agentic AI Project Trends
EY-Parthenon: GenAI in Banking Survey 2025
Deloitte: Talent and AI in Financial Services
KPMG: AI in Payments Modernization
IBM: Voice of the Makers Report
CFA Institute: Skills Revolution in Finance
OCC and NIST: Model Risk and AI Governance Guidelines
Snowflake: Saxo Bank Case Study
Reuters: Citigroup AI Rollout