Goal: Learn from the most common AI adoption mistakes so you can prevent them before they derail your work.

Even with a strong discovery process, challenge statements, and prioritization, organizations can stumble in execution. The most common errors are predictable and therefore preventable.

❌ Pitfall 1: Jumping Straight to Tools

Mistake: Teams get excited by AI platforms (Gemini, Copilot, ChatGPT, Power Automate) and try to plug them in right away.

Why it hurts:

• Creates “solution-first demos” that don’t address real pains
• Tools may not align with data or SDG goals
• Wastes time and risks losing credibility

How to avoid it:

• Follow the Deep Path discipline: Persona → Journey → Pain Points → Challenge → Feasibility → Prioritization → Pilot
• Do not select tools until after Section 6

❌ Pitfall 2: Over-Focusing on Internal Ops

Mistake: Teams concentrate only on efficiency (HR, finance, reporting) instead of beneficiary-facing challenges.

Why it hurts:

• Easy wins for staff, but limited impact alignment with SDG 3
• Donors and partners want to see health and well-being outcomes

How to avoid it:

• Balance: choose at least one use case with direct beneficiary impact