AI Scaling Blockers Diagnostic
The AI Scaling Blockers Diagnostic helps teams identify and address the strategic, technical, organizational, and cultural barriers hindering the successful implementation of AI initiatives across an enterprise. By surfacing these hidden obstacles, teams can shift from questioning *why* AI isn't scaling to focusing on *what* needs to be fixed.
Use this method when AI initiatives are stalled or failing to deliver expected results, and the root causes are unclear. It's particularly useful for cross-functional teams responsible for AI implementation.
Solves: Lack of clear understanding of why AI initiatives are not scaling, leading to frustration and wasted resources.
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Step 1: Introduce the four categories of blockers: Strategy & Organization, Technology & Data, Governance & Operating Model, and People & Culture. (5 minutes)
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Step 2: Individually, have participants brainstorm specific blockers within each category related to their experience. (15 minutes)
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Step 3: As a group, share and map the identified blockers onto a shared canvas, clustering similar themes. (30 minutes)
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Step 4: Prioritize the most critical blockers to address in the next 6-12 months using dot voting or another prioritization technique. (20 minutes)
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Step 5: For the top prioritized blockers, define targeted actions or enablers to address them. (20 minutes)
- Encourage honest and open discussion, even if it involves challenging existing assumptions or practices.
- Ensure all participants have a voice and that dominant personalities don't overshadow quieter ones.
- Focus on identifying actionable solutions rather than dwelling on the problems.
- Use a different framework for categorizing blockers, such as the McKinsey 7-S framework.
- Conduct the diagnostic in multiple sessions, focusing on one category of blockers per session.
- Incorporate external data or benchmarks to provide additional context and insights.