Across industries, you see GenAI driving change at astonishing speed. Everywhere you look, GenAI is supercharging service, productivity, and innovation—but progress is slower in highly regulated industries, such as financial services.
Many FSIs use GenAI in software development. According to PwC, GenAI boosts developer productivity by 20-50%. That can accelerate innovation within FSIs because, as development becomes a commodity, you can put more ideas into production, including those built around GenAI. In a world where innovation is easier, quality engineering (QE) becomes more than a trust, technical, and compliance checkpoint. QE serves as a critical accelerator or enabler, especially in highly regulated industries. How can you activate the accelerator? Infuse QE with GenAI-powered automation and empower your quality engineers to make the most of AI in their workflows.
Unite QE and GenAI
FSIs must balance the desire for rapid customer-facing innovation with the need to maintain compliance with financial regulations, and guarantee accuracy and security. QE testing is essential to meeting compliance requirements and maintaining trust within customer experiences that include GenAI. Employee-facing GenAI tools must overcome similar hurdles.
Consider software development. As much as 40% of GenAI-generated code requires remediation. When you can produce code almost instantly, you need similar automation in quality processes to streamline remediation and catch errors likely to cause issues in production. Machine learning (ML) helps by allowing you to apply non-stop routine testing earlier in development. GenAI goes beyond ML by assisting you in automating test generation for specific applications and a diverse range of scenarios. QE also requires test data. Here, GenAI changes the game by rapidly creating regulatory-compliant synthetic test data suited to specific use cases.
Evolve QE with GenAI
When should you begin your GenAI QE journey? Now. Your use of GenAI in QE needs to grow as fast—or faster—than your appetite for AI-powered innovation. Plan to incorporate GenAI into QE in phases, including:
- Assisted GenAI QE: Increase efficiency and accuracy by using GenAI to help quality engineers retrieve answers, identify patterns, and expand insight. Use cases include automated test result summaries and knowledge retrieval with natural language.
- Augmented GenAI QE: Gain velocity and improve consistency with recommendations based on semi-autonomous simulations, with active oversight of a human in the loop. Use cases include analysis of workflows to spot optimization opportunities and automated test design and planning.
- Agentic: GenAI QE: Scale automated, non-stop QE with cognitive AI that manages tasks, takes actions, and adapts to dynamic environments. Use cases include autonomous testing agents and self-optimizing testing pipelines.
As GenAI QE evolves, further accelerate innovation by ensuring efforts include two key areas: prompt engineering and large language model (LLM) optimization.
Harness QE to Optimize LLMs
In the GenAI era, large language models (LLMs) are the engines that drive FSI innovations, such as AI-powered financial coaches, insightful chat, and other types of personalized interactions. Yet, without the proper guardrails, LLMs don’t meet FSI requirements for accuracy and reliability. QE can help optimize LLMs by applying a comprehensive and continuous approach to fine-tuning capabilities.
Traditional quality engineering practices must be injected themselves with GenAI to optimize LLMs. Customer-ready GenAI requires testing focused on context understanding, content evaluation, and continuous feedback. A non-stop feedback loop helps the model adapt, improve, and avoid errors and bias.
Wake up innovation with QE
GenAI-augmented QE is still emerging within most FSIs, yet the businesses moving early are surging ahead. They’re using faster, more accurate QE to fuel growth with innovation. Let their results inspire you to move forward. Outcomes include a 33% decrease in testing design time, 50% improvement in test coverage optimization, a 60% decrease in testing certification time, and a 65% decrease in tester onboarding time.
The next step is Agentic AI — cognitive quality agents capable of executing tasks, adapting to change, and optimizing pipelines with increasing autonomy. Here, QE shifts from automation to intelligent orchestration: humans define the guardrails, while agent-driven systems handle scale, regression, and continuous validation at speed.
In financial services, leadership in AI will be defined not by novelty, but by resilient performance, accuracy, and trust. Agentic AI represents the path to that standard — enabling quality to stay resilient and adaptive as innovation cycles compress, and to evolve as quickly as the intelligence driving development itself.
