Conversations about artificial intelligence in financial services have recently taken on a familiar tone of skepticism. Questions are surfacing around ROI, pilot fatigue, and whether enterprise enthusiasm has outpaced tangible results. From the outside, it can be tempting to lump AI into the same category as past technology trends that promised transformation but delivered uneven outcomes.
That viewpoint overlooks a more consequential reality. What’s unfolding across financial services is less a speculative surge and more a period of structural change. AI is moving from experimentation into operational relevance, marking a shift away from headline-grabbing pilots toward long-term impact. In fact, Deloitte found financial services companies have doubled worker access to AI in just one year growing from 30% to 62% of workers now equipped with sanctioned AI tools.
When technologies fundamentally reshape how financial organizations operate, early adoption is rarely tidy. Organizations test broadly, tolerate overlap, and prioritize learning over polish. We saw this pattern with cloud adoption, core enterprise platforms, and the evolution of digital payments. AI is following a similar trajectory.
From Novelty to Necessity: Empowering the Subject Matter Expert
Interest in AI hasn’t waned, rather expectations have evolved. Finance and fintech leaders are no longer satisfied with novelty use cases. Instead, they’re asking how AI fits into highly regulated environments, how it scales responsibly, and how it supports critical business processes, from risk management to global tax compliance.
One of the clearest indicators that AI is here to stay is the depth of change happening across the technology ecosystem itself. Innovation is advancing simultaneously across infrastructure, data platforms, and orchestration layers. Short-lived trends rarely drive this level of coordinated evolution across an entire stack. As a result, AI is increasingly being designed into the backbone of financial operations rather than added as a surface-level enhancement.
The focus is also shifting toward areas where precision, transparency, and resilience matter most. In these settings, AI does not replace existing systems outright. Instead, it augments them by reducing friction, accelerating insight, and handling the heavy data processing that allows human experts to operate at scale.
This distinction matters. The value of AI is not defined by how autonomous it becomes, but by how effectively it amplifies and scales human expertise. When teams use AI to handle complexity and surface what truly matters, professionals gain more capacity to focus on judgment, strategy, and higher order decision making.
The Systematic Risk of Standing Still
Ironically, the greater risk for many financial organizations today is not moving too quickly; it’s failing to move at all. According to McKinsey, nearly two-thirds of organizations have not yet begun scaling AI across the enterprise. Meanwhile, core business expectations are already shifting. Reporting cycles are shrinking, compliance is becoming more continuous, and user interfaces are increasingly conversational and agent-driven. AI may not be the sole driver of these changes, but it is accelerating them.
Organizations that delay adoption until the landscape feels settled may find that partners, regulators, and customers have already moved on. Structured, machine-ready data is becoming table stakes, and oversight bodies are demanding faster, more granular visibility into financial activity. At that point, modernization becomes reactive and significantly more costly.
This challenge is particularly acute in areas defined by complexity, such as finance and tax. Constant regulatory change, cross-border operations, and the need for defensible outcomes place growing strain on manual workflows. Applied thoughtfully, AI offers a way to absorb that complexity by streamlining repetitive work and aligning data, decisions, and documentation across global systems.
Trust as a Catalyst for Scale
One reason this phase feels different from earlier cycles is the emphasis on trust. Governance, transparency, and human accountability are increasingly being designed alongside capability, not bolted on later. Rather than slowing progress, these guardrails make meaningful deployment possible.
When financial institutions understand how AI systems reach conclusions and audit their outputs, the technology becomes viable in high-stakes environments. That confidence is what allows AI to move from promise into practice..
What’s happening now is not a peak driven by hype. It’s a quieter, more consequential transition toward systems that can evolve as financial complexity increases. The payoff will be measured in steadier operations, earlier insight, and platforms that scale under pressure.
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Chris Zangrilli, VP of Technology Strategy, Vertex
Chris Zangrilli is Vice President of Technology Strategy at Vertex Inc. In his role, he leads the technology strategy and innovation efforts, applying emerging technologies to understand the art of the possible to drive growth. He has held several technology leadership roles responsible for the architecture and development of SaaS solutions. He brings 30 years of technology and strategic expertise, driving value to customers through tax technology solutions.
