Tacit Gold: How AI-First Companies Unlock What People Can’t Quite Say

Written By Andrew Lopianowski

“We can know more than we can tell.” 
— Michael Polanyi 

You can feel it in the room: the unspoken, the half-glimpsed. The gut sense that something’s off. Or a quiet knowing that someone has the answer, but can’t quite articulate it. 

That’s not a gap in intelligence. It’s tacit knowledge — the hardest kind to capture, the most valuable to harness. 

In the AI era, we’re awash in dashboards, predictions, and outputs. But the organizations pulling ahead are doing something different. They’re mining the invisible. They’re making sense of the things we know but don’t know how to say. They’re building systems that surface the wisdom between the words. 

The Tacit Problem, and the Strategic Opportunity 

Tacit knowledge is intuitive, experience-based, deeply embodied. It’s why a veteran manager sees a red flag before it hits the spreadsheet. Why a field agent trusts a hunch that saves lives. It lives in: 

  • Unspoken norms 

  • Gesture and tone 

  • Relational dynamics 

  • Cultural intuition 

The problem? Most organizations don’t know how to capture it, scale it, or even talk about it. 
The opportunity? Generative AI, when paired with human insight, can make tacit knowledge visible and actionable. 

Think of tacit knowledge as “organizational dark matter”: It shapes everything, even though we rarely see it directly. The challenge is to bring it to light without flattening its subtlety or undermining the trust that allows it to emerge. 

Neuroscience: Where Knowledge Lives in the Body 

Neuroscience confirms what philosophers have long suspected: wisdom isn’t just stored in words. 
The somatosensory cortex, cerebellum, and mirror neuron systems all encode experience-based learning that’s not verbal (Damasio, 2010). Trauma research shows how the body “remembers” what language suppresses (van der Kolk, 2014). Emotional cues and relational trust fire in the limbic system long before conscious articulation (Barrett, 2020). So when you ask an employee, “What’s really going on?” and they pause, what follows may be a verbal shortcut to a deeper felt truth. AI can help hold space for that, but only if designed with that depth in mind. 

Recent neuroscience also highlights how storytelling and social connection activate brain networks related to empathy and meaning-making, suggesting that organizational learning is as much about emotion and narrative as it is about data (Barrett, 2020; Damasio, 2010). 

What AI Can, and Can’t, Do 

AI is brilliant at pattern recognition. But the real opportunity is not in replacing human judgment; it’s in amplifying human sensemaking. 

How leading organizations are doing it: 

  • Using large language models (LLMs) to generate first-draft reflections from unstructured meeting transcripts 

  • Deploying natural language processing (NLP) to identify emerging team values and cultural patterns 

  • Analyzing recurring metaphors across conversation threads to map organizational emotion 

  • Using AI to prompt storytelling, not just data entry (e.g., “Tell me a time you overcame a challenge with a customer…”) 

Generative AI becomes a “conversation mirror,” reflecting back what people may not yet know they’re trying to say. But without a culture of trust and curiosity? AI becomes surveillance. Fear shuts down expression. The gold stays buried. 

Anthropology Meets Engineering 

Anthropologists have long studied how communities encode wisdom not just in speech, but in ritual, rhythm, and repetition. That’s what made oral traditions last for centuries. That’s also why effective teams don’t just “share knowledge,” they live it. 

To build truly AI-first systems, we must embed anthropological sensitivity into design: 

  • What are the unspoken taboos? 

  • Who holds the unofficial knowledge networks? 

  • What rhythms govern energy and creativity? 

In short, culture is not noise in the system. It is the system. AI that ignores this fails. 

Some Points to Ponder 

  1. Map Your Tacit Terrain: Identify the touchpoints where insight is felt but not documented: mentoring, customer service, informal peer learning. 

  1. Create Reflective Capture Moments: Use GenAI tools not just to summarize but to prompt reflection. Ask: “What do you wish more people understood about this?” 

  1. Normalize Storytelling, Not Just Reporting: Build templates and platforms where employees can share lived experiences. Let AI help surface common patterns across those stories. 

  1. Train Leaders to Read Below the Surface: Invest in leadership development that blends AI literacy with emotional intelligence and pattern recognition. 

  1. Mine the Margins: Tacit knowledge often lives at the edge: junior voices, frontline roles, informal chats. Don’t let hierarchy silence your best insights. 

Bringing It Home 

Organizations already know how to track explicit metrics: sales, churn, productivity. But in a volatile world, competitive advantage lies in surfacing the invisible currents that shape action. AI can help us map that landscape. But only when paired with human trust, narrative intelligence, and brave spaces for reflection. 

Tacit knowledge is not hidden by accident. It’s hidden by design, because most workplaces are afraid of what it might say. The future belongs to those willing to listen for what isn’t being said, and to build systems that help everyone speak their unspoken expertise. 

 

Works Referenced.  

What’s the most valuable insight your team holds, but hasn’t yet said out loud? What would it take to hear it? How are you helping your people unlock their unspoken expertise? 

 

 

Want to be part of the (r)evolution?  

I am putting the finishing touches on the first draft of a book with a friend and colleague Andrew Lopianowski on the concept, which we are calling HumanCorps. If you’d like to learn more about the book, or perhaps have some amazing stories of people who are putting these efforts in motion to be the change we need, please drop me a line.    

Andrew Lopianowski https://www.humancorps.co.uk
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