Illustrative use case · Product adoption

How should an enterprise AI assistant introduce automation without weakening trust or administrative control?

Rehearse an AI-product rollout across employees, IT administrators, security teams, and executive sponsors before changing the default experience.

View the rehearsal
Problem

The decision cannot be understood from a survey alone.

A major productivity platform can improve the assistant, onboarding, pricing, or default permissions—but every choice changes adoption and trust differently across organizations. Interviews reveal preference; they do not show how users behave after policy friction, peer influence, and repeated use.

WorldSim approach

Turn the decision into a controlled possible world.

WorldSim builds linked organizational populations, gives each actor a role, risk tolerance, workflow, and memory, then runs the same rollout through alternative product and policy designs. Teams compare adoption, support burden, permission failures, and durable use from an identical starting world.

Example rehearsal

Same starting world.
Three possible paths.

Every path uses the same actors, evidence, constraints, and random seeds so the intervention—not a different starting population—explains the change.

54%

Automation first

Fast discovery, elevated policy friction

78%

Trust first

Slower activation, strongest durable use

71%

Team rollout

Peer learning improves conversion

What the team learns

Observe the behavior behind the outcome.

Admin approval+18 pts

when permissions are explicit before first use

Week-8 retention1.4×

for guided team onboarding versus individual activation

Support pressure−23%

when controls are introduced before automation

Modeled business impact

What your team
could gain.

WorldSim does not promise a result. It identifies where a better decision could save time, prevent avoidable spend, and improve forecast precision—then defines the smallest real-world test needed to verify it.

Time to rollout6–10 weeks

potentially avoided by rejecting weak onboarding and permission designs before engineering and launch.

Support and rework18–28%

modeled reduction when trust controls are tested before automation reaches enterprise tenants.

Adoption forecast+14–20 pts

potential precision gain after calibration against pilot behavior and administrator decisions.

Illustrative ranges generated for this example scenario. Actual impact depends on evidence quality, calibration, deployment scope, and validation against observed outcomes.

RECOMMENDATION

Lead with visible controls and team-level onboarding, then expand automation after administrators and users share the same mental model.

SMALLEST REAL-WORLD TEST

Pilot the trust-first flow with three enterprise cohorts and compare 30-day task completion against the current experience.

Illustrative WorldSim scenario for Microsoft-type operating conditions. It does not describe a client engagement, partnership, endorsement, or verified company result.

Your decision

Rehearse your decision before it becomes real.

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