12/01/2026
You don’t need to be a data scientist to win with AI—you need AI literacy:
knowing what AI can/can’t do, how to use it responsibly, and how to turn use-cases into outcomes.
Why this matters
🌍 Digital public services, mobile money, and e-commerce are scaling fast—teams that speak “AI” make better, faster decisions.
🛡️ Regulators and boards now expect explainability, privacy, and control—not hype.
Core skills to build
🧭 Problem framing: turn a business pain into an AI-ready task (inputs, constraints, success metrics).
✍️ Prompting & tooling: structure prompts, chain tasks, and pick the right tool for the job.
🔍 Verification: fact-check outputs, cite sources, and keep a “human in the loop.”
🔒 Data stewardship: PII hygiene, consent, and minimal data to get the job done.
📜 Governance basics: model risk, bias awareness, and audit trails.
Quick ways to start this month
📝 Create a team prompt library (templates for reports, summaries, emails).
🧪 Run a 1-hour use-case sprint: pick one workflow, measure “before vs after.”
🧰 Standardize tools: one approved chat assistant + clear do/don’t data rules.
📚 15-minute weekly AI huddle: share wins, misses, and better prompts.
Guardrails (keep it safe)
🚫 No sensitive client or citizen data in public tools.
🔐 Use device-bound MFA and role-based access on any AI platform.
🧾 Keep rationale logs for material decisions produced with AI.
Measure progress
⏱️ Time saved per task
📈 Adoption rate by team
🧠 Number of reusable prompts/use-cases created
Bottom line: AI literacy is the new spreadsheet skill—table stakes for every role.
💬 What’s one task you’ll “AI-assist” this week—report drafting, data cleanup, or meeting notes?
