Why You Should Keep Reading
A spring 2025 Pew Research Center survey found that 43 % of U.S. adults believe AI will do them more harm than good, while another 33 % simply aren’t sure—hardly the mindset that fuels confident investment. Pew Research Center If that sounds familiar, this primer is for you.
1 | Decode the Jargon in 90 Seconds
| Term | Plain‑English Translation | Quick Win |
|---|---|---|
| Machine Learning | Software that learns patterns from data. | Use it to auto‑categorize invoices. |
| Large‑Language Model (LLM) | A text‑prediction engine (e.g., ChatGPT). | Draft customer emails in seconds. |
| Model Drift | Accuracy erodes as data changes. | Schedule quarterly accuracy checks. |
2 | Top 4 Myths—Busted
“AI is only for tech giants.” Cloud APIs start at $0.01 per call.
“We need a data‑science team.” Off‑the‑shelf tools embed ML behind drag‑and‑drop.
“AI replaces jobs.” It replaces tasks; staff move to higher‑margin work.
“Ethics can wait.” A one‑page policy now prevents PR and legal pain later.
3 | Three Starter Activities (Low or No Budget)
Publish an internal “AI curiosity” newsletter—one use‑case a month.
Map pain points: list any task eating ≥ 5 staff hours per week.
Host a “tool‑tasting” demo day—free trials of chatbots, forecasting plug‑ins, analytics add‑ons.
Executive Cheat‑Sheet
Learning beats waiting; small experiments de‑risk big bets.
Data quality is your first capital expenditure.
Ethics and governance scale trust.