The AI Canvas for Associations

Hi there! Welcome to the AI Canvas for Associations. Use this as a foundation on how AI could help your association.

Prediction	Judgement	Action	Outcome
What do you need to know to make the decision?	How do you value different outcomes and errors?	What are you trying to do?	What are your metrics for task success
• Member engagement levels and patterns

• Membership renewal probability

• Event attendance likelihood

• Member interests and professional needs

• Resource utilization patterns

• Volunteer participation potential | • False positives: Over-engaging members (moderate cost - annoyance)

• False negatives: Missing at-risk members (high cost - lost membership)•

• Balance between personalization and community-wide initiatives

• Cost of mistargeted resources vs. benefit of successful engagement

• Value of predictive member retention vs. acquisition efforts | • Predict member churn risk

• Personalize member communications

• Optimize event planning and scheduling

• Automate routine administrative tasks

• Match members with relevant opportunities

• Enhance member value delivery | • Member retention rate

• Event participation rates

• Member satisfaction scores

• Resource utilization metrics

• Volunteer engagement levels

• Revenue per member

• New member acquisition rate |

Input	Training	Feedback
What data do you need to run the predictive algorithm?	What data do you need to train the predictive algorithm?	How can you use the outcomes to improve the algorithm?
• Membership history and demographics

• Event attendance records

• Member interaction logs

• Payment and dues history

• Communication engagement data

• Professional development participation

• Committee/volunteer involvement | • Historical membership lifecycle data

• Past event success metrics

• Previous engagement campaigns results

• Member feedback and survey responses

• Historical churn patterns

• Resource usage patterns

• Communication response rates | • Track membership retention impact

• Monitor engagement metric changes

• Analyze program effectiveness

• Measure resource allocation efficiency

• Assess prediction accuracy

• Gather member satisfaction feedback

• Evaluate ROI of AI-driven initiatives |

Here’s an example:

Prediction	Judgement	Action	Outcome
What do you need to know to make the decision?	How do you value different outcomes and errors?	What are you trying to do?	What are your metrics for task success
I need to know who opens your emails, when they read them, what topics they click on, and what content they like best.	Getting it wrong means either annoying people with too many emails at the wrong time (bad) or missing chances to reach them when they're interested (not great, but not as bad). It's like finding the right balance of - not too much, not too little.	Send the right emails to the right people at the right time.	Success means more people opening your emails, clicking on links, and staying subscribed.

Input	Training	Feedback
What data do you need to run the predictive algorithm?	What data do you need to train the predictive algorithm?	How can you use the outcomes to improve the algorithm?
Basic information about your email subscribers: when they usually open emails, what they click on, and what they're interested in.	Past information about what worked and didn't work in your emails - which ones got opened, which links got clicked, and when people unsubscribed.	Watch what works and what doesn't, then make changes to do better next time.