The State of AI Development in 2026
A few years ago, established banks, long known for not readily adopting new technology, were looking for ways to embrace it. And so a company was founded to help them choose fintech partners because these two sides of the same coin could no longer afford to be at odds.. So, Finbridge Global used OutSystems to build a unique product that uses scoring to assess and report on fintech capabilities. Recently, it used OutSystems to add AI and ML search functions to help big banks find the fintech that best fits their needs.
This is the banking of today, where agentic AI has a seat at the CIO’s priority table, along with regulatory pressure and legacy modernization. For years, the industry had a reputation for moving carefully. Then along came agentic AI. And suddenly, banking pulled ahead of the broader market.
The 2026 State of AI Development report from OutSystems includes responses from 320 banking and financial services IT leaders. Their answers tell a story that lines up with the Finbridge Global platform, and it has real implications for what your bank does next.
Table of contents:
- Banking is ahead of the market, by the numbers
- Why banking charged ahead in agentic maturity
- Leading in AI maturity delivers a portfolio of agents in production and reduces AI sprawl
- 51% say a unified platform that grounds AI with enterprise data is critical
- Banking leaders expect specific gains from a single platform
- Banking has the disciplines in place to scale agentic AI
Banking is ahead of the market, by the numbers
Among the financial services respondents, 52% rate their agentic AI capabilities as advanced or expert. Fifteen percent are at the expert level, where AI agents are actively executing transactions inside core, mission-critical systems. That is the highest expert rate of any industry in the survey.
Production deployment tells the same story. Some 12% of banking respondents have moved 76% to 100% of their agentic AI projects from pilot into full enterprise production. This matches the top score posted by the technology industry and beats every other vertical. Another 37 percent have moved more than half of their projects into production.
These numbers indicate that agents are being deployed into the core systems that banks run on. This is remarkable for an industry known for not rushing headlong into unproven technology territory.
Why banking charged ahead in agentic maturity
Decades of investment in regulatory reporting, fraud analytics, risk modeling, and audit logging gave banking a head start with algorithmic decision-making. The governance instinct was already built in.
Banking did not have to invent the human approval step, the compliance check, or the audit trail for agentic AI. Banks already ran on those disciplines, especially after the financial crisis of 2008. Wrapping them around autonomous agents was a natural extension of work that started long before anyone used the word “agentic.”
The numbers back that up, with 55% of banking respondents saying they use human approval steps to ensure trustworthy AI operations. This is the highest rate of any industry. Inside their agent workflows, 53% embed strict compliance rules, and 39% run comprehensive audit logging across agent activity.
Leading in AI maturity delivers a portfolio of agents in production and reduces AI sprawl
Banking now has something other industries do not, which is a working portfolio of agents in production, supported by governance built for regulated environments. That maturity is creating the next opportunity.
More deployed agents means more places where context, data, and policies need to stay in sync. Banking leaders are already seeing the value in pulling that work onto a single foundation, with 36% saying they manage AI tool sprawl proactively through standardized governance. Compare that with 22% in manufacturing. Plus, 12% already run centralized control through a unified platform, the highest share among the sectors we surveyed.
In other words, banking is taking a fragmented field and bringing it into a governable shape, and it is doing so earlier than its peers.
51% say a unified platform that grounds agentic AI with enterprise data is critical
When we asked banking respondents what they need most from a unified platform for building apps, managing data, and deploying AI agents, the answers lined up around three priorities.
The top answer was building AI assistants that ground their answers in enterprise data, which 51% rank as critical. This addresses one of the biggest concerns about generative AI in regulated settings. An agent that draws from the bank’s own data and business rules answers with context, not improvisation.
Using a platform to build external portals was next on the list. It’s a priority for 44% of respondents. Customer-facing experiences are still where banks compete most visibly, and the unified platform has to make those experiences as easy to build as everything else.
Another priority is a unified interface that combines data for AI. It was cited by 40%. When agents need to reason across the front, middle, and back office, the platform has to make that data available without another integration project.
Banking leaders expect specific gains from a single platform
Of all IT leaders surveyed, 96% see a unified platform that offers a centralized space to build and deploy agents as very or somewhat important to their AI future. The banking response maps closely to that aggregate; however, there is more specificity in what they want from that platform:
- Faster iteration and delivery across the software development lifecycle
- Easier governance and policy enforcement across agents and applications
- Shared data context that spans front, middle, and back office systems
- Simplified skill requirements across development and operations teams
- Lower integration and maintenance costs across the portfolio
- A single place to audit agent behavior, review decision logs, and enforce policy across every application the bank runs
"I now find it easier to sleep at night, knowing that our critical business system and client data is secure, and on a technology platform that will scale for hundreds of credit unions and millions of their customers."
Jim Gallagher, Founder and CEO, Lucro
This is already paying off for banks building on OutSystems. At Axos Bank, modernizing legacy applications and embedding agents to automate log analysis and document mapping produced an 85 percent decrease in rework, a 75 percent reduction in defects, and 60 percent faster development.
At Grihum Housing Finance in India, an AI agent built on the same platform now automates property deviation analysis across more than 10 disconnected applications and two core system stacks, contributing to 70 percent faster onboarding and 100 percent paperless operations. Western Union has cut its time to market in half.
These results share a common thread. The agent matters less than the platform underneath it.
Banking has the disciplines in place to scale agentic AI
Banking earned its lead in agentic AI by doing the unglamorous work first. Governance frameworks. Compliance rules. Audit logs. The disciplines that made traditional AI work in banking are the same disciplines that make agentic AI scale, and your bank already has them.
The next phase belongs to the institutions that bring all of that work onto one platform built for regulated environments. That is what turns the current maturity advantage into a durable one. That is how your bank builds, modernizes, governs, and grows on the same foundation.
Read the full 2026 State of AI Development in Banking and Financial Services report to see the benchmarks, customer stories, and CIO checklist behind these findings.
