# How One Regional US Bank Is Using AI to Transform Internal Operations Across Thousands of Employees
A major regional bank in the United States has embraced artificial intelligence on a significant scale, rolling out AI-powered copilots to tens of thousands of staff members. The initiative touches nearly every corner of the organisation, from customer-facing call centres to back-office risk management and software engineering teams.
## From Pilot to Enterprise-Wide Rollout
The bank’s AI journey began with caution. Before making any tools widely available, leadership blocked access to public-facing large language models to protect sensitive internal information. After a careful evaluation of enterprise providers, the bank selected Microsoft Copilot and started with a pilot group of roughly 800 employees.
That pilot paved the way for a much larger deployment. Within roughly a year, more than 15,000 employees were using the AI tool, with the number climbing toward 16,000 out of a total workforce of approximately 22,000. The applications range from drafting emails and reports to summarising lengthy call-centre conversations and generating code.
## AI in Customer Service and Risk Management
One of the most immediate benefits has been in customer service. By using generative AI to summarise call-centre conversations, employees save an estimated six minutes per call. This efficiency gain allows staff to focus on higher-value interactions rather than spending excessive time on note-taking and documentation.
Beyond customer service, the bank is exploring more advanced AI applications in cybersecurity, fraud detection, and portfolio risk monitoring. The bank has also begun using retrieval-augmented generation techniques that draw on internal, governed data to produce more accurate and context-aware AI outputs.
Crucially, every AI-generated output is subject to human review. Employees remain fully responsible for the accuracy and appropriateness of work assisted by AI, a principle that has been codified in the bank’s 2026 Code of Business Conduct and Ethics.
## The Technology Foundation Behind the AI Push
The AI rollout did not happen in isolation. It sits on top of a massive technology transformation that began in 2018. At that time, more than half of the bank’s technology specialists were external contractors. Today, the vast majority of the technology workforce is in-house, with the bank employing approximately 2,000 technologists organised into more than 300 agile teams.
The results of this overhaul are measurable. Technology outages have dropped by more than 80% since 2018, and the number of annual system upgrades has tripled. The bank now completes roughly 65,000 technology releases per year, compared with about 15,000 in 2018. Annual technology spending has also grown substantially, exceeding $1.2 billion in recent years.
Alongside this infrastructure rebuild, the bank invested heavily in data governance. A dedicated data-lineage programme was established to track where information originates, how it is used, and how it moves between systems. The bank also launched a Data Academy that has trained around 2,000 employees in data governance and data skills. An internal repository called Edison houses authoritative documents and policy information, while specialised software from providers like Solidatus and Monte Carlo helps trace data as it flows through databases, applications, and business intelligence systems.
## A Multi-Pronged AI Strategy
The bank’s leadership has outlined three distinct pathways for generative AI adoption. The first is general employee use through tools like Copilot. The second involves embedding AI capabilities directly into existing applications, many of which are supplied by third-party vendors. The third focuses on building proprietary AI systems around the bank’s own data and processes, with early use cases targeting repetitive operational tasks, software development, fraud prevention, and cyber defence.
This layered approach allows the bank to capture quick wins while also investing in longer-term, differentiated AI capabilities that leverage its unique data assets.
—
## Frequently Asked Questions
**How many employees at the bank are currently using AI tools?**
More than 15,000 employees, representing roughly 70% of the bank’s total workforce of approximately 22,000.
**What specific tasks does AI assist with?**
AI is used for drafting emails and reports, summarising call-centre conversations, generating code for software developers, identifying customer needs, and flagging potential portfolio risks.
**Why did the bank initially restrict access to public AI tools?**
Leadership blocked public large language models because employees could accidentally input sensitive or proprietary company information into external, public-facing services.
**How does the bank ensure AI outputs are accurate?**
All AI-generated work must be reviewed by a human employee. This requirement is embedded in both operational policies and the bank’s formal Code of Business Conduct and Ethics.
**What other banks are doing something similar?**
Other major US banks have also expanded generative AI. For example, one large bank launched an internal AI platform for over 200,000 employees, while another deployed a generative AI assistant for customer service agents that reduces call times.
**How does the bank’s technology transformation relate to its AI strategy?**
The AI rollout builds on a technology overhaul that started in 2018, which dramatically improved infrastructure reliability, reduced outages, and strengthened data governance capabilities.
—
## Conclusion
The bank’s experience offers a compelling case study in how regional financial institutions can adopt AI at scale without compromising security or accountability. By combining a strong technology foundation with rigorous data governance and a clear human-in-the-loop approach, the institution has managed to boost productivity, improve customer service, and explore cutting-edge AI applications in risk and security — all while keeping employees firmly in charge of the final output.
The journey underscores a broader lesson: successful AI adoption in banking is not just about choosing the right tools, but about building the infrastructure, governance frameworks, and workforce capabilities that make those tools safe and effective.
Thank you for reading
