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AI & Tech

AInews: M&T Bank’s Tech Reboot Sets Stage for Enterprise‑Wide AI Rollout

Nic Reeve7 min read
AInews: M&T Bank’s Tech Reboot Sets Stage for Enterprise‑Wide AI Rollout

On September 2, 2026, M&T Bank confirmed that it has deployed AI copilots and other enterprise tools to more than 15,000 employees as part of a broad expansion of enterprise artificial intelligence, capping a technology overhaul that began in 2018 and positioning the bank as a regional leader in data‑driven operations.

How large is M&T Bank’s enterprise AI rollout?

M&T Bank’s enterprise AI rollout now reaches the majority of its workforce. According to Yahoo Finance in September 2026, the bank has deployed AI copilots to over 15,000 employees, while Forbes reports that around 16,000 staff actively use generative AI tools for daily work as of August 2026.

The expansion of AI tools inside M&T Bank is now one of the largest documented deployments in a U.S. regional bank. Key figures include:

  • According to Forbes, August 2026: approximately 16,000 employees use generative AI tools across the enterprise.
  • According to Yahoo Finance, September 2026: AI copilots support more than 15,000 employees in tasks such as call analysis, report drafting and code generation.
  • According to ArtificialIntelligence‑News, September 2026: initial pilots involved about 800 employees before scaling across the organization.
  • According to AIM Media House, December 2025: Microsoft 365 Copilot and Copilot Chat had been rolled out to roughly 17,000 employees by early 2025.

Most of these tools run on Microsoft’s Copilot suite, delivered through M&T Bank’s modernized cloud and data architecture. Staff now access generative models through Office applications, internal chat interfaces and embedded features in existing business systems.

What concrete AI use cases are live inside M&T Bank?

M&T Bank is using enterprise AI in internal operations, risk management, software development and commercial credit monitoring, rather than front‑end customer interactions. These use cases combine Microsoft Copilot with specialist platforms from vendors like RDC.AI and Rich Data Co.

According to Yahoo Finance and ArtificialIntelligence‑News, the current internal and risk‑oriented applications include:

  • Analyzing call‑center conversations to identify customer needs and emerging portfolio risks.
  • Drafting reports, internal communications and meeting summaries for employees across departments.
  • Generating and reviewing software code to accelerate development and maintenance work.
  • Spotting potential fraud patterns and strengthening cybersecurity monitoring.

Beyond copilots, M&T has introduced domain‑specific AI platforms in commercial credit:

  • According to the Banking Tech Awards USA showcase, May 2026: M&T Bank partnered with RDC.AI in 2025 to replace manual, rules‑based commercial credit monitoring with an AI‑driven continuous monitoring platform.
  • The same source notes the platform supports over 1,200 relationship managers and credit associates, providing automated alerts on borrower risk and portfolio exposure.

AIM Media House reports that M&T is also integrating Rich Data Co.’s decisioning platform via vendor nCino, and adopting Amperity’s customer data cloud to unify interactions and tailor communications across channels. These tools sit on top of the bank’s controlled data environment rather than feeding directly into unsupervised decisioning.

What technology overhaul enabled M&T Bank’s current AI expansion?

M&T Bank’s push into large‑scale AI follows a multi‑year effort to fix data and legacy systems first. The bank began a broad technology overhaul around 2018, rebuilding its data governance, cloud architecture and application landscape to support modern analytics and regulated AI adoption.

Forbes describes how M&T “built a strong data foundation” in Buffalo that now underpins dozens of generative AI use cases across three pathways: enterprise fluency, embedded capabilities in existing applications and proprietary models. Key elements of that foundation include:

  • According to Forbes, May 2025: an expanding portfolio of cloud‑based data products and ongoing retirement of legacy platforms.
  • According to CDO Magazine, December 2025: a medallion architecture layered over cloud‑enabled data products, with clear accountability at each layer.
  • According to Portfolio by BISA, September 2025: a central data repository called Edison, supported by lineage tools from Solidatus and Monte Carlo to track data movement.
  • According to Windows Forum reporting, March 2026: a centralized cloud data strategy designed to create a unified “Customer 360” view and reduce reconciliation overhead by decommissioning legacy analytics tools.

M&T Bank’s Chief Data Officer Andrew Foster has emphasized that trusted data is the prerequisite for generative AI. Portfolio by BISA quotes him saying that reliable lineage and governance are essential to controlling operational, compliance and reputational risk as AI use scales.

How does M&T Bank govern data and AI across such a large deployment?

M&T Bank has built AI governance on top of its data controls rather than treating it as a separate exercise. The bank uses a federated data model, medallion architecture and strict lineage tooling, and sequences AI projects to follow maturity in oversight and risk management.

AIM Media House describes a stepwise blueprint for AI adoption at M&T Bank:

  • Reset data governance, lineage and controls before major AI pilots.
  • Run six‑month proofs of concept with tools like Microsoft Copilot before enterprise rollout.
  • Deploy internal copilots widely, while keeping customer‑facing applications limited and heavily supervised.
  • Layer in domain‑specific AI, such as commercial credit monitoring and customer‑data platforms, only where oversight frameworks are mature.

Portfolio by BISA reports that Edison and lineage platforms help the bank trace data sources for AI outputs, supporting internal audits and regulator reviews. Windows Forum’s analysis of Western New York banks highlights M&T’s published AI policies, inventory of models and continuous monitoring with alerts for drift and anomalies as part of its risk controls.

Which employees and business units are most affected by M&T Bank’s AI expansion?

M&T Bank’s AI deployment affects office staff, technologists and risk professionals far more than frontline customers. Copilot access is wide across corporate functions, while specialized platforms target commercial credit teams and analytics groups using the bank’s cloud data environment.

Based on reports from Yahoo Finance, Forbes and AIM Media House, the main groups using new AI tools include:

  • Customer service and call‑center agents, who use AI to summarize calls and identify next‑best actions.
  • Operations staff, who rely on copilots to draft emails, reports and meeting notes.
  • Software engineers and technology teams, where copilots assist with coding, documentation and troubleshooting.
  • Risk managers and credit associates, who use RDC.AI’s commercial credit monitoring platform.
  • Marketing and analytics teams, who work with Amperity’s customer data cloud and Rich Data Co’s decisioning tools to refine targeting and product offers.

AIM Media House quotes M&T’s data leadership stressing that the bank is “not going to the customer‑facing side yet” for broad generative AI use, underscoring a decision to focus on staff productivity and risk insights before AI touches customer decisions directly.

What strategy guides M&T Bank’s AI investments and future plans?

M&T Bank’s AI strategy combines three main pathways: expanding enterprise fluency, embedding AI into existing applications and building proprietary models using the bank’s own data, all within a regulated and inspection‑ready environment.

Forbes reports that the three pathways are:

  • Enterprise fluency, where around 16,000 employees experiment with generative tools to improve productivity and learn their capabilities.
  • Embedded capabilities inside roughly 1,800 applications, many sourced from third‑party vendors, where targeted AI features support tasks like document review and risk scoring.
  • Proprietary development of large language model and agentic solutions built around M&T’s data and processes.

ArtificialIntelligence‑News reports that Chief Data Officer Andrew Foster and his team articulated a similar framework: general employee use of generative AI, AI embedded in existing systems and custom solutions aligned with M&T’s distinctive workflows. Forbes and CDO Magazine both describe a “slow to go fast” approach, in which careful groundwork in data, governance and culture enables faster scaling once controls are proven.

Looking ahead, public comments and award submissions point to several likely next steps:

  • According to Forbes, May 2025: continued expansion of cloud‑based data products and retirement of legacy platforms as AI demand grows.
  • According to the Banking Tech Awards USA entry, May 2026: deeper integration of continuous AI monitoring in commercial credit, including new analytical measures of portfolio resilience.
  • According to Yahoo Finance, September 2026: exploration of agentic AI for cybersecurity and fraud detection, building on existing detection use cases.

How does M&T Bank’s AI journey compare with other regional banks?

M&T Bank now stands out among regional banks in Western New York for its focus on centralized data strategy and broad internal AI adoption, while peers such as Five Star Bank are documented as concentrating on AI in specific product lines like indirect auto lending.

Windows Forum summaries of local coverage describe two tracks in the region:

  • M&T Bank invests in cloud data architecture, governed customer insights and large‑scale employee access to generative tools.
  • Five Star Bank applies AI more narrowly to product‑level automation and credit decisioning, with smaller deployments.

These comparisons show that M&T Bank is using its size and long technology overhaul to treat AI as a company‑wide capability rather than an isolated experiment, emphasizing data discipline and internal fluency before aggressive customer‑facing innovation.

Sources

  1. 1.artificialintelligence-news.com
  2. 2.cdomagazine.tech
  3. 3.aimmediahouse.com
  4. 4.elites.zone
  5. 5.forbes.com
  6. 6.aimmediahouse.com
  7. 7.forbes.com
  8. 8.windowsforum.com
  9. 9.podcasts.apple.com
  10. 10.finance.yahoo.com
  11. 11.cdomagazine.tech
  12. 12.windowsforum.com
  13. 13.investing.com
  14. 14.informaconnect.com
  15. 15.portfolio.bisanet.org

Read more →

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