AI changes throughput—but not always in the direction you expect. Motionode's AI Usage Calculator calibrates how different AI ranges affect your team's speed and rework, then simulates the delivery impact. Set role-based AI targets, see bottlenecks move (Dev → QA → PM), and choose the fastest path to hit the deadline.
MEASURE BEFORE YOU MANDATE
Most teams adopt AI blindly: some devs at 90%, others at 10%, with no idea of schedule impact. Motionode lets you test AI policies in a simulator before enforcing them.
"If we set Dev to 61–80% AI, do we ship by Feb 23?"
→ System Result: YES (Projected: 3 weeks earlier)
"If we keep high AI usage, do we need more QA to avoid rework bottlenecks?"
→ System Result: QA becomes bottleneck (Recommendation: +1 QA or lower Dev AI range)
"What if we reduce task returns by improving review gates—how much time do we save?"
→ System Result: Fewer returns = higher throughput (Projected: X days saved)
AI can make individuals faster while slowing the system. More output increases QA load. Quality drops multiply returns. Returns explode context switching. Most agencies discover this months later—after the model landscape has changed.
Delivery estimates swing from "2 weeks" to "6 months" depending on how AI is used.
Dev gets faster, QA gets crushed, and the schedule silently slips.
You need multiple projects and months of data to find the sweet spot—too late.
A FLIGHT SIMULATOR FOR AI ADOPTION
Instead of arguing about "more AI," Motionode measures how AI affects your team's throughput, then runs hundreds of simulations to show delivery impact. Get a clear AI policy by role—and the staffing trade-offs to hit the deadline.
Motionode doesn't guess. It calibrates on your real execution data, then predicts what happens when you change AI usage targets and team composition.
Your team tags AI usage per task. Motionode groups into 5 AI buckets (0–20%, 21–40%, 41–60%, 61–80%, 81–100%) and learns how each affects speed and rework for each role.
Motionode computes effective speed per role and AI bucket, including returns, rework, and context switching. The sweet spot: fast enough without downstream overload.
Pick AI targets by role (ex: Dev 61–80%, QA 21–40%). Optionally adjust team mix. Motionode shows projected delivery date, bottlenecks, and fastest configuration.
Motionode outputs an "AI Usage Policy" card: recommended ranges by role, delivery impact, and bottleneck warnings—so leadership can align without debate.
| Feature | Standard Tools (Asana, Jira, ClickUp) |
Motionode |
|---|---|---|
| AI Guidance | "Use AI if you want." (No measurement.) |
Role-based AI targets backed by calibration. |
| Delivery Impact | No way to see how AI changes the schedule. | Projected delivery date updates when AI policy changes. |
| Bottlenecks | Bottlenecks discovered late (after misses). | Bottleneck alerts (QA overload, review choke points). |
| Staffing Decisions | Hiring/firing based on gut feel. | Simulated team mix trade-offs to hit the deadline. |
| Time to Learn | Months of projects + post-mortems. | ≈2-week calibration on one project. |
Stop letting AI randomly reshape timelines. Calibrate once, simulate endlessly, ship with confidence.