Senior leaders find AI accelerates work but encourages low-quality, uncritical outputs—a phenomenon called 'AI sloth'. To maintain standards, some build AI personas embodying their own perspective, which teams use to vet work before submission, counteracting the deluge of 'junk'.
Despite widespread experimentation with AI across the product development lifecycle, prototyping is the only function that has emerged as a standardized, commonly adopted application. Teams are even using AI prototypes as formal stage gates, while other AI uses remain ad-hoc and experimental.
Senior product leaders report a widespread trend of cutting intern programs and junior PM positions. This focus on senior hires is inadvertently destroying the talent pipeline, raising a critical concern: without these entry-level roles for mentoring, who will be trained to become the CPOs of 2030?
The idea that building with AI is cheap is a dangerous oversimplification. While initial creation is fast, leaders are realizing the immense long-term costs of maintenance, unwinding mistakes, and integrating with legacy systems are substantial and often dangerously overlooked.
While it's easy to measure increased output from AI, like completing more story points, product leaders are failing to connect these efficiency gains to actual business ROI or customer value. This creates a significant blind spot when justifying AI investments.
The common belief that AI gets you 80% of the way there is misleading. The critical 20% that AI misses isn't the final polish; it's the foundational work—setting the right direction, defining the core problem, and ensuring customer grounding from the start.
With AI handling first drafts of documents like PRDs, the PM's value shifts from authoring to editing. The primary job becomes that of an 'editor-in-chief'—questioning outputs, defending the 'why' behind decisions, and ensuring every artifact is grounded in real customer insight and strategic thinking.