"No one is using this! What should I do?"
It's a familiar frustration for many product managers. Your AI-powered feature is live, but users aren't engaging with it. The technology is impressive, but it's not solving the right problem.
Why? Because it was built without a clear understanding of what users actually need.
In the rush to stay ahead of AI trends, many teams prioritize engineering over research, leading to products that may be technically advanced but lack real user value. This approach can result in wasted resources and missed opportunities.
As a Product Manager, you constantly balance user needs, business goals, and technical feasibility. Incorporating product research into your AI development process isn't just about creating better products—it's about driving real business results:
In short, product research isn't just a nice-to-have—it's a critical tool for ensuring the success of your AI initiatives.
While not all of these statistics are specific to AI products, they underscore the critical role that user-centered design and research play in the success of digital initiatives. As AI becomes more prevalent in product development, the importance of product research in this field is likely to grow.
It's easy to get swept up in AI industry trends—automated assistants, chatbots, predictive analytics—but what happens when those experiences don't connect with your users? Often, they fall flat because they weren't designed with real user problems in mind.
Many products today are designed to mimic what's popular rather than being rooted in solid research about user pain points.
When product teams rely on assumptions or jump on trends, they risk building experiences that are more "cool" than useful. The result is impressive technology that doesn't get adopted.
A prime example of this is Google Glass, launched in 2013. Positioned as a revolutionary wearable technology, Google Glass was meant to bring augmented reality to the masses. It allowed users to access the internet, take photos, and receive notifications through a head-mounted display.
However, despite significant hype and Google's technological prowess, Google Glass failed to gain traction in the consumer market and was discontinued in its original form in 2015. This high-profile failure illustrates several key points:
This case underscores the importance of thorough user research and testing in product development. It's not enough to create technologically advanced products; they must be designed with a deep understanding of user needs, behaviors, and social contexts.
By prioritizing user research and testing over simply following trends, product managers can avoid similar pitfalls and create solutions that are not only innovative but also truly valuable and adoptable by users.
So, how do you avoid building AI that misses the mark? The key is research—specifically, exploratory research that goes beyond assumptions and digs into what users truly need. This type of research could include in-depth interviews, contextual inquiries, and diary studies, all designed to uncover user pain points before you start building solutions.
>> Case Study: Exploratory research helps product teams identify the "jobs to be done" that AI can uniquely address. For example, in a recent project, we discovered that users weren't looking for just any automated assistant—they wanted one that could handle complex, nuanced tasks that typical AI tools weren't built to do. By understanding this deeper need early on, the product team was able to develop a more valuable solution.
Once you've identified real user needs through exploratory research, the next step is to test your ideas before committing significant resources. This is where Wizard of Oz testing becomes invaluable.
Wizard of Oz testing allows you to simulate AI functionality without building the full system.
By having a human secretly stand in for the AI behind the scenes, product teams can test how users interact with proposed AI features and gather real feedback on conversational flows and expectations.
>> Case Study: In a recent project, we helped a team refine their AI assistant's onboarding flow using Wizard of Oz testing. We simulated user interactions with suggested prompts and clarifying questions. The findings were clear: users appreciated initial guidance but wanted to maintain autonomy. They preferred just enough direction to navigate the system without feeling constrained. This insight allowed the team to design a chatbot experience that felt helpful and intuitive, striking the right balance between assistance and user control to increase product adoption post-onboarding.
Building an AI product isn't a one-time effort—it's a process that demands continuous testing and refinement at every stage of development. Each phase—from early prototypes to post-launch—benefits from different types of user research.
>> Key Insight: By embedding research at every stage of the product life cycle, you create a product that evolves with your users' needs, improving over time rather than becoming outdated.
At its core, AI is a tool for solving problems. But without user research, AI risks becoming a solution in search of a problem. Effective research helps ensure that AI is not just functional but deeply aligned with the real needs and behaviors of its users.
Through methods like contextual inquiry, usability testing, and in-depth interviews, researchers can uncover users' pain points, mental models, and expectations, providing the necessary direction to tailor AI functionality to real-world use cases. When user needs are properly identified and met, AI feels intuitive, helpful, and valuable. This alignment ensures that AI solves real problems rather than creating new ones.
>> Case Study: To explore how AI could enhance daily routines, we observed participants struggling with multiple disjointed tools and inefficient processes. The research revealed that users desired better integration and identified key moments for automation to simplify their daily tasks and goal management. These insights directly shaped our client’s product roadmap and design priorities, ensuring that development efforts focused on addressing genuine user pain points and needs.
As a Product Manager, you might face several challenges when trying to incorporate product research into your development process:
Here's a simple framework to help you prioritize your product research efforts:
Impact vs. Effort: Apply the impact matrix to determine and prioritize your research opportunities.
Critical User Journeys: Focus your research efforts on the most critical user journeys in your AI product. These are typically:
Risk Mitigation: Prioritize research on aspects of your AI that, if misunderstood or poorly designed, could lead to:
In the fast-paced world of AI, it's easy to get caught up in the latest trends and buzzwords. But as we've explored, the key to building truly impactful AI solutions lies not in following the hype, but in understanding your users.
By integrating product research throughout your AI development process - from exploratory studies and Wizard of Oz testing to continuous usability assessments - you can:
Remember, AI is not just about impressive technology; it's about solving real problems for real people. By making research your guide, you'll build AI solutions that don't just follow trends but set them - driving user engagement, business success, and true innovation.
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Ready to transform your AI development process? AnswerLab's research expertise can help you navigate the complexities of user-centered AI design.