How collaborative, human-centered research Is shaping the future of technology decision-making
As a researcher, my approach to understanding technology buyers and market trends has been shaped by the mentors and leaders who came before me. Working under several accomplished women on the research team prepared me to interrogate not just the data we collect, but also the gaps and inconsistencies within it. One of the most valuable lessons I learned early in my career was to leave space for what might be missing. Asking participants, "What do you wish I had asked you about that I haven't yet?" often surfaces the most revealing insights - those that signal emerging trends or disconnects between how vendors position themselves and how buyers actually behave.
Today, as I lead research at TrustRadius, that principle remains foundational. Our annual buyer research report, for example, examines software buyer preferences alongside vendor go-to-market motions. The most compelling findings frequently lie in the grey areas where those two perspectives diverge. Understanding those disconnects is essential for producing research that is not only accurate but also actionable for decision-makers navigating an increasingly complex technology landscape.
My research process is inherently collaborative. At the outset of any project, I convene stakeholders to align on the desired outcomes. We define what success looks like and articulate the specific questions we want answered by the end of the study. This ensures that the research is anchored in real business needs rather than abstract curiosity. From there, I work closely with our data analysts to assess what information we already possess, what additional data is required, and how best to collect it. Whether we are designing surveys, conducting interviews, or synthesizing existing datasets, the emphasis remains on collaboration and iterative refinement.
Translating data into meaningful insights requires more than statistical rigor; it demands thoughtful storytelling. Quantitative data provides the necessary foundation for credibility, but numbers alone rarely guide action. Decision-makers need interpretation - context that clarifies what the data means and how it should inform strategy. To address this, we incorporate open-ended survey questions and qualitative interviews that allow respondents to articulate their experiences in their own words. These narratives humanize the data and make the findings more accessible without sacrificing analytical depth.
Ensuring that our insights reflect diverse perspectives is also central to the integrity of our work. Collaboration again plays a decisive role. Brainstorming with a broad internal team helps surface blind spots that any single researcher might overlook. In addition, our access to a global community of software buyers allows us to capture viewpoints across geographies, industries, and roles. When selecting qualitative quotes, we make a deliberate effort to include voices that might otherwise be underrepresented, including those of women who are both working in and purchasing technology. This approach strengthens the representativeness of our findings and ensures that the research resonates with a wider audience.
In my experience, women bring a distinctive strength to research-led roles: empathy informed by lived experience. Many women in technology have spent time as the only woman in the room, an experience that sharpens awareness of inclusion and perspective. That awareness translates into research that is more attentive to nuance and more sensitive to the varied realities of technology buyers. To borrow a well-known line from the film Mean Girls, the limit does not exist; the capacity for women to lead in research is not constrained by discipline or domain, but rather expanded by the breadth of perspectives they bring.
When evaluating emerging technology trends and buyer confidence, I rely on a combination of primary research and competitive analysis. Reviewing content published by other research firms helps identify which narratives are already saturated and which perspectives remain unexplored. Sometimes this reveals genuine gaps - areas where new data can offer a fresh, differentiated viewpoint that advances industry understanding.
Balancing quantitative rigor with human-centered storytelling has been one of the most important lessons of my career. Data must be statistically sound, yet also interpretable and relevant. Researchers cannot assume that audiences will intuitively grasp the implications of a dataset. Providing clear interpretation alongside the numbers is essential to turning information into insight and insight into action.
There are, however, persistent misconceptions about technology research roles. One common belief is that leaders must be experts in every element of the research process. In reality, effective leadership often involves recognizing one's knowledge gaps and learning in real time while leveraging the expertise of collaborators. Women, in particular, may underestimate their readiness for leadership roles, assuming they must master every competency before stepping forward. My experience suggests the opposite: growth and credibility are built through engagement, not perfection. For context, I moved into leadership 4 months into my tenure at TrustRadius. The amount that I have grown in both research and leadership skills is immeasurable. I am grateful that I got pushed off that metaphorical professional cliff by other women who sat me down and said something to the effect of "you know enough to get started, and what you don't know, we'll teach you".
Looking ahead, the evolution of AI and data automation will significantly reshape how technology buyers make decisions - and how researchers support them. AI excels at processing large datasets and automating repetitive analytical tasks, enabling both researchers and buyers to operate with greater speed and scale. Yet these capabilities also introduce new risks, including the potential for inaccuracies and overconfidence in automated outputs. Human judgment, critical thinking, and rigorous fact-checking will become even more vital as guardrails against these challenges.
For women leaders, this moment presents a meaningful opportunity. Many organizations are still defining their AI strategies, policies, and ethical frameworks. This period of experimentation creates space for researchers to shape best practices, develop new competencies, and assume more strategic leadership roles. Tying this back to my earlier point around what it feels like to be the only woman in the room, this presents a unique opportunity for women to work together to develop new skills and develop more women into leaders. By combining analytical rigor with empathy, collaboration, and a commitment to inclusive perspectives, women can play a defining role in how technology research evolves in the age of AI.