AI-assisted engineering phase 1: Laying the groundwork for autonomous engineering

TL;DR:

• To start with integrating AI into SDLC the first phase is to focus on AI-assisted engineering to lay the groundwork for future phases.
• The phase’s ley initiatives are the systematic evaluation and adoption of AI copilots, basic ROI observability via telemetry, governance, and quality guardrails.
• This “foundations first approach” will have 80% human-driven and 20% AI-assisted autonomy.

Introduction

In the first blog post of this three-part series, we introduced the concept of leveraging tools with Generative AI capabilities to accelerate the Software Development Life Cycle (SDLC). This post will focus on the first phase: the implementation of AI-assisted engineering.

This phase is crucial for setting up the groundwork for the subsequent phases. It will focus on enabling copilot acceleration, implementing telemetry, observability, and metrics. It will also ensure AI-assistance quality and artifact validation.

During this phase, engineering teams can achieve significant productivity and quality gains while complying with enterprise guardrails and controls. They will achieve this by systematically evaluating and adopting AI copilots, implementing telemetry for ROI observability, and establishing governance and quality guardrails. Those productivity gains will help support further investments into subsequent phases.

Foundation

This first foundational phase focuses on enabling copilot acceleration, implementing telemetry, observability, and metrics, and ensuring AI-assistance quality and artifact validation. This phase is crucial for setting up the groundwork for the subsequent phases.

This phase is built around three key initiatives:

1. Systematic evaluation and adoption of AI copilots: This involves assessing and integrating AI copilots into the existing SDLC architecture and processes, without major structural changes or workflow disruption.
2. Basic ROI observability via telemetry collection and tracking: To measure this phase’s results’, we need to Implement appropriate telemetry to measure and track the return on investment.
3. Governance & quality guardrails: From the start, it’s critical to identify and implement essential governance and quality measures to ensure the smooth functioning of AI-assisted engineering.

Productivity and quality gains

The implementation of AI-assisted engineering is expected to yield significant productivity and quality improvements.

• Productivity gains in routine tasks such as code generation, test generation, documentation generation, and DevOps automation.
• Code quality improvement resulting in reduction in defects. Those results are driven by clearer requirements and automated, comprehensive testing.

The expected gains will vary significantly per use case, adoption, and enterprise. They will be measurable through the implementation of appropriate telemetry and insights dashboards.

For example, an April 2025 Entrepreneur magazine article shared that integrating AI into the SDLC at companies such as Google, Meta, and Microsoft resulted in 20 to 30% of the final code being AI generated.

Change management

The change management level for this phase is moderate, focusing on tool-specific training and adoption programs. The human role in this phase includes decision-making, execution, and validation, while the AI acts as a suggester, assistant, enhancer, and accelerator.

This limited impact on how software engineers’ usual processes allow for better solution adoption and, therefore, faster time-to-results.

Technical solution

The recommended technical solution follows a foundation-first approach with incremental capabilities added in each phase.

During Phase 1, the software development process is 80% human-driven and 20% AI-assisted. The end-to-end software engineering flow involves AI suggestions, human review, and human implementation. For this phase, the AI copilot will have high impact but limited capabilities:

• AI workflows are human-triggered.
• The AI agent is task-specific, not process-wide.
• It will require direct guidance (prompt engineering) from developers and DevOps engineers.
• Its output will be limited to a single context.

In phases 2 and 3, the AI autonomy will increase to 50% in Phase 2 and 80% in Phase 3. The human role will evolve accordingly. First to a supervisor and exception handler in Phase 2, then to a strategic, governance, and exception handler role in Phase 3. In parallel, the AI will transition from a task executor, researcher, analyzer, and recommender in Phase 2 to an autonomous executor making optimization decisions in Phase 3.

Conclusion

The first phase of implementing AI-assisted engineering lays the foundation for a context-aware, autonomous, human-AI symbiotic engineering ecosystem. By systematically evaluating and adopting AI copilots, implementing telemetry for ROI observability, and establishing governance and quality guardrails, we can achieve significant productivity and quality gains.