M&T Bank Expands Enterprise AI to 15,000+ Employees

M&T Bank is expanding its use of artificial intelligence across the organization, with AI copilots now available to more than 15,000 employees. The US regional bank is using enterprise AI for a growing range of activities, including customer service, software development, internal operations, cybersecurity, fraud prevention and risk management.

The expansion comes after several years of investment in technology infrastructure and data governance. Rather than treating generative AI as a standalone experiment, M&T Bank has been building the technology and data foundation needed to use AI across its operations.

The bank’s AI applications include summarizing customer-service conversations, preparing reports, generating software code, identifying customer needs and highlighting potential portfolio risks. M&T is also exploring additional uses for agentic AI in areas such as cybersecurity and fraud detection.

M&T Bank AI Adoption Reaches More Than 15,000 Employees

M&T Bank’s enterprise AI rollout has grown significantly since its initial testing phase.

According to previously reported figures, approximately 16,000 of the bank’s roughly 22,000 employees were using Microsoft Copilot by September 2025. Employees have used the technology for everyday tasks such as drafting emails and reports and summarizing call-center conversations.

The bank initially took a cautious approach to public generative AI tools. Employee access to public large language models was restricted because of concerns that sensitive company information could accidentally be entered into external AI services.

M&T subsequently evaluated enterprise AI solutions and selected Microsoft Copilot. The bank began with a pilot involving approximately 800 employees before expanding the technology across a much larger portion of its workforce.

AI Is Reducing Time Spent on Customer-Service Tasks

One of M&T’s practical AI applications involves customer-service operations.

The bank uses generative AI to analyze and summarize call-center conversations. According to the supplied reporting, the technology saves approximately six minutes per call.

Instead of requiring employees to manually review and summarize every conversation, AI can help generate a summary that employees can then review and use as part of their workflow.

Software development is another major AI use case. M&T employees use GitLab tools to assist with code generation, while developers remain responsible for reviewing and validating AI-generated output.

This human-review approach is an important part of the bank’s AI strategy. Employees are not expected to blindly accept AI-generated information or code.

M&T Maintains Human Oversight of AI

M&T Bank’s approach to responsible AI extends beyond individual workflows.

Its 2026 Code of Business Conduct and Ethics requires employees to use approved AI tools and prohibits them from entering confidential, proprietary, customer, employee or regulated information into unauthorized AI systems.

Employees also remain responsible for ensuring that AI-assisted work is accurate and appropriate.

This framework allows the bank to benefit from generative AI while maintaining controls around sensitive financial and customer information.

For a financial institution, this distinction is particularly important because AI systems can potentially interact with highly confidential data. Enterprise AI deployment therefore requires not only useful models but also clear governance, access controls and employee accountability.

Technology Overhaul Created the Foundation for AI

M&T’s current AI strategy is closely connected to a broader technology transformation that began in 2018.

At the beginning of that program, more than half of the bank’s technology specialists were external workers. Today, approximately 80% of its technology workforce is in-house.

M&T now has around 2,000 technologists organized across more than 300 agile teams. The bank has also hired more than 1,000 technology specialists during its transformation.

The organization has replaced dozens of legacy platforms while modernizing its technology infrastructure.

According to the supplied figures, technology outages have declined by more than 80% since 2018, while the number of system upgrades completed annually has increased by approximately 300%.

M&T Technology Spending Tops $1.2 Billion

The bank has also significantly increased its technology investment.

M&T’s technology spending exceeded $1.2 billion in 2025, almost three times its 2017 level.

The pace of technology releases has increased dramatically as well. According to figures cited from Forbes, annual technology releases grew from roughly 15,000 in 2018 to 65,000 in 2025.

This expansion has been led in part by M&T’s technology leadership. The bank’s current technology and operations leadership is responsible for integrating technology capabilities with broader operational functions.

The scale of this transformation provides important context for M&T’s AI rollout. Generative AI is being introduced into an organization that has already spent years modernizing its systems, applications and technology workforce.

Data Governance Is Critical to M&T’s AI Strategy

AI is only as useful as the data supporting it, making data governance another important part of M&T Bank’s transformation.

M&T’s data program expanded alongside its technology overhaul. Chief Data Officer Andrew Foster joined the bank in 2023 and began developing a data-lineage program designed to track where information originates, how it is used and how it moves through different systems.

The data-lineage initiative was not created specifically because of generative AI. Instead, it was developed as a broader capability for understanding and governing the bank’s data environment.

M&T has also established a Data Academy, focused on data governance and data skills. Approximately 2,000 employees have participated in the program.

Edison Helps Organize Authoritative Bank Information

M&T has created an internal repository called Edison, which contains authoritative documents and information about bank policies.

The bank also uses data-lineage technologies from Solidatus and Monte Carlo to help trace information through databases, applications and business-intelligence systems.

The objective is to provide greater visibility into the source, meaning, quality and governance of individual data elements.

That governed data can then support AI applications such as Microsoft Copilot.

M&T is also using retrieval-augmented generation (RAG) with internal, governed data. This approach can allow AI systems to retrieve relevant information from controlled internal sources before generating an answer, helping connect AI capabilities with the bank’s own information environment.

Three Ways M&T Is Scaling Generative AI

M&T’s generative AI strategy can broadly be divided into three areas.

1. AI for Employees

The first involves general-purpose AI tools that employees can use for everyday productivity.

Examples include:

• Drafting emails
• Creating reports
• Summarizing conversations
• Finding information
• Supporting routine administrative tasks

Microsoft Copilot is central to this employee-focused strategy.

2. AI Built Into Existing Applications

The second approach involves using AI capabilities that are already integrated into software used by the bank.

M&T operates more than 1,800 applications, many of which are supplied by third-party vendors. Rather than developing every AI capability internally, the bank can identify useful AI features already available within those applications.

3. Proprietary AI Systems

The third approach focuses on building AI systems around M&T’s own data, workflows and business processes.

Early applications include repetitive operational tasks, software development, fraud prevention and cyber defense.

This strategy gives the bank flexibility to combine commercial AI products with internally developed capabilities.

AI Applications Are Moving Beyond Basic Productivity

M&T’s early generative AI adoption focused heavily on employee productivity, including writing, summarization and software development.

The bank is now looking at more sophisticated use cases.

These include identifying customer needs and flagging portfolio risks, according to the supplied reporting.

M&T is also examining potential applications for agentic AI in cybersecurity and fraud detection. Agentic systems could potentially support more complex sequences of tasks rather than simply generating text in response to a prompt.

The bank continues to evaluate both internally developed systems and external technology platforms as it determines where AI can deliver practical value.

M&T’s AI Strategy Mirrors a Broader Banking Trend

M&T is not alone in accelerating enterprise AI adoption.

Large US banks including JPMorganChase and Bank of America have also deployed generative AI across employee and customer-service workflows.

JPMorganChase launched its internal LLM Suite platform to more than 200,000 employees in 2024. By 2025, more than 65,000 employees in its Corporate and Investment Bank were actively using the platform, while more than 90% of its engineers were using AI coding assistants.

Bank of America has also introduced EricaAssist, a generative AI-enabled system used by more than 18,000 customer-service employees.

EricaAssist can summarize the reason for a customer’s call, retrieve relevant information and suggest possible next steps while leaving the employee responsible for the interaction.

According to the supplied figures, Bank of America said in July 2026 that EricaAssist could provide contextual guidance in less than three seconds and had reduced average call times by nearly one minute.

These examples demonstrate how AI adoption in banking is moving from experimental projects toward everyday employee workflows.

What M&T Bank’s AI Expansion Means

M&T Bank’s AI rollout highlights an important point about enterprise artificial intelligence: successful deployment requires more than simply purchasing an AI tool.

The bank’s expansion has been supported by years of investment in technology infrastructure, internal technology talent, data governance and modernized applications.

Its approach combines employee-facing AI tools with embedded AI capabilities and proprietary systems built around controlled data.

At the same time, M&T’s governance requirements place limits on how employees can use AI and emphasize human responsibility for AI-assisted work.

Final Takeaway

M&T Bank is entering a new phase of enterprise AI adoption after years of technology modernization.

With more than 15,000 employees using AI copilots, the bank is applying artificial intelligence to practical areas ranging from email and report creation to customer-service summaries, software development, fraud prevention and risk management.

The larger story, however, is the infrastructure behind that adoption. M&T has expanded its internal technology workforce, modernized legacy systems, increased technology spending, improved system reliability and developed stronger data-governance capabilities.

As AI moves deeper into banking operations, M&T’s strategy shows why technology modernization and data governance can be just as important as the AI models themselves. The bank’s next phase will likely focus on embedding AI more deeply into existing applications and developing proprietary systems capable of addressing more complex operational, cybersecurity and financial-risk challenges.