Webinar
Upcoming

Inside a Modern Awards Program

How one association increased participation rates while avoiding volunteer fatigue.

event
August 20, 2026
schedule
12:00 pm

Inside a Modern Awards Program

How one association increased participation rates while avoiding volunteer fatigue.

A start-to-finish walkthrough of one real award workflow — from category mapping to normalized results — and the design choices that brought more nominations in while sending reviewer workload down.

- All seven workflow stops, walked in the order they actually run

- The volunteer fatigue ratio and pairing math, explained plainly

- Where AI summarization helps — and why humans still decide

Why this session

Participation and volunteer capacity aren’t a trade-off. They’re a design problem.

Most award programs live on a seesaw: push for more nominations and the review committee drowns; protect the committee and participation flatlines. The association in this session refused the seesaw.

They redesigned the workflow itself — how nominations are captured, routed, assigned, evaluated, and normalized — so both numbers moved in the right direction at once. This webinar walks that workflow end to end, with the reasoning behind every decision.

The old way vs. the workflow you’ll see

Same award program, same volunteers, same mission — a completely different experience on both sides of the nomination.

The old way

  • One committee reads every nomination in every category
  • Nominators carry the whole burden — long forms and chased-down details
  • Reviewer workload is whatever the pile happens to be
  • Gut-feel rankings get debated in a long meeting
  • Reviewers open every nomination cold
  • A harsh scorer in one category quietly sinks good nominees

The modern workflow

  • Committee groups scoped to the categories they actually know
  • A short nomination starts it — the nominee completes their own profile
  • A fatigue ratio caps reading load; pairings spread it evenly
  • Quick quantitative rubric scoring, online, in minutes
  • AI summaries orient reviewers before the deep read
  • Normalization levels scoring tendencies across reviewers