Douglas Rushkoff suggests that all technologies carry inherent biases. The wheel favors flat surfaces. The clock fragments time into measurable units. Social media privileges immediacy over depth. These biases don’t just shape how we use tools—they shape how we think, work, and exist.
So what about generative AI, like ChatGPT, MidJourney, or other similar tools? These technologies aren’t neutral. They, too, have biases baked into their design, functionality, and even the ways we interact with them. For product managers, the consequences are both exciting and cautionary. Let’s explore how the inherent biases of generative AI might impact the discipline of product management.
Bias #1: Speed Over Depth
Generative AI tools are optimized for speed. They draft content, analyze data, and create mockups in minutes—tasks that used to take hours or days. This is helpful when deadlines loom or when rapid iteration is needed. But there’s a subtle bias here: speed can discourage depth.
When AI tools offer quick answers, it’s tempting to stop asking deeper questions. Why run five rounds of user interviews when you can prompt an AI to generate "user personas" based on millions of data points? Why dive deep into competitive analysis when ChatGPT can synthesize reports in seconds?
Impact on Product Management: Product managers risk losing the human touch. User empathy, cultivated through face-to-face interviews and direct engagement, might be sidelined in favor of AI-driven approximations. Generative AI can provide data, but it struggles with nuance. PMs must consciously slow down to validate AI outputs against real-world complexity.
Bias #2: Preference for the Existing Over the Novel
Generative AI operates by synthesizing existing knowledge. It’s exceptionally good at remixing what already exists but struggles with true originality. This bias toward the known might inadvertently steer PMs away from innovative ideas.
Impact on Product Management: Product roadmaps could become increasingly iterative rather than inspiring. Imagine a PM using AI to brainstorm features: the outputs will likely lean on established patterns, risks, and best practices. While these are valuable, they may overlook transformative opportunities—ideas that deviate from what the AI deems “safe.”
How to Counteract It: PMs must actively inject creativity into the process. Ironically, leveraging AI could be a first step here, too, by forcing it to hallucinate and then taking the somewhat wild output and post-processing it via the human review, ideally by brainstorming with diverse teams or user co-creation workshops.
Bias #3: Optimizing for the Average
AI is trained on large datasets, which often reflect the median of human behavior. While this makes AI a powerful tool for identifying patterns, it also means that AI tends to optimize for the average user rather than outliers or edge cases.
Impact on Product Management: Product decisions driven by generative AI might inadvertently exclude underserved markets or niche user needs. For instance, if an AI-driven analysis suggests prioritizing features that cater to 80% of users, it may overlook the 20% with unique, unmet needs. These edge cases often drive innovation (think early adopters or accessibility-focused design).
How to Counteract It: PMs should use AI insights as a starting point but remain vigilant about including diverse perspectives. Build checks into your process to ensure underserved or minority user groups are accounted for, and champion their needs in the roadmap.
Bias #4: Automation Over Intuition
Generative AI thrives on automation. It automates analysis, idea generation, and even elements of product design. But product management has also always been a practice based on intuition, gut checks, interpersonal skills, and qualitative judgment. The AI bias toward automation can sometimes drown out these subtler, human elements.
Impact on Product Management: PMs may feel pressured to “prove” every decision with data, even in areas where intuition and experience should lead. This could create a culture where experimentation and risk-taking are minimized because AI outputs dominate the conversation.
How to Counteract It: Reframe intuition as a valuable counterbalance to AI insights. PMs should trust their instincts when AI-generated outputs don’t align with their lived experience or observations. Encourage teams to treat AI as a collaborator, not a decision-maker.
Bias #5: The Illusion of Neutrality
Perhaps the most insidious bias of all is the belief that generative AI tools are neutral. Because they aggregate vast amounts of information, it’s easy to assume they reflect an objective reality. But AI is only as unbiased as the data it’s trained on—and we know human biases permeate datasets.
Impact on Product Management: PMs relying too heavily on AI might unknowingly perpetuate biases in their products. For example, an AI-powered feature prioritization tool might reinforce gendered assumptions in UX design or exclude accessibility considerations if the training data lacked diverse representation.
How to Counteract It: PMs must critically interrogate AI outputs. Ask: Where did this data come from? Whose voices might be missing? What assumptions are baked into the model? By fostering a mindset of curiosity and skepticism, PMs can mitigate the unintended consequences of AI biases.
Generative AI as a Partner, Not a Replacement
Generative AI is undeniably transformative. It accelerates workflows, enhances creativity, and provides PMs with powerful new tools. But like all technologies, it comes with biases that shape how we think, work, and make decisions.
The key for product managers is to embrace these tools while remaining conscious of their limitations. Use AI for speed, but don’t sacrifice depth. Let it spark ideas, but don’t let it constrain creativity. Trust its data, but temper it with human intuition.
By recognizing and navigating these biases, product managers can harness generative AI as a true partner—one that amplifies their abilities without compromising the craft and humanity of product management. After all, even in an AI-driven world, the best products will always come from people who deeply understand other people.
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