
On September 10, Rethink Ireland will bring its AI For Good Discovery Day to GreenTech HQ in Enniscorthy….
The free workshop will give nonprofits, social enterprises, and community organisations hands-on experience with AI tools, practical case studies, and guidance on responsible adoption. That makes the next question urgent for Wexford organisations: what happens after a useful AI experiment becomes part of everyday work?
The answer should start with a handoff map. Before an organisation automates a recurring task, it should identify the point at which the AI must stop, the person who takes over, the evidence that person needs to check, and the action they can reverse if something goes wrong.
This matters because most workplace failures do not begin with a spectacular technical breakdown. They begin with ordinary exceptions. A customer asks something the system has not seen before. A grant application contains an unusual eligibility issue. A supplier invoice looks almost right but includes a discrepancy. A volunteer record raises a privacy concern. A generated summary omits a detail that changes the decision.
When staff know the work only through the automated system, those exceptions become harder to spot. The organisation may gain speed on routine cases while quietly losing its capacity to judge the non-routine ones.
Wexford already has a useful foundation for avoiding that tradeoff. Earlier this year, Local Enterprise Office Wexford’s digital-adoption programme highlighted automation and applied AI alongside process improvement, one-to-one digital reviews, and the Grow Digital Voucher. National AI micro-qualifications now also ask employees to complete workplace-based projects while learning to use AI strategically and ethically. Those supports create an opportunity to treat human recovery skills as part of the implementation rather than as an afterthought.
A handoff map can stay simple. For every AI-supported workflow, managers should be able to answer four questions. What can the system do without approval? What signals require human review? Who owns that review? What information lets that person reconstruct what happened?
Consider a small Wexford business using AI to draft customer replies. Routine questions may need little oversight. A complaint involving a refund, legal issue, safety concern, or unusual promise should trigger a named human reviewer. The reviewer should see the customer’s original message, the relevant policy, and the AI draft rather than receiving only a polished final answer.
The same principle applies to community organisations. AI can help summarise meeting notes, prepare fundraising copy, organise volunteer schedules, or draft reports. But an organisation working with vulnerable people or sensitive information needs a clear boundary around what the system may infer, what staff must verify, and what decisions remain human.
This approach also changes training. Instead of teaching people only how to prompt a tool, organisations should train them on exceptions. Give staff examples of plausible but flawed AI output. Ask them to diagnose what went wrong, correct it, and explain when they would escalate. Periodically let them perform critical parts of the workflow without automation so the underlying skill does not disappear.
Rethink Ireland’s AI For Good Fund represents a €1.75 million investment in helping the nonprofit, social enterprise, community, and voluntary sectors build responsible AI capability. The most valuable outcome will come from organisations that leave the workshop with more than a list of tools. They should leave knowing where AI authority ends and human responsibility begins.
Wexford does not need to choose between adopting AI and preserving human competence. It can make the two reinforce each other by building the handoff into the workflow from the start.

Gleb Tsipursky
Gleb Tsipursky, PhD, a behavioral scientist, CEO of Disaster Avoidance Experts, and author of The Psychology of AI Adoption at Work: From Resistance to Results
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