AI systems are complex, adaptive, and often unpredictable. They require us to shift our mindset from thinking business systems are deterministic (inputs give predictable outputs) to being probabilistic (inputs can generate a range of possible outputs).
Whether used for operations, marketing or decision-making, in our opinion, they demand rigorous QA processes to ensure they perform safely, fairly, and reliably.
Why is QA of AI systems crucial?
Reliability Builds Trust
Comprehensive and structured quality assurance testing of AI systems builds confidence and understanding that they will consistently perform as expected, and the circumstances where they may not. Without QA, flawed outputs can erode user confidence and disrupt projects.
You might want to ask yourself how many times you are willing to forgive AI “hallucinations”?
Data Integrity
AI is only as good as its data. QA processes can validate data quality, helping to prevent biased results.
For example, at a very basic level, our experience of providing prospective clients with GA4 and Big Query health checks is that the simplest data analysis set ups for marketing are often not correct. This results in signals from incorrect data informing the media tech stack leading to marketing inefficiencies.
Smooth Scaling and Integration
QA enables integration with existing systems and supports scalability, minimising costly downtime or compatibility issues.
In addition to solving performance marketing and coding challenges with the integration of AI enabled functionality in our own business, our Accelerate Team are exploring “what is possible” for many of our clients to aid their AI journey.
Risk Mitigation and Compliance
Early detection of bugs, security flaws, and compliance gaps protects businesses from legal and financial fallout.
When working with financially regulated businesses for example, we are navigating highly sensitive compliance requirements. In many instances, our application of AI rules helps solve issues and ensure compliance.
However, we recommend you are mindful that your QA review should consider your employment policies to ensure that they are fit for purpose and provide clear guidelines on the application of AI in the work environment. It’s important to ensure AI access to sensitive documents is restricted based on user permissions.
Performance Optimisation
Smart testing and automation can enhance AI system efficiency, reduce costs, and accelerate routes to market.
For example, the creation of scripts that solve coding challenges provides us with additional time to focus on the quality of our outputs. Whether it’s a website, campaign optimisation or media planning AI can give us more time to focus on accelerating growth. However, all AI solutions require human checking and validation to ensure quality.
Continuous Monitoring
Unlike static software, AI evolves. QA must be ongoing, tracking performance, accuracy, and fairness as models adapt over time.
It’s important to work with a partner that you feel is a great cultural fit for your business. Maximising the value of AI for us is about:
- Having a solid base of dependable data – if not present already, we help Clients solve their data analytics challenges, and
- Applying the most appropriate technology to feed off the data to fuel business growth.
Our people work as an extension of our clients’ teams to ensure data integrity and marketing efficiency.
Data | Technology | People combining to build long-term, successful business relationships.
In Summary, why is QA crucial when using AI in your business?
QA is the backbone of trustworthy, scalable, and value adding AI. Treating it as a crucial part of your processes—not an afterthought—is key to delivering great AI solutions that help transform your business and your marketing.
If you would like to discuss our and our partners approach to working to a Quality Assurance framework that is tailored to your business and challenges, please get in touch.
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| Author | Andrew Burgess |