| SDLC stage | Effort, % | Intellias-validated AI, min % | Gap to existing AI |
|---|---|---|---|
| Requirements analysis | 10% | 2.20% | 1.7x |
| System design | 15% | 9.10% | 1.8x |
| Testing & QA | 20% | 12.00% | 8.0x |
| Rollout | 15% | 7.38% | 2.5x |
| Rollout | 15% | 7.38% | 2.5x |
| Total | 100% | 20.00% | 1.7x |
From AI Experiments to 30% Efficiency Gains in Software Delivery for a Top Retailer
Working with a leading UK building materials retailer, Intellias improved software delivery by embedding AI-enabled engineering into development processes
AI can improve Software Delivery Life Cycle (SDLC) when applied in the right
processes. For our client, that means up to 30% efficiency gains across the SDLC.
Working with a large enterprise retailer, Intellias applied and evaluated advanced AI tooling across the software development lifecycle. The engagement delivered clear, stage-specific insights and demonstrated AI’s measurable impact on delivery speed and engineering efficiency.
Intellias AI SDLC efficiency analysis
Client’s in-house AI tools
11.43%
efficiency gain
AI tooling validated by Intellias
Best case scenario,
maximum impact
30%
efficiency gain
AI tooling validated by Intellias
Minimal impact
20%
efficiency gain
Effectiveness ratio
Intellias-selected vs. Existing tools
1.7x
efficiency gain
Challenge:
AI adoption with clear impact
The client had already introduced AI tools into engineering workflows. However, the results were inconsistent and difficult to measure. The client needed clearer answers:
Where does AI actually improve delivery performance?
Which tools deliver the most value?
And how much efficiency can realistically be achieved across the full SDLC?
At the same time, the organization needed to understand whether additional investment in external solutions was justified.
The core challenge was to accelerate software delivery by identifying which
processes should be enhanced with AI, measuring their real impact, and scaling
the most effective improvements across engineering workflows.
Solution
What we did:
Analyzed the entire SDLC:
Requirements
System design
Development
Testing & QA
Involved cross-functional teams:
Frontend
Backend
QA
DevOps
Management
Evaluated dozens of real work items:
Requirements analysis
Release validation
Compared
Internal AI tools already used by the client
Advanced external AI tools
Our approach:
Current AI assistants act as copilots for isolated engineering tasks. Agentic AI expands this model by enabling autonomous, goal-driven execution across multiple development activities. Instead of measuring isolated gains, Intellias assessed AI impact across the SDLC as a connected system, where overall performance is constrained by bottlenecks between stages.
This allowed the team to calculate realistic, end-to-end efficiency improvements, not inflated theoretical gains. The long-term shift is toward AI-orchestrated SDLCs, where interconnected AI agents coordinate software delivery end-to-end, accelerating development while keeping humans in control.
AI tools impact by SDLC stage
Intellias-validated AI tools (min) Intellias-validated AI tools (max)
Key findings
Clear outcomes: From experimentation to accelerated delivery
The investigation provided a clear answer to the client’s core challenge: how to accelerate software delivery by applying AI to the right engineering processes.
Based on validated results, Intellias and the client defined realistic efficiency targets of 20–30% improvement using advanced AI tooling.
In practical terms, this enables:
Faster delivery cycles through automation of routine engineering tasks
Increased output without scaling teams, by accelerating execution-heavy workflows
More efficient use of engineering capacity, enabling teams to focus
on complex problem-solving instead of repetitive work
Software delivery was accelerated
by restructuring key processes with AI support.
Rather than reducing costs through downsizing, the client achieved:
Higher delivery throughput
Shorter time to value
Cost optimization driven by productivity gains
Stage-by-stage breakdown
What began as fragmented experimentation evolved into a data-driven model for accelerating software delivery at scale.
The investigation became the foundation for a more effective, system-level approach to AI adoption in software delivery.
AI usage is now guided by measured impact across specific SDLC processes
High-value use cases are prioritized and scaled across teams
Tooling decisions are aligned with proven performance improvements, not assumptions