Shanon has spent nearly a decade scaling aytm, a fully remote, bootstrapped company, something many people underestimate. Choosing not to rely on external funding early on isn’t an easy feat, and she sees it as an earned luxury that only becomes possible once a company has built enough stability and self-sustaining growth to support that level of independence.

Shanon still recognises the important role that venture capital and private equity play for many companies and doesn’t view external funding negatively. For her, the decision was less about rejecting that path and more about prioritising freedom and control over ease at a particular stage of the company’s journey. 

In her view, the real learning is not about whether companies take money, but how they use capital wisely, regardless of its source. Many companies today are expected to operate with similar discipline to aytm even when funded, which makes intentional decision-making and financial clarity more important than ever.

Her approach to scaling prioritises steady, intentional growth, creating a more stable operating environment with clearer decisions, aligned teams, and less risk of over-hiring and eventual layoffs.

Shanon highlights a major shift in the industry: research is no longer limited to dedicated specialists. With AI tools making data more accessible, more people across organizations can now generate insights.

This shift reflects a new reality she has experienced firsthand, coming from a marketing background rather than traditional research. She represents the growing group of “insight seekers” who use research as part of their role, not as their formal job title, and while she is not an insights professional or technical expert, she has worked with it closely enough to understand how it is used in practice.

But she also notes that this creates a new challenge: many of these users are not trained researchers, which makes it easier to misread data or rely on tools without fully understanding the methodology behind them. 

This is where she focuses on bridging the technical expertise gap and building tools that enable smart research for people who do not have a research background. In this environment, the key issue is whether the tools and systems guiding non-experts are strong enough to produce reliable outcomes.

Shanon believes AI is transforming research, but the real shift is not about automation replacing people but how people choose to work with it. In her view, AI is less like a tool and more like an output-producing system that needs to be actively directed, checked, and corrected, almost like managing an assistant rather than using software.

She argues that the biggest risk is how quickly polished AI answers can feel “correct.” Because AI outputs are fluent and confident, people can start accepting them without questioning the data or the assumptions underneath. Over time, this reduces critical thinking.

That’s why she sees the real skill shift as control, not prompting. The value is in how deliberately people feed context into AI, how aggressively they test what it produces, and how consistently they act as the final layer of quality control.

In her view, transformation only works if humans stay actively in charge of thinking, not just reviewing what the system produces. This reflects the broader culture she helps shape at aytm, where people are encouraged to think critically, move fast, and stay hands-on in how they use new systems rather than passively relying on them.

Shanon challenges the idea that strong leadership means having all the answers. In fast-changing environments, confidence can look more like certainty than clarity, and certainty becomes outdated quickly. Pretending to know more than you do can actually reduce trust rather than build it.

Instead, she believes the strongest leaders operate with intellectual honesty. They are open about what they are testing, what they are unsure about, and what is still evolving. This creates space for teams to think more openly and adjust without feeling locked into early assumptions.

For Shanon, leadership is less about rigid models of “disciplined experimentation” and more about staying flexible in how learning happens. She values the willingness to iterate, adjust, and rethink direction as new information comes in.

Shanon believes being wrong is part of how learning happens, as long as you acknowledge it and adapt quickly. The value comes from staying close enough to reality to recognise mistakes early and adjust. The best leaders, she believes, are not the most controlled thinkers, but the most adaptable ones. This is also the kind of environment she helps foster at aytm, where openness and adaptability are encouraged over appearing certain or overly rigid in structure.