1. Start with the business decision, not the model

I redirect conversations from:

• “Which model should we use?”
  to
• “What decision or workflow are we trying to improve?”

If the decision, owner, and success metric aren’t clear, AI is premature.

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2. Frame AI as augmentation, not automation

I set expectations early:

• AI assists humans today more reliably than it replaces them
• Humans remain in the loop for quality, risk, and accountability

This immediately grounds the conversation in reality.

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3. Be explicit about constraints and trade-offs

I clearly explain:

• Hallucination risk
• Data quality dependencies
• Governance and security requirements
• Cost and latency trade-offs

Credibility increases when you talk about what AI cannot do well.

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4. Push for narrow, high-ROI use cases

I guide customers toward:

• Domain-specific, bounded problems
• Measurable outcomes within weeks, not months
• Reusable patterns (search, summarization, classification)

This prevents “AI everywhere” failure.

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5. Use evidence, not promises

I rely on:

• Real customer examples
• Benchmarks and pilots
• Time-boxed proofs of value

No long-term commitments without validated results.

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6. Set a maturity-based roadmap

I position AI as:

• Phase 1: Data readiness and governance
• Phase 2: Copilots and assistive AI
• Phase 3: Selective automation

This keeps expectations aligned with organizational readiness.

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In summary, “I avoid AI hype by anchoring every conversation to a real business decision, being honest about constraints, and pushing for narrow, measurable use cases before scaling.”