What 50,000 Minutes of Customer Engagements Taught Us: Top Banking Trends in 2025

Building on insights from our 2024 year in review, the IBISWorld Commercial Banking Team raised the bar in 2025. Over the past year, we hosted more than 50,000 minutes of strategic conversations with banks across the market, representing a 20 percent increase year over year. These discussions spanned regional community banks through to global institutions and revealed a clear shift in how lenders approach technology and data. Banks are moving from reactive adoption toward more proactive, AI enabled strategies that prioritize efficiency, governance, and client value.

Two themes surfaced consistently. Lenders want research that is reliable, easy to find, and directly actionable. At the same time, many institutions remain uncertain about which resources can best support their research needs while aligning with broader digital transformation efforts.

As our Chief Data Officer has emphasized, smarter AI starts with smarter data. While AI continues to advance rapidly, the human element remains essential. At IBISWorld, more than 150 research analysts, editors, data engineers, and quality assurance specialists build, review, and enrich every one of our 30 million data points to ensure accuracy, consistency, and credibility.

At a time when many lenders feel pressured to choose between speed and reliability, the market confirmed that IBISWorld is uniquely positioned to deliver both. Our long-standing expertise in publishing structured, human vetted industry data for th banking sector enables lenders to move faster without compromising confidence or compliance.

These conversations directly shaped deeper investment in banking workflows while reinforcing the disciplined data approach IBISWorld has carried forward since 1971. Whether accessed through IBISWorld, embedded within native banking systems, or delivered through industry leading integration partners, banks are making decisions more quickly and with greater confidence than ever before.

Embedding industry intelligence across sales, credit, and risk workflows

In 2025, banks moved from treating industry research as a credit specific input to embedding it across core commercial banking workflows. Conversations revealed a clear shift toward enterprise-wide intelligence that supports prospecting, underwriting, and ongoing risk monitoring. The emphasis is no longer on accessing research, but on operationalizing it where bankers already work. This evolution reflects a broader move from reactive analysis to proactive, workflow-driven decision making.

Operationalizing industry research across banking workflows

Industry research that once lived primarily within credit teams is now informing relationship managers, treasury groups, and portfolio oversight. Bankers are using industry insights to anticipate client needs, tailor outreach, and frame conversations around sector specific risks and opportunities. What began as a credit focused resource has become a shared intelligence layer that strengthens client conversations and risk awareness across the bank.

Loan origination systems: Centralizing workflows for faster decisions

Loan origination systems such as nCino and Abrigo have become key execution points for embedding industry data. Banks are prioritizing third party integrations that auto populate benchmarks into underwriting workflows, reducing manual entry and preparation time. Continuous credit monitoring capabilities further extend this value by introducing industry context earlier and more consistently in the credit lifecycle.

Tackling the swivel chair effect with CRM consolidation

Client-facing workflows are following the same consolidation trend. Banks are actively addressing the swivel chair effect created by moving between CRM platforms, LOS tools, and research portals. By embedding industry insights directly into systems like Salesforce, bankers gain a unified view of client performance and sector dynamics, supported by AI driven summaries that improve call preparation and relationship management.

Action items

• Audit how industry research is accessed across sales, credit, and risk teams.
• Identify where insights still sit outside core LOS and CRM workflows.
• Prioritize embedding industry data directly into underwriting and client facing systems.
• Set a measurable goal to reduce credit or client preparation time within one core workflow.

Making data and AI scalable through governance and enablement

Many banks learned in 2025 that technology investment alone does not guarantee impact. Adoption stalled when teams lacked training, confidence, or clarity around how data and AI should be used. As a result, banks shifted focus toward governance, enablement, and trust as prerequisites for scale. Transformation increasingly depended on people and process, not just platforms.

Skilling teams to harness data’s full power

Banks doubled down on role specific training tied to real workflows. Relationship managers focused on prospecting and call preparation using segmented benchmarks, while credit teams received deeper instruction on AI assisted memo drafting and scenario analysis. Institutions that connected training to live use cases and visible outcomes consistently reported stronger adoption and confidence.

AI evolution: From experiment to governed workflow essential

AI moved from experimentation to governed deployment in 2025, with tools like Microsoft Copilot supporting underwriting, analysis, and client preparation. While productivity gains were meaningful, banks emphasized the importance of structured governance and reliable inputs. Human verified data remained critical to producing explainable outputs that met regulatory and risk expectations.

SSO, APIs, and adoption boosters

Ease of access emerged as a decisive factor in adoption. Single sign on and embedded access within existing platforms removed friction that had previously limited usage. Pre-built APIs allowed banks to integrate industry data directly into workflows, increasing consistency and reducing reliance on standalone tools.

Action items

• Launch role specific training programs tied directly to banker workflows.
• Establish clear governance standards for AI usage across credit and sales teams.
• Ensure AI driven workflows are powered by human verified data sources.
• Remove access barriers through single sign on and embedded integrations.

Designing data foundations for localized and scalable decision making

As economic uncertainty persisted, banks reassessed the data foundations supporting their decisions. Generic benchmarks proved insufficient for targeted growth and risk management. Institutions increasingly prioritized precision, scalability, and consistency across teams. These investments are now seen as foundational to future AI and analytics initiatives.

Localized and segmented data: Fueling geographic precision

Banks placed growing emphasis on state and county level data, as well as segmentation by borrower size and operating model. Lenders highlighted that apples to apples comparisons are essential for middle market underwriting and targeted portfolio strategies. Localized insight improved both risk assessment and the relevance of client conversations.

Data lakes and scalable architectures

Enterprise data lakes, often built on platforms such as Snowflake, emerged as central hubs for consolidating vendor data and supporting reporting at scale. These architectures allow industry data to be ingested once and distributed across dashboards, workflows, and analytics tools. Leaders stressed that scalability must be paired with human verification to prevent poor quality inputs from undermining insight.

Action items

• Identify priority portfolios and geographies requiring localized or segmented data.
• Assess readiness for centralized data architecture to support analytics and reporting.
• Pilot integration of industry data into an enterprise data lake environment.
• Maintain governance processes to ensure data quality as scale increases.