December 19, 2025
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Does investing in AI-driven automated coding really increase software development productivity in the finance sector? Here’s what you must know.
As a technology or business leader, you’ve likely been investing in AI tools and processes with a clear focus on accelerating digital delivery, using improvements in developer productivity as a core measure of success.
Early outcomes validate these investments, with leading financial services firms reporting an average 20% increase in overall software development productivity and coding efficiency gains of 30% to 55% among developers using large language model (LLM)–powered tools.
Deloitte projects that, by 2028, these improvements could yield cost savings of $0.5–1.1 million per engineer, driving an industry-wide reduction of 20%–40% in total software development expenditures.
According to the State of AI-assisted Software Development 2025 report, by Google’s DevOps Research and Assessment (DORA) team, AI adoption has surged to 90% among software development professionals worldwide, increasing 14% over the 2024 level.
What these numbers don’t show is the structural paradox that lies beneath. Developers are building code faster than ever, but that does not mean the efficiency gains percolate through the value chain. The true measure of how value reaches production remains unchanged in most cases.
AI-powered software development is rapidly becoming the new normal in banking.
Learn more in our blog: AI-Powered SDLC – The Paradigm Shift has Begun!
Productivity Gain – Reality or a Mirage?
The acceleration that automated code generation brings has created an illusion of progress. Developers log shorter velocities in Jira and Scrum iterations. But in most cases, release cadence and business KPIs show minimal improvement.
The problem isn’t in the coding process. It is in the value stream bottlenecks that persist downstream. The most prominent of these are:
- Dependence on manual security reviews, penetration testing cycles, and compliance checks, which delay deployment.
- Peer review cycles remain inconsistent and often non-automated.
- Rigid change management processes, including approval gates (quality and security), extend the release timeline.
- Siloed performance testing cycles are adding to the delays.
- Software requirement and design phases are heavily manual and are full of tedium.
- Database deployments are mostly manual.
In effect, the locus of inefficiency has shifted from the coding phase to the validation and delivery phases. The result is idle productivity, where efficiency gains at one phase fail to convert into measurable business velocity.
This fact was further underscored at Unscripted 2025, hosted by Harness, whose central theme was “The Future of AI-powered Software Delivery” and emphasized moving beyond AI for writing code to applying it across the entire Software Development Lifecycle (SDLC). As Nick Durkin (CTO, Harness) emphasized, the real challenge lies in automating trust across the SDLC. The goal isn’t just generating code faster but ensuring every commit can move seamlessly through compliance, testing, and deployment with confidence.
Automating database deployment ensures that database changes are reliably and consistently applied across different environments.
Learn how in our whitepaper: Mastering Automated Database Deployment – A Guide to Achieving Seamless Continuous Deployment
The Path Forward: From Developer Productivity to Systemic Velocity
“When AI dramatically accelerates software development, our control systems—that’s us—must also speed up.”
~ Gene Kim, bestselling author
Modern delivery processes must evolve from being solo execution engines to becoming intelligent, self-validating stream of processes. True velocity is a function of how well all stages of the value stream, including build, validate, deploy, and monitor, are interconnected.
The idea is to identify levers of automation that are chained together for this transformation, such as:
- AI-driven policy engines that automatically enforce compliance and governance before code merges. This will mean automating tools that enforce policy as code with AI.
- Using AI Agents to automate fixes to vulnerabilities and code issues during pipeline execution.
- Creating requirements and design via AI Agents, which allows integration with tools like Jira. This enables requirements to be pushed as user stories and assigned to developers using a delivery excellence agent based on velocity derived from historical data.
- Continuous verification frameworks that track risk signals in real time and trigger automated mitigation or rollback.
- Adaptive pipelines that leverage AI to optimize test coverage, resource utilization, and deployment strategies dynamically.
The possibilities and options are endless. But they will need to be curated for each organization to meet and satisfy their specific business goals so that they address true productivity gap. According to an interesting example cited in the 2025 State of AI-assisted Software Development by DORA, a leading e-commerce company made the biggest gains not by detecting failures quickly, but by reducing the effort required to fix them.
The New Normal in Software Development
- Intelligent automation is becoming the new baseline.
- Visibility across the value stream is the real differentiator.
- Policy-based governance makes scaling sustainable.
The report emphasized that by embedding AI into the CI/CD loop, teams can deliver targeted, explainable suggestions and auto-generated fixes that drive greater developer engagement by working seamlessly within their existing tools.
The October 2025 AWS outage in the US east coast brought down thousands of sites and apps that host their services with the company.
Redefining Performance Metrics in the AI Era
As GenAI reshapes the SDLC, traditional productivity KPIs (such as lines of code and sprint velocity) have become obsolete. To measure true transformation, organizations must track metrics that correlate directly with business agility and resilience. This is also highlighted by the lessons we all learned from the recent AWS outage, where organizations that were architected for 99.99% availability were down and unresponsive. This could have been avoided through a greater focus on metrics such as RTO and RPO. We suggest using the metrics prescribed by Google’s State of AI-assisted Software Development report as the starting point while adopting AI-infused SDLC.
- Lead Time for Change: Time from code commit to production deployment.
- Change Failure Rate: Percentage of changes causing production incidents.
- Mean Time to Recovery (MTTR): Speed of incident resolution post-deployment.
- Deployment Frequency: How often validated features reach production safely.
These metrics collectively indicate whether AI is improving flow efficiency or merely amplifying local productivity without systemic gain.
Learn more about the software development and delivery performance metrics in our whitepaper:
Large Scale DevSecOps Transformation
The 2025 State of AI-assisted Software Development has enhanced these metrices to add two more KPIs while segregating them into two categories for measuring software performance built using AI.
1. Throughput
- Lead time for changes
- Deployment frequency
- Failed deployment recovery time: The time systems take to recover from a failed deployment. (New)
2. Instability
This is a measure of how well the software deployments go. When deployments go well, teams can confidently push more changes into production and users are less likely to experience issues with the application immediately following a deployment
- Change fail rate
- Rework rate: The ratio of deployments that are unplanned but happen as a result of an incident in production. (New)
Conclusion: From Illusion to Impact
Generative AI is constantly redefining the frontiers of developer efficiency, but does that equate to velocity? No. The illusion of productivity emerges when organizations mistake faster creation for faster delivery.
The new evolution in software delivery lies in AI-driven system orchestration as a control plane, integrating intelligence into every control and release point of the SDLC. Through end-to-end automation of trust, compliance, and risk management, enterprises can translate coding gains into genuine business acceleration.
In the age of AI, speed without flow is noise. True progress comes when productivity compounds into velocity and velocity into value. With nearly three decades of experience in developing cutting-edge solutions for the finance sector, Opus Technologies is the preferred partner to facilitate AI adoption. Speak with our experts to discover how we can ease the transformation for your organization.
Debashis Bhattacharyya
Debashis Bhattacharyya has over 18 years of experience planning and delivering technology solutions, specializing in Cloud, API, DevSecOps, and Payment Application Modernization. He has been instrumental in Opus’ transformational DevOps initiative with FIS, which won the prestigious DevOps Dozen Award for ‘Best DevOps Industry Implementation’ in 2022.
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Our promise is simple: Business Value Acceleration. Realization. Maximization.
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