How I Use AI to Accelerate A/B Testing in Companies
After years of helping businesses break free from the "single-test paradigm," I've discovered another powerful way to accelerate growth: AI-enhanced testing processes. This isn't about replacing human creativity or strategy—it's about removing bottlenecks and dramatically increasing test velocity.
From Overlapping Tests to AI Acceleration
My journey with AI in testing began when I noticed how much time teams spent on repetitive tasks. Even with overlapping tests running, many companies still struggled with the mechanics of test creation, documentation, and analysis.
Now, AI tools form the backbone of my CRO consulting approach, helping clients implement more tests with less effort.
Real Results from AI-Enhanced Testing
My recent work with inne.io, International Drivers Association (IDA), and Truely has shown the transformative power of AI in testing programs.
At inne.io, we transformed their mobile-first CRO strategy by implementing AI-powered workflows across both German and English markets. We increased test velocity by 400% after integrating AI into our processes. The AI helped generate test ideas and created initial mockups that designers could refine, eliminating a major bottleneck.
With IDA and Truely, we faced a similar challenge—reliance on developer-implemented tests severely limited their testing velocity. By introducing AI-assisted test creation with Convert.com's editor, we went from running a handful of tests to maintaining a robust testing calendar for both businesses. Team members could create and implement tests independently, significantly improving conversion rates for both IDA's International Driver's Permit offerings and Truely's eSIM service.
My AI Testing Toolkit
I use a Kanban tool called HyperTasks with AI built directly into the workflow. With custom AI instructions, I can produce ready-to-implement test tickets in minutes rather than hours. The AI doesn't just document ideas—it contributes genuinely valuable testing concepts based on patterns it's learned.
For test ideation, I've developed specific AI prompts that generate high-potential test variations based on the company's historical data, industry patterns, and current site behavior. This adds diversity to the testing roadmap that might otherwise be missed.
I'm also working directly with testing platforms like VWO to improve their co-pilot AI functionality. These built-in tools are becoming increasingly valuable for companies that want to accelerate their testing programs without adding headcount.
Where AI Delivers the Most Value
The most impressive results I've seen are in these areas:
- Copy testing: AI generates dozens of compelling variant headlines, product descriptions, and CTAs that follow brand guidelines while introducing meaningful variations. At Truely, we used AI to create localized copy variants that resonated with different target markets.
- Process automation: Test documentation, analysis summaries, and implementation tickets can be generated in seconds rather than taking up valuable team time. This was particularly valuable at inne.io, where we needed to coordinate testing across multiple languages.
- Insight synthesis: AI can process thousands of customer service logs, user testing videos, and survey responses to identify patterns humans might miss. We used this approach at IDA to identify pain points in their international permit application process.
- Test prioritization: By analyzing historical test performance and business impact, AI helps ensure we're working on the highest-value tests first. This helped Truely focus on tests with the biggest potential ROI.
Practical Tips for Implementing AI in Your Testing Program
If you're looking to accelerate your own testing program with AI, here's what works:
- Start with process automation: Before diving into complex applications, use AI to streamline documentation and reporting.
- Create company-specific prompt libraries: Develop AI prompts tailored to your industry, audience, and brand voice. These become valuable company assets over time.
- Test the testers: Run small experiments on your AI-generated concepts before implementing them in live tests.
- Combine human insight with AI scale: The best results come when experienced CRO professionals guide the AI rather than letting it run unsupervised.
The Future of AI in Testing
Looking ahead, I see AI playing an increasingly central role in testing programs—not by replacing humans, but by handling the time-consuming tasks that prevent teams from running more tests.
The companies gaining competitive advantage aren't just those using AI, but those integrating it thoughtfully into established testing methodologies. When combined with an overlapping test approach, AI-enhanced workflows can truly transform how quickly companies learn and adapt.
As my work with inne.io, IDA, and Truely demonstrates, the potential for AI to accelerate testing programs is enormous—regardless of industry or company size. The key is starting with the right processes and building from there.