AI and Advanced Workflows
How to use AI goal generation, post-scorecard flows, access controls, and group goal patterns effectively in WeMatter.
Overview
The core goals workflow is simple enough: set a goal, break it into objectives, track progress, add updates, and complete it when the work is done.
The advanced workflows matter when you want better source material, faster draft quality, or a more structured collaborative pattern. In the current product, the most important advanced workflows are AI goal generation, post-scorecard goal creation, access delegation, and group goal structures.
Generate
Use AI to build a stronger first draft from the evidence already inside WeMatter.
Connect
Create goals directly from the post-scorecard flow so review and development remain connected.
Collaborate
Use access roles and group goal structures when the work needs to stay shared, not purely individual.
Why AI generation matters so much
AI goal generation is a genuine product differentiator in WeMatter. It is not just speeding up writing. It is helping the platform use the context it already holds to build a more coherent development draft.
That context can include the latest completed scorecard, submitted Foundation material, and the assigned competency framework. Where those sources are all available, the generated goal can be more grounded in evidence, expectations, and personal context than a blank-sheet manual draft usually is.
What the AI uses as source material
The AI generation flow is designed to work with whatever reliable development evidence is available.
The scorecard gives recent review evidence. The framework gives the capability map. Foundation adds context about priorities, enablers, blockers, and the wider personal picture.
When all three are present, the generated output can be stronger. When some are missing, the platform still works with what it has rather than inventing context that is not there.
That is an important point to explain in the docs, because it helps people trust the output for the right reasons.
Post-scorecard goal generation
One of the clearest AI workflows in the product appears immediately after the WeMatter stage of a scorecard is submitted.
At that point, the platform can offer to generate a personalised goal built on the insight gathered from the scorecard and the other available resources. This is a strong flow because the development opportunity has just been clarified, aligned, and made visible.
In practice, post-scorecard generation is often the clearest example of how WeMatter is meant to work as a connected system rather than a set of isolated modules.
Reading AI output well
Generated goals should still be read critically.
Check whether the goal title is clear enough to live with over time. Check whether the relevance statement explains why this development area matters now. Check whether the objectives are genuinely measurable, and whether the achievables are concrete enough to track without becoming trivial.
A useful review pass usually looks at four things:
- clarity: is the goal easy to understand quickly?
- relevance: does it obviously connect back to the real development need?
- structure: do the objectives and achievables make the work manageable?
- realism: do the timeline and ambition level feel workable in practice?
The strongest use of AI in this flow is as a high-quality first draft that humans can sharpen, not as a substitute for judgement.
Manual creation still has a role
Even with strong AI support, manual creation still matters.
Sometimes the goal is already obvious. Sometimes the development area is too sensitive, too early-stage, or too organisation-specific for AI to be the best first move. In those cases, the manual workflow gives you more direct control over the framing from the start.
The best documentation should make both things true at once: AI generation is a core feature, and manual creation is still a valuable option.
Access as an advanced collaboration tool
The access model is not only governance. It is also part of how goals become collaborative.
Owners hold the goal. Editors help manage and maintain it. Contributors add a supportive layer through updates and replies. Broader organisational roles may also gain edit-level access depending on the product rules.
At its best, that structure lets a goal stay owned without becoming isolated.
Group goals
Group goals are one of the more specialised patterns in the goals area.
They are useful when several people need to contribute to the same business, team, or organisational outcome, even if their individual development goals are different. The group goal carries the shared outcome and group objectives. The connected individual goals show how each person contributes through their own personal objectives, achievables, and updates.
The more advanced behaviour sits in the group workspace. Progress rolls up from connected participant objectives and achievables. Activity brings together updates and key events from the linked individual records. Broadcast updates let an editor post once from the group goal or group objective thread and copy that message into the connected individual threads.
For the detailed guide, read Group Goals.
Advanced quality checks
Whether a goal was written manually or generated with AI, the same higher-level questions still apply.
Is the goal specific enough to guide action without becoming rigid? Do the objectives describe meaningful milestones rather than generic intentions? Are the achievables concrete enough to track? Does the update pattern show real movement over time? And does the access model reflect who actually needs to participate?
These are the checks that turn a technically valid goal into a genuinely useful one.
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