Financial institutions can use AI to expand software delivery capacity, but an AI-native approach is not a promise of faster code or automatic savings. It means integrating AI into requirements, design, coding, testing, release and maintenance—with measurable outcomes, human accountability and controls suited to financial services. The practical goal is to deliver more useful change without weakening security, reliability or compliance.
What is the software delivery gap in financial services?
The delivery gap is the mismatch between demand for technology-enabled change and the capacity available to deliver it. It is shaped by constrained budgets, maintenance-heavy technology estates, integration challenges and systems that can be difficult to scale. It is a useful description of a business problem, not a universal numerical measure: the evidence cited here does not establish one gap size that applies to every bank or financial institution.
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McKinsey’s November 2024 analysis says its best-performing banks can achieve 50% more technology capacity than average banks for the same budget. That is McKinsey’s reported comparison, not an independently verified industry-wide benchmark or a result that AI adoption alone guarantees. Deloitte’s April 2025 article reports that interviews with bank technology and software engineering leaders in 2024 surfaced inefficient projects, costly maintenance, integration problems, systems not built for scale and slower runtimes. Those interviews describe a reported pattern, not a census of all banks.
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Where can AI contribute across software delivery?
AI can assist at several stages of the software lifecycle. These are potential uses, not proof that a given institution has deployed them safely or achieved a particular benefit.
Requirements and problem definition
AI tools can help identify and classify requirements, surface implicit requests and assemble an initial problem statement. Teams still need business and technical owners to resolve ambiguity, set priorities and confirm that the result reflects the actual need.
Design and architecture
Given natural-language requirements, AI can propose initial design options and trade-offs. Engineers and architects must assess whether those options fit the institution’s systems, resilience needs, data boundaries and regulatory obligations.
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Coding and legacy maintenance
Coding assistants can help generate or maintain software, including work involving legacy code. Generated changes need the same ownership, review and verification as other changes; plausible-looking output is not evidence that a change is correct, safe or appropriate for production.
Testing and verification
AI-enabled tools can help generate test cases and execute many tests. Their value depends on whether the tests cover meaningful risks and expected behavior—not simply on how many cases they produce. Human review remains important for material changes and for gaps in test coverage.
Release and ongoing maintenance
AI assistants may support continuous integration and deployment tasks such as scheduling, rollout checks and maintenance. Those contributions should operate within release controls and monitoring rather than bypassing them.
In this article, “AI-native” describes an operating approach that integrates AI into how teams specify, build, verify, release and maintain software, with explicit human review and operational controls appropriate to the change. It is an editorial synthesis of the lifecycle use cases and financial-services oversight concerns described by Deloitte, the U.S. Government Accountability Office (GAO) and the U.S. Department of the Treasury—not a formal industry standard.
What do the reported benefits actually establish?
The figures below describe different kinds of evidence. Forecasts, a bounded test case and industry statistics should not be treated as interchangeable proof of an institution’s likely return.
| Figure | What it represents | How to interpret it |
|---|---|---|
| 20%–40% | Deloitte’s 2025 estimate of the potential reduction in banking-industry software investment by 2028. | A forecast, not an observed industry result or a guaranteed saving for an individual institution. |
| US$0.5 million–US$1.1 million | Deloitte’s 2025 estimated savings per software engineer by 2028. | A forward-looking estimate; it is not an observed saving per engineer. |
| 20% | A productivity boost among a group of engineers in a Citizens Bank test case, as reported by Deloitte in 2025, citing an external Banking Dive account. | A bounded test case, not evidence that all banks or engineering teams will achieve the same result. |
| Approximately US$107.8 billion | U.S. enterprise IT software spending by banking and investment services in 2024, as cited by Deloitte from Gartner. | Sector spending context, not the amount AI could save. |
| 50% more technology capacity | McKinsey’s November 2024 comparison of its best-performing banks with average banks for the same budget. | A reported performance comparison, not a guaranteed effect of adopting AI. |
The distinctions matter: a forecast can inform a business case, but it cannot substitute for an institution’s own baseline, pilot results or cost accounting. Likewise, the Citizens Bank figure is evidence from a specific test case, not a general productivity multiplier. Deloitte’s estimates should be evaluated as estimates rather than as realized returns.
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What banking technology signals are useful—and what are their limits?
Gartner reported that banking IT spending equaled 8.1% of revenue in 2024. The same Gartner material says banking and investment services software engineering leaders identified automation (64%), generative AI (60%) and cloud adoption (45%) as approaches for cost control or reduction. Respondents identified application security (76%) and API design (69%) as skills for delivering software that meets business needs.
These figures provide sector context, not proof that any one approach produces a particular saving or that the percentages represent adoption by every institution. Gartner’s publicly accessible page is a research abstract; the full research is access restricted. The figures should be read with that limitation in mind.
What controls should accompany AI-assisted delivery?
Financial institutions face risks that extend beyond output quality. GAO’s May 2025 report discusses lending bias and cybersecurity risk, as well as concerns about reliability, explainability and oversight of third-party AI providers. For the United States, GAO notes that regulators’ existing technology policies cover areas including data protection, IT security, model risk management, and acquisition or oversight of third-party software.
The U.S. Treasury’s December 2024 summary recommends that financial firms check AI use cases for compliance with existing laws before deployment and periodically reevaluate compliance. It also calls for coordination and information sharing on risk-management practices and standards. Treasury’s recommendations do not replace institution-specific legal analysis. The summary followed Treasury’s 2024 AI financial-services request for information, which received 103 comment letters. Under Secretary for Domestic Finance Nellie Liang said: “Through this AI RFI, Treasury continues to engage with stakeholders to deepen its understanding of current uses, opportunities, and associated risks of AI in the financial sector.”
The Bank for International Settlements’ Financial Stability Institute (BIS FSI) wrote in December 2024 that existing frameworks address many risks, while identifying governance, expertise and skills, model risk management, data governance, non-traditional players, new business models and third-party AI services as areas needing further attention. The authors note that their views do not necessarily represent the BIS or its member central banks.
Taken together, these sources support a practical set of operational safeguards. This is a synthesis of the risks they identify, not a verbatim regulatory checklist:
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- Set data boundaries. Define what information approved tools may receive, and apply access restrictions consistent with data-protection and security requirements.
- Keep material decisions reviewable. Assign people to review and approve significant generated code, requirements, designs and release decisions. The degree of review should reflect the change’s potential impact.
- Make verification traceable. Record relevant generated changes, tests, approvals and release decisions so teams can understand what was changed and how it was checked.
- Retain security and model oversight. Include security checks and the institution’s applicable model-risk processes in the delivery workflow rather than treating AI-assisted work as exempt from them.
- Assess external providers. Understand third-party services, responsibilities and oversight arrangements, including how changes in a provider or service could affect controls.
- Monitor after release. Check production behavior and operational outcomes so that teams can detect problems that pre-release tests did not expose.
How should an institution evaluate an AI-native approach?
Measure the whole delivery system, not just the time saved on a coding task. Deloitte argues for demonstrating improvement across software delivery, while McKinsey frames the opportunity in terms of greater technology capacity and faster delivery of customer features. The following evaluation axes are an editorial framework informed by those analyses, not a quoted industry standard.
- End-to-end flow: track cycle time and movement of work through requirements, design, build, verification and release. Faster code completion does not establish faster delivery if review, integration or testing becomes a bottleneck.
- Quality and rework: examine defects, rework and whether tests effectively cover expected behavior and important risks.
- Reliability and security: assess operational reliability and security outcomes alongside delivery speed.
- Capacity for priorities: determine whether engineering effort is being redirected to valuable business work, rather than counting activity or output alone.
- Developer experience and skills: assess whether engineers can use the tools effectively, understand their limits and maintain ownership of the software.
- Governance: check data boundaries, reviewability, control ownership and visibility into third-party services.
- Total cost: account for implementation, licenses, infrastructure, security work and ongoing maintenance, not just the apparent cost of generating a change.
A credible evaluation starts with a defined workflow and baseline, then compares results against the same measures after introducing AI assistance. Record quality and control outcomes as well as speed and cost. A pilot that completes coding tasks faster but increases rework, weakens review or shifts effort into integration has not demonstrated an end-to-end delivery improvement.
What does a responsible rollout look like?
- Choose a bounded workflow. Select a delivery task with a clear business purpose and owners who can judge both its value and its risks.
- Set the boundaries before use. Establish approved data handling, access restrictions, review responsibilities, security checks and any applicable model or third-party oversight for that workflow.
- Define measures in advance. Record a baseline for flow, quality, reliability, security, engineering capacity, developer experience and total cost, using the measures relevant to the task.
- Run the work with accountable review. Treat AI output as a contribution to the workflow, not an approval. Keep material changes, tests and release decisions reviewable and traceable.
- Inspect outcomes before expanding. Compare results with the baseline, including failures, rework and control performance. Expand only where the evidence supports the change and the necessary oversight remains workable.
- Reassess after deployment. Monitor live behavior and revisit compliance and risk as the use case, tools, data or provider arrangements change.
The sequence is a practical implementation framework derived from the cited oversight and delivery concerns; it is not presented as a prescribed regulator process.
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