Have you ever wondered if AI might take over optimisation entirely, leaving little need for human input? It’s a question that comes up a lot, and it’s no surprise, given how fast AI is advancing. Clients often ask me:
- What’s AI’s impact on A/B testing and optimisation?
- Will human involvement still matter?
- Where can AI help most with strategy and implementation?
There’s a lot of hype (and fear) about AI out there. This article dives into these questions and shares my perspective.
AI brings some serious advantages to the table:
Let’s face it: no matter how good you are with numbers, there’s only so much complexity one person can handle. Managing countless audience segments, especially with variables like user activity, preferences, or location, is overwhelming. AI takes this off your plate, making hyper-personalisation scalable and achievable.
Think about short-term campaigns, like holiday sales or back-to-school events. Waiting for traditional A/B test results isn’t practical in these cases. AI algorithms can predict what works best for each user in real time, helping you maximise impact before the moment passes.
While AI is great for personalisation, there’s a lot that only A/B testing can deliver:
Before AI can optimise, you need strong foundational elements—things like page layouts, imagery, and messaging. A/B testing helps you figure out what works best, giving AI a solid base to build on.
Even the best designs can fall apart in the real world. A/B testing lets you test new features or designs on a small scale, so you can fix problems before they affect your entire audience.
Not every test is a winner, and that’s okay. Some of the biggest insights come from so-called “failed” tests. The key is digging into the data to understand what didn’t work and why.
A/B testing is more than running a few tests and picking a winner. It’s about designing experiments with clear goals and collecting meaningful insights. This approach helps you make informed decisions and consistently improve.
The best optimisation strategies combine the strengths of both A/B testing and AI. AI helps you scale and speed up, while A/B testing ensures your foundation is solid and your decisions are grounded in real-world insights.
AI isn’t perfect, and it comes with a few risks:
- Data-Driven Bias: AI is only as good as the data it learns from, which might not reflect your unique audience, especially early on.
- Missing the Human Touch: AI can’t talk to customers or understand how they feel. A/B testing, on the other hand, can integrate human insights and best practices.
- Ethical Concerns: AI can accidentally (or intentionally) amplify harmful design practices, like dark patterns that trick users into doing things they don’t want to.
By combining A/B testing with AI, you can build a smarter, faster, and more ethical optimisation strategy. Want to chat more about this? Drop us a line at hello@daydot.agency. We'd love to help!