AI-Driven Testing: How to Use AI for A/B Testing
By April Miller, Technology Writer at ReHack has over 5 years of experience writing about small and enterprise business technology. You can find more of her work on ASBN and ReHack and connect with her on LinkedIn.
The internet makes countless possibilities accessible with a few clicks. Whether someone needs a new outfit or a reputable electrician in their area, websites provide jumping-off points for future transactions while providing information to help people make confident decisions. A/B testing allows quality assurance testers, software engineers and others to see which versions of apps, websites and online interfaces perform best in the real world.
It also lets them experiment with addressing specific audiences, such as potential customers from particular age groups or locations. It only takes seconds for someone to decide whether to stay on a website or click to a different one. A/B testing helps developers clarify which aspects matter most to audiences and how to highlight them.
The rise of artificial intelligence has also encouraged people to use AI for A/B testing. The technology is an attractive option that can save time and optimize results. Software professionals can then meet visitors’ expectations and encourage desirable behaviors, such as conversions and repeat visits. How should they incorporate this technology into their work?
Identify the Business Case
AI has become so ubiquitous in contemporary life that some executives have chosen to begin utilizing it without verifying a compelling business case. Leaders should speak directly to software developers, engineers, testers and others who run or design A/B tests.
Their feedback can determine if genuine pain points exist and whether AI could feasibly solve them. In some cases, the best approach is not to implement another technology, but to improve existing processes to remove inefficiencies and error-causing issues.
In one customer-facing example, a team spent a year developing a chatbot that included hundreds of potential scenarios. However, only a tiny percentage of people interacted with it because the tool did not solve any real pain points. Software experts who want to use AI for A/B testing must ensure there is a need before proceeding.
Use AI to Simulate User Personas
Using AI to improve A/B testing may be easiest when companies invest in existing products that feature the desired capabilities. Building a tool from scratch may fit niche cases, but it often makes more sense for broader applications to purchase commercial tools.
Blok is a startup that uses AI within a simulation engine to predict user behavior before software developers write any code. It creates various user personas to cover most of an app’s expected user base, with the users also inputting a hypothesis to test and the desired user goal. The results indicate how users would interact with a particular feature and give recommendations to improve it if needed.
The company’s founders spoke to more than 100 product engineers to learn about their most common problems. Getting those perspectives helped them create something professionals would actually need and want to use.
Let AI Agents Handle Time-Consuming Steps
AI agents have become popular among leaders exploring new, effective ways to use technology in the workplace. These software tools perform sequences of autonomous steps, helping users get more done in less time. Although some earlier forms of AI merely responded to single requests, agents are more goal-oriented. People simply specify what they want to achieve and the agents can begin doing what is necessary to achieve the outcome.
Reviewing internal data to identify which parts of A/B testing or other quality assurance tasks typically take the most time helps project managers target the processes best suited to AI agents. An industry analyst survey indicated respondents believe 50% of organizations will rely on AI agents by 2027 to streamline collaborative efforts and minimize routine administrative work. That outcome would let workers spend more time on valuable work.
Leaders may decide to gradually introduce agents into workflows and carefully track associated metrics before expanding the rollouts. This strategic approach helps them spend money wisely by verifying desired outcomes before proceeding.
Test and Verify All AI Tools
AI has many powerful business use cases, but users should never explicitly trust the results. Managers should strongly consider formulating processes, checklists, or other resources to help developers and quality assurance specialists check AI for accuracy. Otherwise, the technology may replicate or amplify existing biases, making it far less useful for A/B testing.
Because quality assurance is an accuracy-driven effort, executives should not plan to remove humans from processes. Instead, they should supplement their expertise with technology. People may be responsible for verifying that AI delivered trustworthy information about A/B test results and checking its processes for abnormalities.
Apply AI to Tackle Known A/B Testing Shortcomings
Selection bias can occur when people either participate in or reject offers presented to them on websites or elsewhere.
Some experts suggest using machine learning to estimate the likelihood of someone claiming an incentive or showing disinterest. Software experts can then adjust their approaches based on those weighted tendencies.
That possibility shows the value in using AI for A/B testing to overcome known challenges. Companies and teams can maximize their technology use while continuing to apply other best practices to understand and quantify behavior.
Make Strategies Organization-Specific
Professionals can anticipate positive outcomes by implementing these practical suggestions to understand how to use AI for A/B testing. However, they should always tweak the suggestions to fit organizational needs and software professionals’ feedback. Tailoring use cases leads to success and satisfaction for all involved.