Discover how Coherent Solutions built SPARK, an AI-powered enterprise knowledge platform that transforms internal data into measurable business value.
Organizations typically introduce AI into QA and other departments, solely to accelerate individual tasks. Few actually consider redesigning the delivery flow, which can improve efficiency and continuous feedback throughout the software development lifecycle (SDLC).
Recognizing the benefits of using AI to streamline processes, Coherent Solutions developed a comprehensive implementation strategy to embed AI into its QA workflows. The goal was to reduce manual effort and ensure insights from each delivery cycle informed the next.
The result was not just faster execution and a 35% cut in QA testing effort, but an early shift toward AI-native delivery behavior, where quality improves through compound engineering rather than repeated effort.
The challenge
As expectations for delivery speed increased, QA teams were spending more time recreating test documentation, revisiting planning assumptions, and maintaining regression coverage. Manual processes fell behind as requirements changed, and quality relied too much on individual know-how.
AI tools were available, but inside Coherent’s traditional SDLC, they mostly sped up individual steps without improving how the entire delivery workflow functioned. Each sprint felt like starting over, which made it hard to scale or build on what teams had already learned.
Before embedding AI, Coherent’s QA outcomes depended largely on tribal knowledge. It was hard to improve systems and processes over time because each team took their knowledge and improvements with them at the end of projects.
Our approach
Instead of using AI just to increase delivery speed, the team integrated the technology into redesigned QA workflows, so what they learned in one cycle was carried into the next.
The team began by building language models for basic QA tasks, such as planning, documentation, regression testing, and analysis. By grounding these language models in historical test artifacts, defect data, and prior execution outcomes, the team enabled the system to surface recurring risks, inform scope decisions, and adapt regression coverage as requirements evolved.
Engineers remained responsible for decisions and validation, while AI handled the heavy lifting of routine test documentation, generating drafts from evolving requirements, and synthesizing backlog and execution data to propose coverage adjustments and defect patterns.
As these workflows matured over time, QA work shifted away from redoing artifacts toward applying judgment based on automated system insights.
This approach laid the foundation for principles later formalized in Coherent Solutions’ Continuous Delivery Loop. The Continuous Delivery Loop is an intent-first delivery model in which human expertise and AI work together to retain context, apply learning, and continuously improve outcomes across delivery cycles, rather than resetting at the start of each sprint.
The solution
OpenAI and Claude models were integrated into the existing QA toolchain and tied together through automated workflows. Together, these were used to:
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Keep test documentation up to date as requirements changed.
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Shape test plans based on past execution and defects.
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Adjust regression coverage as requirements evolved.
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Standardize bug reporting and traceability.
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Surface testing risks earlier using backlog and execution data.
The AI-enabled QA framework became a core part of how Coherent Solutions delivers software. By embedding AI directly into workflows rather than using it to improve individual tasks, the team integrated AI into its delivery fabric. This allowed the technology to function as a crucial automation and support partner, delivering consistent, reliable results.
The impact
The initiative led to clear, measurable improvements in QA and delivery workflows:
Each delivery cycle required less manual effort, which wasn’t due to automating tasks. Instead, manual effort lessened as the AI-powered systems transferred knowledge between projects.
Planning speed also increased because risks were identified earlier. Additionally, as artifacts evolved instead of being recreated, documentation took less effort. Over time, quality became more predictable as feedback loops tightened.
The real value of this initiative emerged in more predictable releases, reduced operational volatility, and delivery gains that compound over time — outcomes that align with Coherent’s Digital Value Creation framework’s focus on scalability, efficiency, and measurable business results.
Why this matters
Many organizations experimenting with AI are still working with older SDLC models that don't retain information, reinforce silos between teams, and rely heavily on manual processes. In those environments, AI speeds up individual tasks but doesn’t really change how delivery works, which stalls progress.
This initiative demonstrates a simple principle driving Coherent Solutions’ AI strategy: When AI is built organically into work, rather than added as an afterthought, teams can build momentum and scale efficiently. This is referred to as AI as fabric: AI is embedded into everyday workflows, so learning, context, and informed decision-making compound over time instead of needing to be recreated sprint after sprint.
Tasks are easy to optimize. Changing how delivery works is the hard part.
What’s next
Coherent Solutions’ QA project laid the groundwork for extending intelligent workflows into other delivery processes. Going forward, the focus isn’t on more automation, but on finding better ways to carry feedback and learning across the lifecycle.
This work also helped clarify what it would really take to scale AI across the organization over time.
For organizations assessing their own progress, the question becomes clear: where do you stand on the path to AI-native delivery? Are you moving towards organic integration or are you still tripping over manual processes?
