Lev often describes data quality as a long-standing “boogeyman” in the market research industry, one that has dominated conferences and discussions for over a decade. He points out that concerns around fraud alone can account for 30% to 40% of survey samples, but he also emphasizes that the problem is broader than that, driven by issues like inattentiveness, weak survey design, and poor panel hygiene.

For VP-level leaders in marketing and insights, Lev uses this framing to highlight a critical risk: decisions are only as strong as the data behind them. When data quality is weak, organisations risk building strategies, campaigns, and product decisions on unstable foundations, leading to misallocated budgets, misleading insights, and ultimately slower, less confident decision-making at scale.

Lev explains that at aytm, they are “blessed” with consistently low, single-digit levels of data quality issues, which he attributes to how the business evolved and how its panels are built and managed. Rather than treating data quality as a fixed constraint, he positions it as something that can be systematically improved through design, discipline, and continuous iteration.

At the core of his approach is what he calls “scientific honesty”: a mindset of curiosity and rigour where the goal is to constantly distinguish good data from bad data and strengthen the system over time.

Lev has spent years building a profitable, bootstrapped SaaS company without external funding, developing a perspective that resonates strongly with senior marketing, insights, and branding leaders. More importantly, he can teach them how to apply these principles within their own organisations.

In an industry where many companies prioritise speed and scale through investment, his approach has focused on steady, earned growth. As aytm evolved from serving startups to agencies and enterprise clients, growth came from continuously adapting the product to real user needs. A key part of this has been staying in constant “listening mode” rather than following a fixed expansion plan, showing how sustained growth often comes less from predefined strategy and more from responding directly to market signals.

Aytm is often described as an “octopus company,” because its growth didn’t follow a fixed, linear plan. Instead, the core platform was shaped through continuous experimentation and close user feedback, with different “arms” of the business emerging naturally as new research needs appeared across customer groups, offering growth leaders a lens into how insight functions can expand across multiple areas without losing coherence.

Operating this way created a natural discipline where every decision had to prove its value, driving clear prioritisation and sustainable growth. For Lev, it ultimately comes down to building with intention, staying close to users, and growing from a strong, responsive foundation.

Lev helps senior insights professionals understand how the consumer insights landscape is evolving across three coexisting waves: Full Service, DIY, and the emerging agentic AI model. By framing these shifts, he helps organisations make sense of how insight work is changing, not in a linear way, but through multiple systems operating in parallel.

The first wave, Full Service, is traditional, human-led research and still makes up a large share of enterprise spending. It’s deeply embedded in how many Fortune 500 organisations run insights today. Lev points out that while it’s still important, it’s no longer the default way companies get answers, as faster alternatives are increasingly being used or layered alongside it.

The second wave, DIY, introduced self-serve platforms that let teams run research on their own, making it faster and more accessible. aytm is well known for operating in this space. Lev uses this lens to help leaders see both sides: the clear gains in speed and ownership, but also the challenges that come with it, like keeping quality consistent and making sure insights are properly used across the wider organisation.

The third wave, agentic AI, is where Lev sees the most rapid acceleration. He helps leaders think through how AI-driven systems are beginning to automate and augment large parts of the research process, fundamentally changing how insights are generated and consumed. His perspective helps teams prepare for a shift where insight functions move beyond just managing tools, and instead focus on coordinating intelligence across both human input and AI systems.

Lev has helped build a culture where empathy is used as a practical decision-making filter, not just a value statement. Every product decision is tested against how real people will experience it, helping close the gap between internal assumptions and actual customer reality.

Empathy also shapes how feedback is interpreted. Instead of dismissing unclear signals, the team digs deeper to understand user friction, an approach insight leaders can use to make sense of messy or incomplete data in complex enterprise environments.

Lev also believes curiosity is critical for senior leaders who need to stay ahead of shifting market dynamics. By encouraging their teams to question assumptions and stay open to being wrong early, organisations avoid locking into outdated thinking.

Creativity, in his view, is a practical tool for enterprise problem-solving. It helps teams rethink constraints and improve how research and product experiences are delivered, rather than being limited by existing structures.

Lev believes there is enough opportunity to go around, and that companies perform better when they operate from generosity rather than scarcity. At aytm, this shows up in being quick to respond, quick to help, and genuinely invested in others’ success. He believes teams with an abundance mindset build stronger relationships, collaborate more effectively, and ultimately create more opportunities than they lose by sharing.

Finally, Lev believes in organic growth, an approach that offers senior leaders an alternative to scale-first strategies, focusing instead on steady improvement driven by real user feedback and continuous iteration.

Lev believes there are significant tensions and growing pains as the insights industry shifts from traditional ways of working to new ones shaped by AI and the decentralisation of insight creation. 

He sees this transition as a natural evolution rather than something being “broken,” as value moves away from a small group of specialists toward a model where insights can be generated and used by many people across the organisation, even without a formal research background.

 For many insights professionals, this shift can feel unsettling, as long-established expertise, processes, and ways of defining impact are being re-evaluated in real time. This creates a sense of uncertainty, but also opens up space to rethink how the function fits into a more distributed, faster-moving environment.

Lev encourages insights professionals to use this moment to reinvent their role, and for leaders to help their teams rethink how they contribute, rather than resisting the changes underway. In his view, the key challenge is not the loss of control, but the need to stay relevant in a system where insight creation is becoming faster, more accessible, and more widely owned across the business.