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Financial firms can use AI-native engineering to expand software delivery capacity—but the case should be proved through faster end-to-end delivery, sound quality and security, and effective controls, not assumed from faster code completion. The delivery gap is the practical mismatch between demand for technology-enabled change, the capacity available to deliver it, and the effort required to maintain complex existing systems. It is not a single measured shortfall shared by every institution.
What creates the software delivery gap in financial services?
Financial institutions must maintain and integrate technology estates while responding to new business needs. McKinsey’s November 2024 analysis describes constrained budgets and growing maintenance demands as factors that leave less room for technology-enabled innovation. It reports that its best-performing banks can achieve 50% more technology capacity than average banks for the same budget. That is McKinsey’s comparison, not a universal benchmark or a result attributable to AI adoption.
Deloitte’s April 2025 article reports that interviews with bank technology and software engineering leaders in 2024 surfaced inefficient projects, systems not built to scale, costly maintenance, integration problems, and slower runtimes. These are reported patterns from interviews, not a census of every bank. Together, the sources point to a capacity problem with several causes: demand, legacy complexity, delivery friction, and the cost of keeping systems running.
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Here, AI-native means integrating AI into how teams specify, design, build, verify, release, and maintain software, while retaining human review and operational controls suited to the change. It is a practical description, not a formal industry standard. The aim is to improve the flow of work across the lifecycle, rather than bolt on a code assistant and treat usage as proof of better delivery.
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Deloitte describes potential AI use across several stages. These are possible contributions, not evidence that every institution has deployed them safely or achieved a particular result.
Requirements and design
AI tools may help classify requirements, surface implicit requests, and assemble a problem statement. Given natural-language requirements, they may also propose initial design options and trade-offs. Teams still need to validate whether the requirements are complete, feasible, and consistent with business and regulatory needs.
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Code and legacy maintenance
Coding assistance may generate or help maintain code, including work involving legacy systems. Generated code remains a proposal: engineers need to review it for correctness, security, compatibility, and fit with the system’s design.
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Testing and release
AI-assisted tools may help generate test cases and execute large sets of tests. Assistants may also support continuous integration and deployment, including scheduling, rollout checks, and maintenance. A generated test suite is not proof of coverage or safety; teams need traceable tests and release checks that reflect the risk of the change.
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What do the reported figures actually show?
The numbers below describe different kinds of evidence. Forecasts are not observed savings, an individual test case does not establish a sector-wide effect, and spending context is not a savings estimate.
| Figure | What it represents | How to interpret it |
|---|---|---|
| 20% to 40% | Deloitte’s 2025 estimate of potential reduction in banking-industry software investment by 2028. | A forecast, not an observed outcome or guarantee. |
| US$0.5 million to US$1.1 million per software engineer | Deloitte’s 2025 estimated savings per engineer by 2028. | A future-oriented estimate; it is not a reported saving already achieved by each engineer or institution. |
| 20% productivity boost | A result for a group of engineers in a Citizens Bank test case, as reported by Deloitte in 2025, which cites an external Banking Dive account. | A bounded test case, not evidence that all banks or teams will see the same result. |
| Approximately US$107.8 billion | Gartner’s estimate, cited by Deloitte in 2025, of US enterprise IT software spending by banking and investment services in 2024. | Sector spending context, not the amount AI can save. |
| 50% more technology capacity | McKinsey’s November 2024 comparison of its best-performing banks with average banks for the same budget. | A reported comparison, not a guaranteed benefit of AI adoption. |
| 8.1% of revenue | Gartner’s 2024 figure for banking IT spending as a share of revenue. | Industry context reported in Gartner’s publicly accessible research abstract. |
| 64% automation; 60% generative AI; 45% cloud | Gartner’s 2024 figures for approaches that banking and investment services software engineering leaders identified for cost control or reduction. | Reported respondent selections, not measured savings or proof of implementation outcomes. |
| 76% application security; 69% API design | Gartner’s 2024 figures for skills respondents identified as important for delivering software that meets business needs. | Reported respondent selections, not a measure of each workforce’s proficiency. |
| 103 comment letters | The number of responses Treasury received to its 2024 AI financial-services request for information. | Evidence of stakeholder engagement, not a measure of consensus. |
The Gartner figures are available on a publicly accessible research abstract; Gartner says the full research is access restricted. The figures should therefore be read as the abstract presents them, not as a substitute for the full study’s methodology or detail.
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How should financial firms manage AI-related risk?
Financial-services use raises familiar technology and model concerns alongside AI-specific questions. The U.S. Government Accountability Office’s May 2025 report discusses risks including lending bias and cybersecurity, and notes that reliability and explainability can make institutions cautious. It also identifies oversight of third-party AI providers as a concern. The report describes existing U.S. regulator policies addressing areas such as 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 proposed AI use cases for compliance with existing laws before deployment and periodically reevaluate compliance. It also recommends coordination and information-sharing on risk-management practices and standards. These are Treasury recommendations, not a substitute for institution-specific legal analysis.
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The BIS Financial Stability Institute’s December 2024 analysis says existing frameworks address many risks while highlighting areas for further attention: governance, expertise and skills, model risk management, data governance, non-traditional players, new business models, and third-party AI services. Its authors note that their views do not necessarily represent the BIS or its member central banks.
As a practical synthesis of these risk areas, teams can build controls into delivery rather than leave them to a final approval step:
- Define approved data handling and access boundaries for AI tools, including what information may be submitted and who can use each capability.
- Require human review for material changes, with review depth proportionate to the software’s function and potential impact.
- Keep generated code and tests traceable to their review and verification, and run security checks as part of the normal delivery process.
- Assign ownership for model risk and vendor oversight, including visibility into third-party services used in the workflow.
- Monitor released systems for reliability, security, and compliance issues, and reassess controls as use cases or services change.
How can you tell whether an approach is working?
Compare delivery models, tools, or pilots across the full software lifecycle. Completion speed for a coding task alone can miss added review, rework, defects, release delays, or operating costs. Deloitte argues for demonstrating improvement across delivery; McKinsey frames the opportunity as more technology capacity and faster customer-feature delivery. The following comparison framework is an editorial synthesis of those perspectives, not a quoted standard.
- End-to-end flow: Measure cycle time and work flow from requirements through release, not only time spent writing code.
- Quality: Track defects, rework, and whether tests effectively catch problems.
- Reliability and security: Examine operational outcomes and security findings alongside speed.
- Engineering capacity: Determine whether time released is redirected to business priorities rather than absorbed by added review or maintenance.
- Developer experience and skills: Assess adoption and whether teams have the expertise to use and check AI outputs appropriately.
- Governance: Check data boundaries, reviewability, clear control ownership, and visibility into third-party services.
- Total cost: Include implementation, licenses, infrastructure, security, and ongoing maintenance.
A useful pilot therefore starts with a defined workflow and baseline, selects a risk-appropriate use case, and measures the same delivery and control outcomes before and after adoption. The decision to expand should follow evidence that the overall workflow improved without unacceptable deterioration in quality, reliability, security, or governance.
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