AI makes customer proximity more important not less
AI has dramatically changed how product managers do their work.
Discovery artifacts that once took days to synthesize can now be summarized in minutes. Patterns surface faster. Options are easier to generate. The mechanics of product discovery have become far more efficient. But that efficiency introduces a new risk: Product teams can now move from raw customer input to polished conclusions without ever engaging customers directly. That shift is subtle. And dangerous.
Synthesis is faster. Sense-making is not.
One of AI’s biggest strengths in product management is synthesis. AI is excellent at organizing information, summarizing interviews, and clustering qualitative input at scale. For teams overwhelmed by data, this is a genuine breakthrough. But customer understanding does not come from summaries alone.
Decades of UX research, including work published by Nielsen Norman Group, have consistently shown that understanding users requires observing behavior, hearing nuance, and engaging with context. Many of the most important insights never appear in transcripts or surveys at all.
When teams skip direct exposure to customers, they trade understanding for efficiency.
The illusion of completeness
AI-generated outputs often feel “done.”
- A summary sounds authoritative.
- A persona reads as coherent.
- A synthesized insight deck feels complete.
This creates a dangerous illusion: that learning is finished when it has only been processed. Research on decision-making and judgment, frequently discussed in Harvard Business Review, shows that fluent and confident outputs increase trust even when underlying understanding is shallow. When teams lack firsthand exposure to source material, they are far more likely to accept conclusions without sufficient scrutiny.
Why customer proximity matters more in an AI-enabled workflow
Customer proximity has always been central to strong product management. AI doesn’t change that. It raises the stakes. Direct customer contact allows product teams to:
- Hear hesitation, not just answers
- Notice contradictions and workarounds
- Ask follow-up questions in real time
- Understand emotional and organizational context
Behavioral research has long demonstrated that people routinely simplify, rationalize, or misreport their motivations. Observation and dialogue are required to surface what actually drives behavior. AI can process information at scale. Only humans can contextualize it (as of now.)
AI shifts the bottleneck-not the responsibility
As AI reduces the cost of analysis and production, the bottleneck in product work moves upstream. The hardest work becomes:
- Framing the right problems
- Interpreting ambiguous signals and following up with the right questions
- Making tradeoffs under uncertainty
- Deciding what not to build
Leading tech thinkers like Andrew Ng, along with researchers writing in Harvard Business Review, have made this explicit: as building and prototyping become cheaper, decision quality becomes the real constraint.
AI doesn’t remove responsibility from product managers or product leaders. It concentrates it.
How to use AI in the product operating model without losing customer proximity
Here are three ways of working with AI to ensure your team is building judgment, empathy, and decision confidence over time.
- Use AI to compound customer understanding over time, not just to summarize one-off research.
Great product judgment doesn’t come from a single study or a perfect synthesis. It comes from prolonged exposure to customers over time. Strong teams design discovery so:
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- Customer conversations happen continuously, not episodically
- AI synthesizes patterns across many small learning moments
- Insights are allowed to evolve as understanding deepens
If AI is helping your team recognize patterns across time, it’s building judgment. If it’s summarizing a single moment, it’s creating false confidence.
- Make customer exposure a shared, visceral experience, not a report.
Reading a summary is not the same as feeling customer pain. Teams that operate with urgency and clarity tend to share one trait: they’ve personally seen or heard the customer struggle. Strong teams ensure:
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- Cross functional stakeholders regularly hear and or observe real customer interactions.
- AI-generated synthesis is paired with raw moments
- Empathy is built collectively, not delegated to a single role
Alignment accelerates when the entire team is emotionally invested in the work they are doing.
- Match AI confidence to evidence and make assumptions explicit early.
AI is often tempting early in discovery, when data is thin and pressure to decide is high. Strong teams:
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- Acknowledge where AI is operating on limited inputs
- Surface the biggest leaps of faith
- Build validation plans before major investments are locked in
The earlier AI is introduced, the more explicit the assumptions and the stronger the commitment to validate them.
The takeaway
AI does not make customer proximity obsolete. It makes losing it easier.
Want help putting this into practice?
At Product Rebels, we work with product leaders and teams navigating exactly this shift: using AI to accelerate delivery without losing product discipline or customer trust.
Is your team experiencing challenges in implementing AI into your product operating model or struggling in establishing the practices that enable the best outcomes from AI product building?
Schedule 30 minutes with us to learn a little bit about you and explore how we can help.
About Product Rebels
Product Rebels helps product leaders bring their teams from good to great. We work with established product organizations that already know the basics of product management but want to operate at a higher level. Our focus is not Product Management 101. It’s helping teams build strong customer foundations and outcome-oriented ways of working that consistently translate into better results; for customers and for the business.
We partner with leaders and teams to change how product work actually happens day to day: how customer insight is gathered and shared, how problems are framed, how tradeoffs are made, and how learning compounds over time. AI is infused throughout these practices as an accelerator, helping teams synthesize learning faster, explore more options, and move with greater confidence without sacrificing judgment or customer connection.
The result is product teams that don’t just ship more, they build the right things, make better decisions under pressure, and deliver meaningful, sustained impact.
Sources & further reading
- Nielsen Norman Group – Which UX Research Methods?
https://www.nngroup.com/articles/which-ux-research-methods/ - Nielsen Norman Group – Contextual Inquiry: Inspire Design by Observing Users in Context
https://www.nngroup.com/articles/contextual-inquiry/ - Harvard Business Review – How to Use AI to Augment Human Decision-Making
https://hbr.org/2023/07/how-to-use-ai-to-augment-human-decision-making - Harvard Business Review – The False Promise of AI in Decision Making
https://hbr.org/2024/02/the-false-promise-of-ai-in-decision-making - MIT Sloan Management Review – The Productivity J-Curve: How Intangibles Complement Gen AI
https://sloanreview.mit.edu/article/the-productivity-j-curve-how-intangibles-complement-gen-ai/ - Teresa Torres – Continuous Discovery Habits
https://www.continuousdiscoveryhabits.com/ - Daniel Kahneman – Thinking, Fast and Slow
- Andrew Ng – AI Is the New Electricity
https://www.deeplearning.ai/the-batch/ai-is-the-new-electricity/