Before any tool, any subscription or any pilot: know where you stand. The AI Readiness Audit gives leadership a plain-language answer.
Ranked by effort and payoff, specific to your systems, your team and your season. Not a generic list of use cases.
Donor data, client confidentiality and the obligations that apply in your jurisdiction, turned into guardrails people can follow.
A sequenced, realistic plan your team can own, with the first win close enough to keep everyone with you.
"Our staff are already using AI tools quietly. How do we make that safe without banning it?"
"We have donor data in three systems and a nervous board. Where would you start?"
A board-approvable policy covering donor data, staff use, disclosure and the limits, written for your jurisdiction.
The specific question every fundraising team is asking: what can we safely do with donor records, and what can we not.
Monthly leadership advisory: a standing hour for the questions that come up as you adopt, without a retainer you cannot justify.
The sizes differ, the countries differ, the pressure differs. The gap is the same: everyone is being asked about AI, and nobody has a map, a policy or a safe first move. Four scenarios. Find yours.
A board member read about AI all summer and asked for a plan by the next meeting. The executive director has no technical staff, a full plate, and no idea which of the dozen AI pitches in the inbox is real.
The audit maps how the organization actually runs, ranks the two or three places AI pays off, and writes the plain-language policy and pilot plan the board asked for.
The ED walks into the meeting with a map, a policy and a pilot already running, and the AI conversation moves from anxiety to a plan.
Half the staff quietly use chatbots for drafts and summaries. Nobody has said what is allowed, and donor information is one careless paste away from leaving the building.
An audit of what is actually in use, a data line staff can follow, and a policy that keeps the time savings while closing the risk.
Staff keep the tools, now with rules. The trustees know what is in use, and donor trust stops depending on luck.
A family foundation runs on three people who spend most of the week on admin: acknowledgments, summaries, meeting notes, reports. Judgment is not the bottleneck; hours are.
The audit ranks the admin workflows by payoff, sets up one safe pilot on material with no personal details, and writes the rules the family is comfortable with.
Hours come back every week, the family’s judgment stays exactly where it was, and the foundation scales without a fourth hire.
Funders and partners now ask how the organization uses AI with the data it holds. The honest answer so far is a shrug, and the next proposal deadline is close.
The audit produces the policy and the data map that answer the question in writing, plus a pilot that shows responsible use in practice.
The question becomes a strength: the proposal carries a policy, a map and a running example, which most competitors cannot show.
Where AI pays off is read from your workflows, not from a vendor’s pitch.
A plain-language policy, adopted, before anything scales.
Small, owned, measured, with a stop rule. Running when we leave.
Different triggers, same question: can we use AI with the data donors trusted us with? Four scenarios. Find yours.
The organization lives on Microsoft 365 and wants Copilot for email and documents. But mailboxes and shared drives hold years of donor records, and nobody can say what Copilot would see.
The Copilot and email check: what AI can reach today, where folders are shared wider than intended, and the settings that close the gaps, plus the policy that governs it.
Copilot goes on with the gaps closed first. The time savings arrive without the sleepless nights.
Someone pasted a donor list into a public chatbot to clean it up. Nothing bad happened, this time. Leadership wants rules before the next time, without banning the tools everyone now relies on.
The donor data map sorted into fine to use, use with controls and keep out, and a policy that names the line instead of banning the tool.
Staff know exactly which data goes where. The tools stay, the fear goes, and the near-miss becomes the reason the rules exist.
A federated charity operates across provinces, including Quebec, where Law 25 expects privacy assessments, a named privacy officer and openness about automated decisions. Nobody is sure what that means for AI.
The privacy and legal map: which rules likely apply, what they mean for AI and donor data, and a prepared list of questions for the lawyer.
The board adopts one policy that works across provinces, and the lawyer conversation starts prepared instead of from zero.
A foundation’s grantees keep asking whether they may use AI on funded projects and with beneficiary data. The funder has no answer to give, and every grantee is improvising a different one.
A policy for the foundation itself, plus a shareable standard its grantees can adopt, with the data lines and accountability written in plain words.
The funder leads instead of shrugging. Grantees follow one standard, and the portfolio’s data practice rises together.
Which data can go where is decided first, in writing.
Adopted by the board, readable by every member of staff.
No identifiable donor records leave your systems. Ever.
One-pagers and use-case companions, two pages each.
Thirty minutes. We will tell you whether you are ready, nearly ready, or better off fixing something else first.
Choose what you are comfortable with. Nothing in the optional categories runs until you switch it on, and you can come back and change this whenever you like.
Needed for the site to work at all: security, load balancing and remembering this cookie choice. These cannot be switched off.
Helps us see which pages are useful so we can improve them. Aggregated, never used to identify you.
Used to measure campaigns and show relevant content elsewhere. Off unless you turn it on.