6 days ago
Generative AI As Infrastructure: A Productivity Playbook For Banks
AI productivity in banks
OBSERVATIONS FROM THE FINTECH SNARK TANK
As banks explore the value of artificial intelligence, one fact is becoming increasingly clear: generative AI is emerging as core infrastructure for operational transformation. A recent report by Cornerstone Advisors and commissioned by Hapax, provides a framework for how banks and credit unions can move beyond experimentation and begin capturing tangible productivity gains from generative AI.
Moving From Generative AI Experimentation to Enterprise Value
Many financial institutions are in a reactive posture toward generative AI—running pilot programs, deploying isolated tools, or setting general usage policies. While this exploratory stage is important, generative AI must ultimately be treated as foundational infrastructure, similar to broadband connectivity or cloud architecture.
Institutions that approach generative AI this way are seeing measurable improvements in productivity and staff enablement in three key areas: 1) knowledge management; 2) process and workflow optimization; and 3) personal productivity.
The most immediate and impactful application of generative AI has been streamlining internal knowledge access. Employees at every level—from compliance teams to branch staff—routinely spend valuable time searching for policy documents, procedural guidance, or answers to routine questions.
By implementing 'super search' platforms powered by generative AI, these institutions have enabled staff to find accurate, real-time answers in seconds rather than minutes. Marine Credit Union, for example, reported a 20–25% increase in employee productivity after adopting such a solution.
These tools reduce cognitive friction, shorten onboarding and training times, and enhance overall employee experience. Rather than replacing staff, AI acts as an expert assistant—always available, up to date, and context-aware.
Some institutions are beginning to integrate AI more deeply into critical business workflows. In particular, compliance, vendor due diligence, treasury operations, and HR policy development are emerging as high-impact use cases.
By automating tasks like document summarization, risk flagging, or policy drafting, AI is helping teams reduce cycle times, improve accuracy, and free up capacity for higher-order decision-making. In doing so, AI becomes less of a tool and more of a collaborator embedded within the daily operational rhythm.
At First State Community Bank, initial concerns about uncontrolled AI usage were replaced by a structured adoption framework that included employee training, clear governance, and change management support. The outcome: improved productivity and rising employee interest in new AI applications.
Measuring and Managing the Value of Generative AI
The report emphasizes the need for rigorous measurement when deploying AI. Banks and credit unions should treat each AI-enabled workflow like a capital investment—with clear ROI metrics, performance tracking, and continuous feedback loops.
Key performance indicators (KPIs) should be defined upfront, including:
Institutions like Visa and JPMorgan Chase have published productivity figures tied to AI initiatives: Visa reported $40 billion in fraud prevention using AI, and JPMorgan's COIN platform automated 360,000 hours of legal document review. While community banks and credit unions may operate at a smaller scale, the principle remains the same—measurable value is the standard for success.
Building a Scalable Generative AI Adoption Strategy
Rather than launching full-scale AI transformations, take a stepwise approach:
This incremental approach reduces risk while accelerating organizational learning and adoption.
Generative AI as Infrastructure
Just as no one says 'we're doing a cloud project' anymore—because cloud is now assumed to be part of modern IT architecture—the same will soon be true for generative AI. Too often, AI is viewed as an 'app'—something a business can bolt on to fix a specific problem. AI isn't a project—it's infrastructure because it:
Treating AI as infrastructure means rethinking how you: 1) Budget—moving from 'AI project line item' to 'platform-level investment;' 2) Organize—creating cross-functional teams for AI model ops, governance, and integration; 3) Govern—establishing AI lifecycle management, bias detection, and explainability standards; and 4) Train—shifting the focus from 'how to use the tool' to 'how to build on top of it.'
Organizational Considerations: Trust, Culture, and Governance
Perhaps most importantly, the report underscores that AI transformation is as much about people as it is about technology. Trust is a prerequisite. Employees must understand how AI works, what it's doing, and how it benefits them. Leaders must invest in change management, education, and the creation of AI champions throughout the organization.
At Magnifi Financial, a culture of transparency and hands-on experimentation helped overcome initial skepticism. Employees began with small use cases, validated outcomes, and built confidence organically. Over time, AI became not just a technical initiative, but a workforce enabler.
A Strategic Inflection Point For Generative AI
Financial institutions now face a strategic decision: treat AI as an isolated technology, or embrace it as foundational infrastructure. The former risks stagnation. The latter opens the door to continuous productivity improvement, talent enablement, and better outcomes for customers.
The institutions profiled in the report demonstrate that real, measurable value is within reach—achievable today through focused deployment, strong governance, and a commitment to change.
As generative AI matures, the competitive advantage will go to those who embed it deeply and intelligently across their organizations. For banks and credit unions ready to move from hype to impact, this generative AI playbook provides a valuable guide.
For a complimentary copy of the report The Playbook for Generative AI-Driven
Productivity Improvement, click here.
