Investing in AI-assisted software development
In today's fast-paced technological landscape, federal agencies are increasingly recognizing the transformative potential of AI-assisted software development. A staggering 99% of federal agency mission leaders believe that investing in safe and effective AI is essential to achieving their organization's goals.
The mandate for federal IT leaders is clear: explore AI, modernize IT systems, and fortify security to enhance government services. Amid these challenges, generative AI (GenAI) emerges as a powerful catalyst for software development, offering tangible benefits that align with these objectives.
At ICF, we understand the profound impact GenAI can have on the Software Development Life Cycle (SDLC). By integrating GenAI into our AI Accelerated Framework (A2F), we are not only enhancing the quality of our code but also streamlining development processes to achieve higher efficiency. We are currently leveraging GenAI to realize moderate gains in areas such as automated code documentation and bug detection. Looking ahead, we anticipate that the evolving utility of GenAI will drive even more extensive efficiencies, enabling faster development times, increased throughput of user stories per sprint, and ultimately, lower development costs.
We are investing in AI as a long-term efficiency tool. We’re not just looking at current sprints—we’re looking 1-3 years ahead, making sure we’re meeting today’s requirements while preparing for future needs and challenges.
Our commitment to this innovative approach is underpinned by our Responsible AI principles, which ensure that our use of GenAI is both safe and effective. By prioritizing these principles, we are navigating today's requirements while positioning ourselves to meet the evolving demands of the future.
Why you should use GenAI in software development
GenAI’s ability to rapidly create and evaluate complex patterns is perfect for software development’s clearly defined process and iterative nature. There are three main benefits of applying GenAI to software development:
Quality: Automated approaches to testing broaden coverage and create opportunities for deeper dives. For example, introducing GenAI to your testing approach means that more software defects can be found and fixed before production. Or, similarly, if a team is only able to manually test 3 out of 10 code solutions, GenAI enables them to automatically test all 10. This increases the likelihood of detecting issues before the code goes into production and allows manual testing to become more exploratory, testing more diverse uses of the software and validating it meets user needs.
Efficiency: Some parts of software development are tedious but necessary. Using GenAI for aspects like code documentation means the workforce can instead focus on higher-level, more creative, and more sensitive work, such as architectural guidance, code reviews, and mentoring. By incorporating AI into your SDLC, you’ll also free up time to communicate with customers and end users to ensure alignment—which contributes to higher-quality products and services that meet mission objectives.
Customer efficiency gains: Greater quality and improved efficiency combine to create a development process that lets the customer either require less time for the same results, or spend the same and get more for it: With GenAI, we can have fewer people working on the project, which results in increased speed of development, or we can keep the team the same size but move faster. In our work for CDC, for example, we used AI to generate the base code for a website, saving time and cost while finding the optimal balance between generative AI and human oversight.
How we do it
Plan
Create
Verify
Summarize code modifications and review comments; identify security vulnerabilities; and test data generation with synthetic data.
Release
Continuously verify releases; predict change failures by assessing release readiness; and automatically enable/disable feature checks.
Configure
Create configuration automation recipes; continuously detect and fix configuration drift.
Monitor
Recognize patterns, detect anomalies, and self-heal; optimize workflows; use predictive analytics to forecast future needs.
We also tap into our powerful ecosystem of technology partners to ensure we’re implementing the best possible option at every step. Our approach is to invest in AI as a long-term efficiency tool. We’re not just looking at current sprints—we're looking 1-3 years ahead, making sure we're meeting today’s requirements while preparing for future needs and challenges, laying a foundation for stable and secure growth and improvement.
To establish that success, we have created a playbook for GenAI implementation and consistency across the full SDLC of every federal project. IT has always evolved rapidly. GenAI is useful already, but if it’s implemented correctly now, it will be exponentially more powerful and helpful in the years to come. Our playbook helps us create and deliver responsible AI solutions that are appropriate for government projects, integrate easily, and maintain information security.
