Where AI Integration Breaks Down
Our analysis of 32 life sciences workflow models found that 34.8% of AI-designated stages have no output owner — and 64.8% of rework triggers trace back to governance gaps, rather than technology failures.
AI initiatives stall because organizations lack the infrastructure to absorb them. The technology works. The receiving structure does not.
The Data: 32 workflow models. 313 workflow stages. Seven domains: clinical, CMC, regulatory, commercial, medical affairs, R&D operations, quality.
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of stages have no AI output owner
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of handoffs are rework loops
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of stages have no formal override authority
The Engagement: We do a rapid assessment of your organization, bridging functions. To accelerate the generation of actionable insight, we leverage Modus, a proprietary diagnostic workbench built by Brinton Bio. We use it in 30/60/90 day engagements to identify where integration will break before it becomes a cost.
If you're planning an AI integration in the next 12 months, we'll map where it will break before it does.
What You Get:
Absorption capacity diagnostic. Friction mapping.
Intervention recommendations.
Leave-behind governance templates.
What Modus™ Does: Maps the intersection of organizational structure and workflow execution. Identifies accountability gaps, rework loops, and governance misalignment before they become costs.