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MarginWise
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What to think about before running an AI audit.
Pick the themes that matters most and discuss with your leadership team
Strategy & Direction
where are we going?- Where do we start?
- What does "transforming the business with AI" actually mean here? What's the menu of opportunity?
- Cohesive AI strategy, or scattered experiments with no rigor?
- How do we tell AI activity from AI productivity?
- Central team, or embedded in every function?
Market & Competition
what's the landscape?- Which competitive advantages get eroded or amplified by AI?
- If an AI-native competitor launched tomorrow, what would they do differently? Why aren't we?
- Who has transformed with AI successfully? Who has failed? What can we learn from both?
- What are the risks of doing this? What are the risks of not doing it?
Build & Reimagine
how do we make the work?- Reimagine from first principles, or just bolt AI on?
- If a task drops from $100 to $1, what's worth doing that wasn't before?
- A data strategy that gets us AI-ready without holding us back today?
People & Leadership
who does the work?- How does leadership get hands-on with AI to lead by example?
- How do we find our AI A-players and make their work the gold standard?
- Our responsibility for upskilling vs. our employees'?
- How do we test for AI curiosity and literacy in hiring?
Culture & Messaging
earning buy-in?- How do we message our AI strategy honestly and empathetically?
- How do we shift culture when employees are apathetic or fearful of being replaced?
- How do we surface use cases from employees and productionize from the top?
- How true is the "redeploy people to higher-value tasks" narrative?
Risk & Resilience
what could break?- Risk framework for go / no-go decisions on AI tooling?
- Encourage experimentation while mitigating security risk?
- Make IT, legal, and compliance partners and heroes, not gatekeepers?
- Build model-agnostic systems for a volatile market?
- If we depend on external vendors, what happens if LLM costs skyrocket?