Forrester Boomerangs: A Q&A Series

One in 15 Forresterites is a boomerang, a former employee who has rejoined the company. Boomerangs are an important part of Forrester’s cultural fabric, bringing new skills, fresh perspectives, and a rekindled passion to their roles. Our Boomerang Q&A series highlights the stories of people who have made their way back to Forrester.

Senior Analyst Rowan Curran returned to Forrester in early 2022 after a stint in the public sector. The timing was fortuitous — both for him and for Forrester. After having previously covered predictive analytics and search and knowledge discovery, Rowan soon began spearheading Forrester’s coverage of synthetic data and large language models (LLMs). When generative AI (genAI) took off with the launch of ChatGPT in late 2022, Rowan — and Forrester — were perfectly positioned to seize the opportunity.

We sat down with Rowan to discuss what first led him to Forrester, his ongoing fascination with the human-tech relationship, and the lessons he’s learned in his career thus far.

Q: What first brought you to Forrester in 2012?

RC: A friend who was working in sales at Forrester recommended that I apply to be a research associate (RA). Knowing that I’m a curious person, she thought it would be a good fit for me. I had graduated from Emerson College the year before and was interning at the Berkman Klein Center for Internet and Society at Harvard, which is a think tank focused on digital human rights and ethics. I was very interested in how humans and technology interact, and the opportunity to learn about the latest cutting-edge technologies at Forrester was appealing. I applied and was hired as an RA on the application development and delivery team.

Q: How did you progress from an RA to a researcher and then an analyst?

RC: I was very proactive and eager to learn and contribute. Whenever there was a topic that I was particularly interested in — or if there was something an analyst wanted to work on but didn’t have time to — I would offer to help. If I’d read something I found interesting and thought an analyst would, too, I would share it, even if it wasn’t an analyst that I was supporting as an RA. I pretty much fanboyed analysts that I thought were doing cool things [laughs]. That often would lead to a larger conversation, or even to my working with them on a piece of research.

By the time I left Forrester at the end of 2016, I was an analyst covering predictive analytics, search and knowledge discovery, and geospatial analytics. The mentorship from my managers and other analysts around me played a huge part in my success. They believed in my abilities, they encouraged me, and they trusted me — even in situations where I wasn’t entirely sure of my own capabilities.

Q: You left Forrester in 2016 and worked for the Massachusetts Department of Youth Services for five years. How did that come about?

RC: I had varying degrees of impostor syndrome throughout my time as a researcher and analyst, since I was covering topics that I hadn’t worked on as a practitioner. I worried about being successful long-term if I didn’t have a firsthand understanding of some of the problems our clients experienced. Then I learned about an opportunity with the state’s Department of Youth Services to help build the data and analytics team and really mature their practice. It felt like the timing was right. Since the department’s focus was juvenile justice, I also felt like I could do good work and have a societal impact. But I hoped to return to Forrester at some point.

Q: How did what you learned help you once you returned to Forrester?

RC: I had a much better understanding of the behind-the-scenes problems that don’t necessarily come up in research interviews but that can slow clients down when they’re trying to make changes. I understood that it could take half a year or longer to implement a new enterprise software/data warehouse platform — even for a very small deployment. I knew how frustrating it could be to run a few lines of code over and over to try to get something to run but then it doesn’t, and so you work with a vendor for a few months to make adjustments. I was able to empathize more with the sorts of problems our clients face.

Q: How did you come to cover genAI and LLMs?

RC: It really was a case of preparation meeting opportunity. When I came back to Forrester in 2022, I started covering the evolved versions of the research areas I had covered before. Search and knowledge discovery had become cognitive search; the predictive analytics space was now the AI machine learning platform space. One of the new things I wanted to delve into was synthetic data generation, which included image and virtual environment generation as well as LLMs. Through our research, we experimented with a lot of cutting-edge tools and were blown away by their capabilities.

We started to get more inquiries that fall on LLMs, and I was taking them as part of my synthetic data coverage as well as my cognitive search work — LLMs had emerged as a key part of knowledge retrieval. Then, ChatGPT was released at the end of that November, and we’ve all seen what’s happened since.

Q: Recognizing the tremendous potential benefits genAI brings to businesses, you and other Forrester analysts were quick to also highlight the potential risks. What should people keep in mind when using genAI?

RC: Right after ChatGPT was released, we knew we needed to publish a blog post about the challenges of LLMs providing inaccurate or even biased information — particularly since these tools had gone from relative obscurity to mainstream use overnight. Organizations needed to recognize that it’s important to maintain the transparency and governance around genAI that exists around their other content generation processes and AI applications. That continues to be a challenge we help them with today.

Q: What do you enjoy most about Forrester, and what excites you most about what’s ahead?

RC: Forresterites are a really wonderful group of smart and hardworking people who not only care about the work but about each other — and how the work we do with our clients impacts the world. The concentration of curiosity and genuine openness to learning and refining perspectives here is unique. Having the opportunity to influence how a cutting-edge technology gets adopted on a very large scale is something that not many people get to do — and even fewer get to do with colleagues they admire professionally and personally. I’m super excited to see how genAI and AI more broadly continue evolving and to keep helping clients in this space. We’re helping the future take shape. What could be more challenging and fun than that?

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