Accelerating Delivery With AI: Transforming How We Build And Deliver Software
Estimated reading time: 6 minutes
Key Takeaways
- Parsons integrates AI into the Software Development Lifecycle (SDLC), transforming software delivery and operational efficiency.
- Key advancements include Spec-Driven Development, Copilot CLI, and reusable AI skills, enabling faster development cycles.
- AI enhances design-to-deployment processes, linking Figma with GitHub Copilot to streamline UI creation.
- Security is embedded within the SDLC, allowing for earlier detection and quicker resolution of vulnerabilities.
- Through AI-powered software delivery, teams achieve significant productivity gains and redefine modern software engineering.
A Platform-Driven Transformation
Over the past year, our software development groups have transformed how they build, secure, and scale software by embedding artificial intelligence (AI) into both the Software Development Lifecycle (SDLC) and the products we deliver.
This shift began with a strategic investment and partnership with Microsoft, which gave us enterprise access to GitHub, GitHub Copilot, and GitHub Advanced Security. What started as a platform enablement effort quickly became a developer-led transformation across Parsons’ Intelligent Infrastructure business unit. AI is no longer an add-on. It now shapes daily development, team structures, and product delivery.
“By operationalizing AI in both our development workflows and our products, Parsons is redefining how intelligent infrastructure software is built, secured, and delivered.” — Ricardo Lorenzo, Chief Technology Officer, Parsons
AI-Driven Development at Scale
Since May 2025, more than 100 developers across Intelligent Infrastructure have adopted AI-enabled workflows and embedded intelligence into daily development.
Key advancements include:
- Spec-Driven Development (SDD), which improves planning and code quality.
- Copilot CLI and agentic workflows, which accelerate automation.
- Reusable AI skills and agents, which standardize delivery.
The iNET® Data Hub team best demonstrates this impact. Developers built an AI-driven pipeline with reusable skills, specialized agents, and continuous feedback loops. As a result, they reduced complex development efforts from 14 days to approximately one day.
This shift does more than accelerate delivery. It frees developers to focus on higher-value problem-solving while AI handles repetitive, time-intensive tasks. Our developers did not just adopt AI, they operationalized it and turned weeks of effort into day-scale execution.
Accelerating Design to Deployment
AI also removes friction between design and development. By integrating Figma, a cloud-based design and collaboration tool, with the Figma Model Context Protocol (MCP) server, a standardized bridge that enables AI models to securely connect to Figma and GitHub Copilot, teams can generate near-production-ready user interfaces from simple prompts.
This approach keeps outputs aligned with branding and coding standards while eliminating traditional handoff delays. The result is a faster path from concept to deployment and greater agility in responding to user needs.
Security Built into the SDLC
We now integrate security directly into development instead of addressing it later. Using GitHub Advanced Security, Dependabot, and Copilot-assisted remediation, teams identify vulnerabilities and security risks earlier in the lifecycle. These tools provide real-time recommendations in pull requests, which allows teams to resolve issues during development rather than after release.
This proactive approach has improved continuous risk detection and reduced the time needed to resolve security findings by 30 percent. We are not just finding issues earlier, we are fixing them faster, often before they reach production.
AI Innovation in Our iNET Products
The same AI capabilities transforming our SDLC now power the iNET® platform:
- iNET® Sidekick uses Azure OpenAI to enable natural-language interaction with system data and help operators make faster decisions.
- iNET® Video Analytics uses real-time computer vision to detect traffic events, automate alerts, and improve situational awareness.
- Asset Guardian combines vision, LiDAR, and GPS to identify roadway defects such as potholes and signage issues, with active pilots underway in U.S. and international markets.
Together, these innovations improve operational intelligence and deliver measurable value to customers.
“The most compelling proof of AI’s impact is what customers can actually feel, and that’s what Parsons is delivering. By embedding Microsoft AI, including Azure OpenAI and GitHub Copilot, directly into the iNET® platform, Parsons is making infrastructure genuinely intelligent. It’s a powerful example of innovation moving from the developer’s desk to real outcomes for the governments and citizens Parsons serves.” — Jamie Harper, VP, Defense Industrial Base, Microsoft
Modernizing at Scale
Modernizing a large software portfolio is essential to maintaining a secure, reliable, and supportable codebase, but it can be expensive and time-consuming. To accelerate this work, our experts developed an AI-powered refactoring workflow to migrate legacy iNET® modules from Ant to Gradle.
The result was a major productivity breakthrough. The team migrated more than 60 modules in one week, a task that would have taken at least two months through a fully manual approach.
This capability reduces cost, improves maintainability, and helps ensure that critical systems remain secure and scalable over time.
Innovation Across Teams
Across Parsons, teams are applying AI in new ways to solve complex challenges.
The Utility Enterprise Data Management team used AI-enabled development to build a demand response product internally in a matter of weeks, avoiding the need for third-party development. The solution included expanded automated testing, a full demo environment, and the ability to meet significantly more compliance requirements.
At the same time, the Smart Vehicle Solutions group is using AI to transform quality assurance by embedding GitHub Copilot into testing workflows. The team implemented an end-to-end process that automatically generates test cases from requirements, builds and maintains automation scripts with awareness of the live UI, and adapts those scripts as code changes occur. The system now automates test execution, cycles, and results reporting, while also generating detailed bug tickets for QA review.
This approach reduces manual effort, improves test coverage, and increases confidence in release quality.
The Impact
By embedding AI across development and products, our experts are fundamentally changing delivery outcomes. Development cycles are shrinking from weeks to days. Teams are identifying and resolving security issues earlier. They are also unlocking new levels of productivity and innovation.
Most importantly, this transformation highlights the ingenuity of our teams, who find new ways to apply AI to real-world challenges every day.
Looking Ahead
What began as a platform investment has become a strategic advantage.
As we continue to scale agentic workflows, AI-assisted development, and integrated security, we are strengthening our position at the forefront of modern software engineering.
With the right tools and the right talent, we are not just accelerating delivery. We are redefining how software is built and how intelligent infrastructure is delivered.