Scaling AI: NIX’s Practical Steps to AI Adoption in Quality Assurance

In the last three years, businesses across domains felt the pressure to adopt AI to automate processes, gain a competitive advantage, and reduce costs. The pressure is even greater for tech companies that need to be at the forefront of technological progress. Despite AI’s indisputable potential, implementing it effectively and at scale poses a substantial challenge. Exploring this topic is Serhii Mohylevskyi, the NIX’s QA Practice Leader, who examined the current AI market and developed actionable steps for adopting AI in quality assurance and beyond.

AI adoption: expectations vs. reality

Since ChatGPT’s groundbreaking release in November 2022, the world has been grappling with the new technological era. Although auspicious, the current AI landscape is brimming with pseudo-revolutionary products that sound too good to be true. NIX’s QA department took on the challenge to investigate the market and identify ways to adopt and scale AI effectively. How can businesses incorporate AI into their processes most efficiently? Are QA engineers at risk of being replaced by AI-powered tools? And finally, what is the right time to start the adoption process?

Adopting AI in quality assurance

When the NIX team was tasked to examine the current AI landscape, we started by thoroughly assessing the existing solutions tailored to quality assurance use cases. The market comprises three main components:

Existing solutions with AI add-ons
New AI products
Misleading AI tools

Upon looking deeper into these products, NIX experts swiftly concluded that most, if not all, of the solutions are overhyped, failing to deliver on their marketing promises. However, they also surveyed QA engineers, revealing that most of the 400 respondents were already using general-purpose AI tools for assistance in test automation, test case and test data generation, content proofreading, and automation of routine tasks.

Instead of investing in an existing subpar solution, NIX decided to find another path:

Start with general-purpose tools: The NIX team pivoted to general-purpose AI platforms and used them for high-value use cases.
Develop AI competency centres: These centres are designed to collect best practices for AI adoption across domains, generate training materials, evaluate new tools and techniques, and share findings with stakeholders.
Implement a tiered adoption approach: NIX identified varying AI needs throughout the organisation, which led to the creation of tiers: high-expertise teams with immediate AI needs, teams with moderate expertise yet potential benefits from AI adoption, and teams requiring more preparation to invest in AI.

To summarise, NIX’s findings showed that the future of AI in quality assurance is promising, if utilised properly and effectively. However, no amount of automation can replace human judgement and expertise that provides critical thinking, creativity, and nuanced understanding of user needs and expectations. NIX’s QA team sees the future of AI as a symbiotic relationship where AI augments human capabilities, allowing engineers to focus on complex strategic tasks.

Conclusion: NIX’s findings

NIX’s investigative journey offers valuable insights into scaling AI adoption:

Although not a panacea, AI can certainly deliver significant benefits when applied correctly. For example, NIX’s mid-to-senior level testers spent 20% less time on test case generation and documentation.
Human expertise remains crucial, especially for complex projects and for the evaluation of AI outputs.
Current domain-specific tools are not mature enough, but general-purpose platforms can be quite useful.
Companies should start the AI adoption process earlier rather than later to allow their teams time to master AI tools and develop best practices.

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