Accelerating Release Velocity by 30% in Healthcare Tech Using AI Assistants

A leading healthcare tech company overcame release delays and resource constraints by integrating AI development tools, achieving a 30% boost in productivity. The result: crunching first-to-final draft time, expediting unit tests, 50% lower maker-manager time, faster merges and pull requests.

About the Client

Leading innovation in metabolic healthcare

Our client brings together the fruits of innovation in an optimized care ecosystem where the success and health of each individual is paramount. They have been developing technologies with a proven medical model refined over 30+ years of clinical success. With a highly agile development process, the company typically ran 10–12 concurrent projects every quarter, organized into rapid sprints. Speed, accuracy, and reliability were essential to maintain a competitive edge in a regulated industry.

The Challenge

Releases falling behind schedule, straining team members

In mid-2024, the client’s leadership, particularly the CTO and Director of Engineering, identified a growing bottleneck: despite efficient sprint planning, product releases were frequently delayed by 1–2 weeks. The core issue was a need for increased development productivity—specifically, shipping more features and fixing bugs faster—without the option to increase headcount.

The team was under constant pressure to meet quarterly major releases and fortnightly minor ones, but the growing workload was straining available resources.

Our Solution

Driving momentum with AI-powered conversational assistants

Rather than expanding the team, we partnered with the client to explore AI-powered developer productivity tools. We used GitHub Copilot and Cursor AI, supported by internal training on:

Writing effective prompts
Reducing AI hallucinations
Sharing best practices across sprints

Within weeks, measurable productivity gains emerged:

The tools flagged common coding mistakes (e.g., variable declarations) in real time

Developers leveraged autocomplete to write code faster.

Routine tasks like clearing tickets, writing unit tests, debugging, and implementing features were accelerated—saving 2–3 hours per ticket on average.

Encouraged by early success, we scaled usage to the broader development team. The CTO personally tracked AI effectiveness, hosted regular check-ins to share prompt engineering insights, and encouraged experimentation.

Business Impact

Achieved peak release velocity with minimal defects

The impact of our AI-augmented solution was transformational, across multiple dimensions.

0%
increase in release velocity
0%
reduction in time spent on bug fixes

2-3 hours

saved per customer ticket

Productivity gains

30% increase in productivity, based on hour-by-hour comparisons between March–April 2024 and the same period in 2025.
Average 2–3 hours saved per ticket, across all developers.

Maximum release velocity

Major releases that were previously delayed by 1–2 weeks began shipping on time.
Minor releases, previously at risk of backlog, were consistently delivered as scheduled.
Sprint capacity increased, allowing the team to squeeze in more features per cycle.

Minimum bugs

Time spent fixing bugs dropped from 4 hours to 2 hours per issue.

Fewer customer-reported tickets and post-release issues.

Zero rollbacks on AI-assisted code.

Enhanced unit test coverage and bug prevention from first to final drafts.

Lower manual effort

Developers wrote fewer lines manually thanks to AI-assisted completions.
More consistent code quality with fewer uncaught bugs or regressions.

Client Testimonial

The Director of Engineering was particularly enthusiastic about how many additional releases were achieved within standard timelines, and how AI tools became integral to the team’s day-to-day development practices.

We were skeptical at first, but the results speak for themselves. With AI tools and the right enablement from our solution partners, we’ve accelerated our release velocity, reduced bug counts, and improved code quality—all without increasing our headcount. This has transformed how we build and ship software.

Director of Engineering,
Healthcare Technology Company

This case study demonstrates how strategic AI tool adoption can overcome resource constraints while delivering superior business outcomes in competitive markets.