Augmented Engineering with AI-First Approach
AI tools are everywhere, but adoption in engineering processes is random, with developers experimenting individually while teams lack coherent integration strategies.
At Techstack, we identified three key challenges: speed/quality limitations, economic inefficiency, and AI talent gaps. We asked: How can we adopt AI systematically to speed up development and enhance code quality? How can we convert random AI usage into measurable efficiency?
Our team tackled these challenges head-on. In just two sprints, the core engineering team achieved up to 35% velocity increase while maintaining quality. This case study shares our approach, challenges, and results—offering an honest view of AI-first engineering.
Industry:
Automation processes, Digital Transformation
Services:
AI & ML, Consulting Service
Location:
United States
Challenge
Techstack's engineering team faced several ongoing challenges that were limiting productivity and scalability:
Lengthy code reviews and quality assurance processes
Repetitive coding tasks consuming valuable engineering resources
Inconsistent development velocity across sprints
Limited capacity for innovation due to ongoing maintenance needs
As development requirements grew, we needed a solution to help our team achieve more. We wanted to achieve this without compromising quality or increasing headcount.
We had to focus on product ownership and use AI as a tool to augment engineering expertise. While it was intended to help us optimize routine tasks, it required careful adoption to ensure alignment within the team. We didn’t aim to replace existing development expertise, but rather to assist in the workflow.
Balancing this approach with the need to increase development velocity was a complex and challenging task.
Solution
We developed a comprehensive AI-powered engineering approach that directly addressed our core challenges:
Comprehensive metrics framework
Created a clear way to measure real impact across our teams.
Built a three-pillar measurement system: Productivity metrics: code completion rates, language usage, team velocity Quality metrics: bug density, code coverage, defect rates Adoption metrics: tool usage, engineer satisfaction, feature utilization
Established baseline metrics before implementation to ensure accurate comparison
Experimental methodology
Started small to learn what actually works before scaling.
Selected core engineering teams as test groups
Emphasized an "AI-first" mindset: start with AI, then refine with human expertise
Conducted a controlled experiment across two sprints with consistent measurement
Technical stack optimization
Focused on what mattered most in our development environment.
Prioritized our most-used languages: JavaScript/TypeScript, Terraform, and YAML
Customized AI tools for our specific development patterns and project needs
Put in place security measures and NDA agreements to protect our intellectual property
Strategic AI adoption
Integrated AI tools thoughtfully to support our teams, not disrupt them.
Implemented GitHub Copilot as the foundation for real-time code assistance
Developed contextually-aware custom AI assistants that understood our codebase
Gradually integrated AI tools (e.g. GitHub Copilot, Cursor, ChatGPT, Claude) into existing workflows
Workflow transformation
Targeted the most time-consuming parts of our development process.
Unit tests are no longer a pain: delegate writing 90% of unit tests to AI, reducing preparation time by over 70%
Implemented early-stage code reviews using AI before PR submission
Created and shared best-work combinations like VS Code & Copilot & Claude
Enabled AI-powered brainstorming for architectural solutions
Knowledge sharing
Built a culture where teams learned from each other's AI successes.
Established systems for collaborative learning across teams
Created feedback loops for engineers to share successful strategies
Documented effective prompts and approaches for common tasks
Built an internal knowledge base of AI usage patterns
Technologies Used
Strategic selection of AI tools and languages to maximize adoption and results formed the foundation of our approach, balancing technical capabilities with team expertise and project requirements.
The workflow
Our AI-adoption in engineering unfolded in three distinct phases, each with its own challenges and breakthroughs that transformed our workflows and elevated team productivity.
Integration & setup
Laying the foundation for AI-augmented development:
Selected engineering teams with complex products
Integrated custom AI assistants and out-of-the-box solutions
Established security measures and NDA agreements
Initial training focused on basic AI-assisted cases
Initial exploration
Building confidence through low-risk experimentation:
Launched two-sprint experiment with "AI-first" directive for all tasks
Started with simple use cases code generation
Identified language-specific adoption patterns
Increased AI assistant for unit test generation
Created collaborative learning environment for sharing insights
Expanding capabilities
Scaling AI usage to more complex engineering tasks:
AI-augmentation code reviews before formal PR submissions
Collaborated with AI in brainstorming and solution exploration
Maintained quality standards with consistent bug density metrics
Measured team velocity improvements & AI-penetration
Presented final results for teams and stakeholders
About the team
For this AI-adoption initiative, we took a streamlined approach. Instead of creating a specialized team, we assigned one engineering manager drove our AI adoption effort. The manager collaborated directly with leadership on strategy, with architects on integration, and with engineers on day-to-day usage. This approach speed up adoption and allowed AI tools to integrate naturally into existing workflows without disrupting team dynamics.
Team composition
Engineering manager
1
Impact
The results delivered measurable business value across all key metrics:
Accelerated productivity & efficiency
Routine tasks transformed into development velocity.
Up to 35% increase in team velocity (measured in story points) compared to previous sprints
Engineers spent 65-70% less time on repetitive tasks
Approximately 3.5K AI-generated code lines accepted from 20K+ suggestions
TypeScript showed the highest AI acceptance rate, offering language-specific optimization opportunities
Consistent code quality
Despite faster development, quality remained stable throughout the transition.
Bug density remained unchanged as teams actively integrated AI-generated code (～30%)
AI-assisted code maintained the same quality standards as human-written code
Developers used AI to find edge cases and improve test coverage
Organizational transformation
Engineers became advocates for expanding AI integration across more workflows.
Teams reduced review cycles through AI-assisted early-stage code reviews
AI handled repetitive coding tasks, freeing engineers for higher-value work
Initial investment in AI tools quickly offset by reduced need for multiple third-party subscriptions
Team members created knowledge sharing systems to spread successful AI practices
