Podcast | The Hidden Complexity Behind Scaling Knowledge in Science-Driven Organisations – with Mavic AI
Most science-driven organisations are not struggling because they lack knowledge. They are struggling because knowledge does not scale the way their organisations do.
Catch the full episode on Spotify, Amazon Music, Apple Podcast, or watch on YouTube. In this episode of The Digital Chemical Playbook, Florian Brunet speaks with Jess Tang, Founder and CEO of Mavic AI, to unpack the reality behind one of the most overlooked challenges in complex industries: how knowledge breaks when organisations grow.
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🎧 Listen to the full episode on Spotify, Apple Podcast, or watch on YouTube.
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When Growth Creates Complexity
In industries like chemicals, pharmaceuticals, and specialty materials, decisions are rarely simple. Teams across regions operate differently. Technical experts hold years of experience. Commercial teams translate science into business value. Every market has its own context.
As organisations scale, so does the complexity of keeping everyone aligned. Jess shares how much time companies spend re-explaining things that were already solved before:
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Why a decision was made
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What worked in another market
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How a customer challenge was approached
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What “good” actually looks like internally
The knowledge exists. But the context behind it often does not travel well.
The Hidden Cost of Rebuilding What Already Exists
One of the least visible inefficiencies in science-driven organisations is the constant repetition of knowledge transfer.
Instead of progressing, teams spend time:
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Re-explaining market context
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Re-aligning global and local perspectives
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Reconstructing past decisions
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Revalidating assumptions that already exist somewhere in the organisation
This creates a silent operational drag that slows execution, increases complexity, and reduces consistency across teams.
Why Traditional Systems Fail to Solve This Problem
Even in digitally advanced organisations, knowledge systems are still primarily designed to store information rather than preserve intelligence.
They can store:
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Documents
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Data
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Presentations
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Reports
But they rarely retain:
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Decision logic
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Trade-off reasoning
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Market context behind choices
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Cross-functional interpretation of information
As a result, knowledge exists, but it is not easily reusable without human reconstruction.
AI as a Layer for Scaling Context, Not Just Content
Jess explains how Mavic AI was built to address this structural gap. The goal is not to replace expertise, but to preserve context so knowledge can scale with the organisation.
With AI acting as a contextual layer, organisations can:
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Retain decision history in usable form
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Reduce repeated briefing cycles
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Improve alignment across teams and regions
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Accelerate onboarding and execution
This shifts AI from being a productivity tool to becoming part of the organisation’s knowledge infrastructure.
Why This Matters Now
As businesses move faster and teams become increasingly global, the ability to retain and transfer knowledge becomes a competitive advantage. The organisations that move quickest will not necessarily be the ones with the most data.
They will be the ones that can connect expertise, context, and decision-making across the business without constantly starting over.
Key Takeaway
The conversation behind The Hidden Complexity Behind Scaling Knowledge in Science-Driven Organisations – with Mavic AI is ultimately not about technology alone. It is about how organisations keep valuable knowledge alive as they grow.
Because scaling a business is hard. Scaling understanding across that business is even harder.
🎧 Listen to the full episode on Spotify, Apple Podcast, or watch on YouTube.
