It’s time to get comfortable with the struggle again
A Reality Check in the Silence
It started with the current connectivity blackouts in Iran. For a significant period, the internet was unavailable, and suddenly, the entire product operation at a leading InsurTech company—an environment that usually prided itself on speed—came to a complete standstill. Watching the team struggle during this period was a revelation. The critical issue wasn't the lack of tools; it was the collective inability to face a blank page. The environment had become so conditioned to having AI build the "base" components. When the infrastructure was gone, the ability to think from scratch seemed to have disappeared. It was a clear sign of "structural laziness"—a state where the professional mind, adjusted to instant gratification, struggles to manually craft a deep analysis without a digital prompt.
Skipping the "Digestion" Phase: The Heavy Price of Speed
In the pre-AI era, finding a pattern in product management was a "slow burn". A PM had to spend hours wrestling with SQL queries, cleaning messy data in Excel, and failing through multiple hypotheses before reaching an insight. But that "waiting time" and that "struggle" weren't wasted; they allowed the brain to internalize the data. The data would settle in the mind, connect with past experiences, and eventually give birth to Product Intuition. One had to "live" with the problem. Today, AI hands us the answer in 30 seconds. We have eliminated the vital "digestion" phase. As a result, many teams now have the "answer," but they lack the "understanding" of that answer. They own results whose roots they don't recognize. This is why, when the tools are gone, we aren't just professionals without software; we become professionals who are "strangers" to our own business because we never let the problem sink in.
The "Question-to-Answer" Trap
The healthy problem-solving flow in Product Management is: Question -> Ambiguity -> Struggle -> Hypothesis -> Answer. The most critical part of this journey is the uncomfortable but necessary stage of Ambiguity. AI has collapsed this path into: Question -> AI -> Answer. We no longer allow the question mark to sit in our minds. The moment an unknown is faced, the instinct is to run to the machine to suppress the anxiety of "not knowing" with a ready-made response. But true innovation is born exactly in those moments of frustration—where we give up on easy answers and start seeing connections that no algorithm has spotted yet.
The Commoditization of Thinking
AI is brilliant at finding correlations, but it is often blind to causality. Causality is the "Human Why" that isn't recorded in databases. When a PM’s tolerance for ambiguity drops, they lose the ability to discover the hidden layers of user behavior; they simply "rewrite the past". If professional value is reduced to just "editing machine output," the real work is no longer being done. We are simply automating cognitive decline. A Product Manager who cannot function without an AI co-pilot is not a strategist; they are a Prompt Operator whose analytical muscles have atrophied.
The Systemic Barrier: The Employer’s Trap
The reality is that deep thinking requires a resource that modern corporate environments often treat as a waste: Time.
Many professionals don't turn to AI out of choice, but out of survival. Under systemic pressure, employers frequently demand a volume of output that exceeds sustainable human capacity for a single role. This forced "efficiency" turns AI from a tool into a crutch. When a workplace rewards the quantity of tickets closed over the depth of the insight, it effectively subsidizes structural laziness. We aren't just fighting our own cognitive habits; we are fighting a work culture that prizes the speed of the machine over the irreplaceable intuition of the human.
Conclusion: Bringing Back the Strategic Muscle
In the age of AI, a Product Manager's competitive advantage is no longer "speed". It is the capacity to tolerate ambiguity and the power of independent analysis. To wake up our strategic muscles from hibernation, we must rethink our operational capacity:
- Deconstructing Tasks: Instead of outsourcing entire outputs to AI, we should break tasks into smaller, manual micro-steps that force the PM to engage directly with the "Why" before touching a prompt.
- Capacity for Deep Thinking: We must recognize that AI has artificially inflated our capacity while thinning our depth. By intentionally reducing task loads, we allow PMs the dedicated time needed to wrestle with data manually and rebuild their intuition.
- The 10-Minute Rule: We must learn how to walk in the dark of unsolved problems without the flashlight of AI. No query should be sent to a machine before at least ten minutes of independent hypothesis-making.
Real Product Management is the art of asking questions that don't have a pre-recorded answer in a machine's memory. It’s time to get comfortable with the struggle again—because that’s where the real value lives.
