As AI lowers the barrier to building and experimenting, the critical product differentiator shifts from aesthetic taste to strategic discernment. Miller emphasizes that because teams can now build almost anything rapidly, product managers must apply sound judgment to determine which problems to prioritize, why they matter, and how urgently they need solving, filtering out ideas that lack real customer value.
Perk frames AI automation around identifying and removing 'shadow work'—the rote, manual, and cumbersome tasks buried in enterprise workflows like expense logging and reconciliations. By having AI automate these low-value operational burdens, B2B products free users and administrators to concentrate their energy and judgment on critical strategic choices rather than repetitive administrative overhead.
Despite predictions that conversational bots, voice, or CLI tools will replace graphical user interfaces, Miller argues diverse cognitive styles ensure multi-modal UIs will remain essential. Some users think visually, while others prefer text. In workflows like corporate booking, clicking a single button or checkbox remains faster and less intrusive than conversational prompts, meaning AI agents will complement rather than eradicate traditional interfaces.
Rather than enabling startups to easily displace legacy software with superior UX, AI may actually strengthen massive incumbents. Miller explains that enterprise incumbents hold massive embedded value and financial capital. This advantage allows established players to deploy AI to simplify complex interfaces and segment customer workflows faster than smaller challengers who face steep compute and operational costs.
Instead of starting with a narrow wedge, Perk anchored on corporate travel—a massive, operationally complex problem involving localized cross-border compliance, CFO policies, and approvals. Mastering this complex foundational workflow enabled seamless natural expansions into corporate spend cards, expense reconciliation, and corporate events, since they share the same employee profiles, managerial approval workflows, and procurement administration.
Nikita Miller notes that while AI tooling does not eliminate core crafts like design or engineering, it allows smaller teams to execute faster. Designers increasingly handle front-end coding, while engineers engage directly with UX. By reducing team headcount and encouraging specialists to flex across disciplines, organizations preserve deep craftsmanship while accelerating their overall product velocity.
For early-career product managers navigating rapid AI shifts, reading widely remains the most durable skill investment. Miller argues that foundational learning through extensive reading sharpens clear thinking and articulate expression. These capabilities are crucial not only for traditional team storytelling and defining customer problems, but also for effectively instructing AI systems and designing reliable prompts.