In our quest to become more customer-centric, many of us have poured resources into expanding our research capabilities. We've added more people to conduct research, implemented new tools, and gathered vast amounts of data. Yet, despite these efforts, we often find ourselves grappling with a paradox: increased research activity doesn’t always translate into better insights or more informed decision-making. Instead, we risk mistaking the map for the territory—thinking that more data automatically means better understanding.

Today, we’re standing at the crossroads of a pivotal transformation in how we conduct research and how we view our roles within product organizations. It’s time to rethink our practices, embrace new modalities, and redefine our contributions. Let’s dive into the key transformations needed to achieve better outcomes in product research.

 1. From Gatekeepers to Facilitators: Sharing Ownership of Research

Historically, researchers have often acted as gatekeepers of customer insights. We’ve held the keys to data collection, analysis, and interpretation, believing that our specialized skills were necessary for extracting valuable insights. However, this approach can create bottlenecks and limit the flow of information within the organization.

Transformation: Empowering Others

Instead of insisting on conducting all user interviews ourselves, we can train product managers and designers to run their own user sessions through AI-powered qualitative research surveys using tools like Metaforms. By providing them with templates, guidelines, and feedback, we allow them to gather real-time insights and foster a more collaborative research culture.

Practical Use Case: 

At a recent product launch, our team equipped customer support specialists with a structured framework for conducting post-launch user feedback sessions. This not only sped up the feedback loop but also enriched our understanding of user experiences from a different perspective, leading to more nuanced product iterations.

 2. From Isolated Experts to Integrated Partners: Building Cross-Functional Relationships

In many organizations, researchers often operate in silos, disconnected from other teams. This can lead to misaligned priorities and a lack of understanding of how research insights fit into the broader product strategy.

Transformation: Becoming Integrated Partners

Embed researchers within product teams to ensure that research is aligned with development goals and timelines. This closer collaboration helps us understand the context of product decisions and ensures that insights are immediately actionable.

Practical Use Case: 

In one project, I embedded a researcher with the development team working on a new feature. This integration allowed the researcher to provide immediate feedback on user testing, which directly influenced the design decisions, resulting in a feature that was better aligned with user needs and more quickly implemented.

 3. From Rigid Processes to Agile Methods: Adopting Flexibility in Research

Traditional research methods can be rigid, with long cycles of data collection and analysis. In the fast-paced world of product development, this can slow down decision-making and make it difficult to keep up with changing user needs.

Transformation: Embracing Agile Research

Adopt mixed-method approaches that combine qualitative and quantitative research, using iterative cycles to rapidly test hypotheses and gather insights.

Practical Use Case: 

When developing a new mobile app, we employed an agile research approach, running weekly sprints that included quick user testing sessions and rapid data analysis. This allowed us to adjust our design and functionality in real-time, significantly improving the final product’s user experience.

 4. From Data Collectors to Insight Architects: Designing Effective Research Ecosystems

Collecting data is only part of the equation. The real value lies in how effectively we can turn data into actionable insights. However, many organizations struggle with integrating and making sense of the vast amounts of data they gather.

Transformation: Building Insight Ecosystems

Develop systems and frameworks for managing data collection, participant recruitment, and insights dissemination. Use AI tools to automate repetitive tasks and focus on synthesizing insights.

Practical Use Case: 

We recently implemented a centralized insights platform that uses AI to categorize and prioritize user feedback from multiple channels. This streamlined our analysis process and ensured that insights were easily accessible and actionable for all team members, leading to more informed decision-making across the board.

 5. From Report Generators to Strategic Advisors: Focusing on High-Value Activities

Creating comprehensive reports has been a staple of our work. However, these reports often get buried in inboxes and fail to drive actionable outcomes. We need to shift our focus towards more strategic activities that directly influence product decisions.

Transformation: Becoming Strategic Advisors

Instead of producing lengthy reports, deliver key insights through briefings, dashboards, and workshops that engage stakeholders and drive action.

Practical Use Case: 

In a recent engagement with our executive team, we replaced traditional reports with interactive dashboards that highlighted critical user metrics and trends. This shift not only made the data more accessible but also facilitated more dynamic discussions around strategy and priorities, leading to quicker and more informed decision-making.

 6. From Traditional Roles to New Modalities: Adapting to Emerging Technologies

AI and new technologies are transforming our tools and methodologies. As researchers, we need to embrace these technologies to enhance our capabilities and explore new roles.

Transformation: Adopting New Roles and Modalities

Utilize AI-powered tools for qualitative data analysis, employ predictive analytics, and engage in prompt engineering to maximize the potential of AI in research.

Practical Use Case: 

By integrating AI tools like Dovetail for qualitative data analysis, we significantly reduced the time required to synthesize interview insights. This allowed us to spend more time on strategic planning and less on manual data processing, ultimately leading to more timely and impactful research contributions.

 Conclusion: Embracing the Future of Product Research

As product researchers, we are at a transformative juncture. To deliver better outcomes, we must move beyond our traditional roles and embrace new ways of thinking and working. By becoming facilitators, partners, agile practitioners, insight architects, strategic advisors, and technology adopters, we can enhance our contributions and drive more significant impact across our organizations.

Let’s leverage the power of AI and advanced AI-native survey builders like Metaforms, Dovetail and to rethink our practices, integrate more deeply with our teams, and focus on strategic activities that elevate our roles. The future of product research is not just about doing research but about shaping how research drives every decision and action in our product development journey.