Adoption forecasting for implementation and scale-out (not long forms)
PROLIFERATE_AI is an adoption forecasting workflow. It combines short structured inputs, operational signals, and qualitative feedback
to estimate adoption likelihood by segment, explain what is driving risk, and track change over time.
Evidence inputs from multiple sources
Forecast + uncertainty range
Key drivers + next actions
Before/after benchmarking
Co-design informed uplift
Public-facing note: the demo is a visual walkthrough to help partners understand what information is needed and what outputs they receive.
What PROLIFERATE_AI does
Simple, public-facing overview.
PROLIFERATE_AI helps teams plan, implement, and scale changes by forecasting adoption likelihood across key groups,
showing what is driving risk, and recommending practical next actions.
What information you provide
Structured inputs (about 5 minutes)
minimal
A small set of implementation constructs rated using simple anchors (0–10) to establish a baseline.
Operational signals
optional
Existing metrics such as onboarding completion, usage, support requests, or compliance indicators.
Qualitative feedback
optional
Open text insights (e.g., notes, feedback themes) that can be synthesised into signals in production.
What users get back
A forecast (likelihood + uncertainty), the top drivers to address, prioritised actions, and a “before vs after” illustration for co-design uplift.
Provide inputs
Capture minimal evidence across multiple sources to generate an adoption forecast.
Short structured inputs are evidence capture for forecasting.implementation-ready, not long forms
Use sample values to explore the workflow, or enter your own. The scoring logic is illustrative (for visual explanation only).
Use sample values
Enter my own
Structured inputs
Operational signals
Qualitative feedback
What these inputs mean
guidance
Each field represents a practical implementation construct (e.g., leadership support, workflow fit).
Teams usually score these using simple anchors based on what they already know, what they observe, and what evidence they can access.
Choose input style
Quick input (simple)
Confidence ranges (advanced)
Do decision-makers sponsor the change, remove blockers, and stay engaged?
How well does the change fit existing routines, roles, and constraints?
Are people prepared to use it safely and consistently (skills + support)?
Are prompts, SOPs, resources, and escalation pathways in place?
Do users believe it improves outcomes, safety, time, or consistency?
0–3 = limited / not in place, 4–6 = partial / inconsistent, 7–10 = strong / routine.
The goal is clarity and consistency, not “perfect precision”.
Confidence ranges (Low / Typical / High)
Useful when different stakeholders have slightly different views or evidence is mixed.
Construct
Low
Typical
High
Leadership alignment
sponsorship strength
Workflow fit
fit with routines
Capability readiness
training uptake
Operational enablement
SOPs & support
Perceived value
value perception
Implementation complexity
higher = harder
Operational signals
Where this information usually comes from
practical
These are often already available in existing systems (training logs, platform analytics, helpdesk reports, audits, or dashboards).
If you do not have these yet, you can leave them as estimates for an initial forecast.
Example: completion of onboarding modules or onboarding checklist.
Example: weekly engagement/utilisation.
Higher can indicate friction (not always negative).
Short notes or themes from staff, leaders, and end-users (e.g., meeting notes, feedback emails, brief interviews, workshop summaries).
In production, these can be synthesised into signals to support prioritisation.
This demo detects simple keywords to illustrate how feedback may influence drivers.
Forecast
Adoption likelihood (overall)
-
-
Uncertainty (illustrative)
-
Shown as a range to support decision-making.
Top driver to address
-
-
How to interpret these results
plain language
Likelihood is the estimated chance that adoption will be strong and sustained, given the evidence you entered.
Uncertainty shows how confident the forecast is (wider ranges usually mean mixed evidence).
Drivers highlight what is pushing risk up or down, and Actions suggest practical next steps.
Segments
Key drivers
Prioritised actions
Before / after (co-design illustration)
Segment
Baseline likelihood
After co-design (illustrative)
Note: The “co-design” button shows an illustrative uplift to demonstrate the concept (redesigning steps with users, improving fit/enablement, reducing friction).
What powers PROLIFERATE_AI
High-level overview (safe for public pages).
Forecasting + scenario simulation
forecast
Produces adoption likelihood estimates and explores the impact of prioritised implementation actions.
Uncertainty exploration
transparency
Expresses uncertainty so stakeholders interpret outputs as probabilistic forecasts, not guarantees.
Qualitative synthesis
signals
Extracts patterns from open text and translates them into actionable drivers (e.g., training needs, friction points).
Optional expert elicitation
advanced
Captures confidence ranges (Low/Typical/High) when evidence varies across stakeholders.
Public note: this page intentionally stays at a high level. Internal modelling pipelines, parameters, and scoring rules are not displayed.
Expression of Interest (EOI) / Contact
Send an enquiry and a brief. This form is configured to work on Netlify.
What to include: what you want to implement/scale, who the users are, what timeline you’re working to, and what information you already have
(structured ratings, operational metrics, qualitative feedback).