AI Coding Assistants: Seizing the Once-in-a-Generation Opportunity for Your Organization

Introduction

AI coding assistants have rapidly become a transformative force in software development. These tools, powered by advanced AI models, can generate code, automate repetitive tasks and provide real-time suggestions, fundamentally changing how software teams operate. Their adoption is now widespread: over 75% of developers in enterprise settings use some form of AI coding assistant, and 41% of all code written globally is now AI-generated or AI-assisted.

But the real opportunity isn't just in writing code faster. The true value lies in how organizations leverage the time and staff savings to drive strategic outcomes, including opportunities to modernize legacy systems, accelerate innovation and upskill talent. Without a plan, these gains risk being absorbed into the status quo, missing a transformational window to reshape software delivery.

What are AI coding assistants?

AI coding assistants are intelligent software tools that help developers write, review and maintain code. They use large language models (LLMs) trained on vast codebases to:

• Suggest code completions and generate boilerplate code
• Automate documentation and test creation
• Refactor and debug code, often identifying issues before they reach production
• Explain complex code and architectural patterns in plain language
• Facilitate the learning and visualization of existing code bases and architecture

Modern AI coding assistants, such as GitHub Copilot, Windsurf and Cursor, integrate directly into developers' IDEs and support dozens of programming languages and frameworks. Far more than auto-complete tools, they act as "pair programmers," helping developers navigate large codebases, generate tests and even translate legacy code into modern architectures.

What are typical productivity gains?

The productivity impact of AI coding assistants is significant, but nuanced. Multiple studies and real-world pilots show:

• Individual developer output increases by 20-40% on average, with some reports as high as 50% for routine tasks
• Junior developers see the largest gains – up to 39% more tasks completed, compared to 7-16% for senior developers
• Code commits and build frequency rise by 13-38%, enabling faster iteration and prototyping
• Code quality is maintained or improved when AI is paired with robust review processes

For a 1,000-person software organization, these gains can translate into hundreds of thousands of hours saved annually, which is enough to staff multiple new teams or accelerate major initiatives.

Overlooking long-term value in productivity gains

We see the successful adoption of AI coding assistants as a once-in-a-generation opportunity to remove constraints on organizational agility and finally close widening gaps in the software delivery cycle. It also provides the chance to feasibly address legacy applications at scale.

But not everyone sees things the way we do. Some clients have spoken of plans to convert this time savings into what we see as short-term cost efficiencies. 

Reducing software staff is not the right move

Some organizations see AI-driven productivity as a reason to reduce headcount, with plans to eliminate up to 25% of their software teams. This is typically backed or driven by the board and senior leadership to achieve short-term cost gains to the balance sheet.

In our opinion, this approach is shortsighted. While the change to the balance sheet is attractive, the engineers you've trained on your codebase and architecture are invaluable assets. Eliminating them risks losing critical institutional knowledge and undermining long-term innovation capacity.

Do not devalue junior developers

Another common misstep we are seeing is freezing hiring for junior developers or targeting them more explicitly for reduction. We see this as a bit counterintuitive. While junior developers have less experience than senior developers, they are often the most aggressive adopters of AI tools, such as coding assistants.

In addition, while AI can automate many entry-level tasks, junior engineers are the future pipeline for senior talent and often the driving force behind innovation. Cutting this cohort may jeopardize your organization's ability to grow and adapt.

"Absorbing" time savings vs. redeploying

Some teams simply plan to "absorb" the time savings, giving developers more breathing room. While this can reduce burnout, it misses the opportunity to redeploy resources toward strategic goals. Without a plan, productivity gains can be lost to inefficiencies elsewhere in the delivery pipeline. Further, removing time constraints on delivery efforts could tempt engineers to engage in needless optimization that doesn't add business value.

Inadvertently yielding your competitive advantage

Having a short-term view of the costs and staff savings from deploying AI coding assistants could create a long-term issue with competitive advantage. The new baseline for software delivery will entail leveraging AI tools. Therefore, if you reduce staff and take short-term cost benefits while your competitors leverage the time savings for greater strategic value, you could put your organization at a significant disadvantage.

Seven high-impact ways to redeploy productivity gains

With the adoption of ever-advancing AI coding assistants, we believe there is a game-changing opportunity to apply staffing and bandwidth to gain ground on the top challenges faced by nearly every organization. 

Based on real-world client experience and our experience adopting these tools ourselves, here are seven areas we recommend considering in your strategic planning to redeploy software resources saved by AI coding assistants.

1. Tackle the application backlog faster

Most organizations have a backlog of applications and features requested by the business, slowing down their ability to move quickly and capture new market opportunities. Backlogs can take the form of much-needed enhancements to existing applications or requests to support line-of-business innovation with new applications.

By using productivity gains from AI coding assistants to stand up teams focused on high‑value projects, organizations can accelerate delivery and boost stakeholder satisfaction. As mentioned earlier, if you do not invest in this way and your competitors do, you could be inadvertently creating a market challenge.

2. Modernize legacy programming languages and application architecture

Legacy systems, whether written in COBOL, Java or running on mainframes, are costly to maintain and difficult to evolve. This can slow the organization's ability to respond to new market opportunities and create risks to application reliability and cybersecurity posture. Several of our clients are reluctant to modify some of their legacy applications, viewing them as "too fragile."

AI coding assistants can automate code translation, refactoring and even architectural migration to cloud-native, containerized or microservices-based platforms. This accelerates modernization and reduces technical debt, enabling the organization to be better positioned to implement the application enhancements and corrections needed.

3. Harden the testing coverage of applications

Many organizations hesitate to change or upgrade applications due to weak test coverage or lengthy manual test cycles, which make it difficult to build confidence that code modifications won't break existing functionality. Once again, this can slow down the organization's ability to respond to changing market demands and new business opportunities.

AI assistants can rapidly generate and expand automated test suites, improving regression coverage and increasing confidence in code changes. They also create valuable opportunities for junior developers to learn the codebase and application architecture.

4. Harden the cybersecurity posture of applications

Most organizations struggle to consistently apply cyber controls and standards to their codebase. Often, it relies upon the experience of software developers and code reviews to ensure best practices are followed. 

AI tools can help enforce secure coding practices, identify vulnerabilities and automate security testing. By training assistants on your codebase and security standards, you can "bake in" cybersecurity improvements during both modernization and new development.

5. Strengthen overall adoption success

One misconception about AI coding assistants is the assumption, typically by those who aren't experienced software engineers, that AI assistants simply reduce the number of developers you need by replacing them with the AI performing their tasks. Further, there is a bit of a "set it and forget it" approach, assuming that the purchase and installation of a platform is all that is needed. This can lead to an over-reliance on AI assistants.

We recommend investing some of the time savings in monitoring and strengthening how these tools fit into the overall CI/CD pipeline and processes. For example, if the data shows extremely high approval of coding assistant recommendations, this could indicate an over-reliance on the assistant and suggest that a culture change is needed. Conversely, if the data shows extremely low approval of recommendations, this could indicate either resistance to adoption or that the AI coding assistant platform itself has not been properly trained on the organization's codebase.

In either case, dedicating some time cycles to monitoring and continually improving how your AI coding assistants are used will yield stronger results over time. 

6. Accelerate the onboarding of junior developers and new hires

Learning an existing codebase can be very challenging for early-career talent and junior developers coming into the organization. Some of our clients noted that it can take six months or more for junior developers to become productive, and even then, they will not fully understand the breadth of the codebase. Developing the deep expertise needed to fully support the organization can take years. 

One overlooked advantage of AI coding assistants is their ability to help examine the application codebase and architecture, making it much easier to understand the various software files, modules and interfaces. There is a vast difference between understanding "what" a piece of code is doing and "why" it works within the overall code system.

AI coding assistants can accelerate onboarding, provide real-time feedback and help junior developers understand complex systems. This strengthens your talent pipeline and ensures a steady flow of future experts and senior developers. One client shared they reduced the time for a newly hired developer to understand the codebase enough to confidently make changes, from six months to two to four weeks.

7. Jump-start innovation

Many organizations struggle to innovate at the speed their organization demands and at the scale needed. There is, of course, no shortage of ideas. What we have observed is that there tends to be a resource gap between ideation and scaling these concepts for production execution.

AI assistants can provide much-needed capacity to form innovation teams, run hackathons, or partner with business units to explore new products and revenue streams. AI coding assistants lower the barrier to experimentation, enabling rapid prototyping and iteration. If well integrated into the software delivery framework, these rapid prototypes can be more easily scaled to production using the same toolsets.

Conclusion

AI coding assistants are very powerful platforms that can deliver a truly rare opportunity to unlock significant time and people productivity gains. There is an opportunity to fundamentally transform both the software delivery process and the way the organization deploys resources, including the possibility of finally addressing legacy application technical debt, improving the cybersecurity posture and jump-starting innovation.

As you adopt AI coding assistants, we recommend developing a clear plan to redeploy productivity gains toward your organization's top challenges and position you for a long-term strategic advantage.

The short-term cost savings are tempting, and pressures from senior leadership will be present. However, we believe there are more important long-term gains to be realized.

References:

Gartner Says 75% of Enterprise Software Engineers Will Use AI Code Assistants by 2028

AI Coding Assistant Statistics & Trends [2025]

AI Coding Assistants ROI Study: Measuring Developer Productivity Gains

New Research Reveals AI Coding Assistants Boost Developer Productivity