How to become a TheyDo Pro
The rest of the Help Center explains how TheyDo works. This section is about how to work well: the habits, conventions and operating decisions that separate a workspace people trust from one that quietly goes stale. It is written for practitioners who have the basics down and now have to make journey management stick in a real organization.
What changes when you go from using TheyDo to running it
Early on, the questions are mechanical: how do I create an opportunity, how do I mine insights, how do I share a journey. Once several teams are in the platform, the questions change shape:
- How do we make sure two teams describe the same problem the same way?
- Who owns a journey, and who is allowed to change it?
- How do we keep our data good enough for AI to be useful?
- What do we actually do every quarter, as a rhythm rather than a project?
None of those are answered by a button. They are answered by conventions, and that is what this section collects.
Best Practices
Practical guidance for the craft: writing insights that hold up, opportunities that are actually actionable, metrics that measure something real, and goals people can rally behind.
This resource is continuously updated by our team, and this is where to get started today:
- 6 tips for setting effective Goals in TheyDo, on writing goals your teams can work towards.
- Data governance for AI: preparing your organization to scale, on getting your data and taxonomy in the shape AI needs.
AI Governance
AI is only as good as the context and the conventions around it. This sub section covers how to use TheyDo's AI deliberately rather than opportunistically: what to standardise, what to review, and what to leave to people. Start with Skills best practices, which covers how to turn a good way of working into a skill your whole team can use.
Scaling
What to do when journey management stops being one team's initiative. This sub section deals with the organizational side: the roles involved, how mature your practice is, the rhythms and ceremonies that keep it alive, and how to grow from a handful of journeys to a portfolio.
Advanced
Deeper craft topics for experienced practitioners, including research technique and combining qualitative and quantitative evidence in the same journey.
End-to-End Workflows
Where the other sub sections take one topic at a time, these follow a full arc from start to finish: fragmented data into structured insights, insights into shared context, context into prioritized opportunities, and opportunities into reported business impact. Useful when you want to see how the pieces connect in sequence rather than one at a time.
Note: this section is growing. Several articles listed under Scaling, Advanced and End-to-End Workflows are still being written, so check back if the one you want is not there yet.
A reasonable order to work through it
- Get your language consistent first. Agree your taxonomy and statuses before you have thousands of records to reclassify. See What is a taxonomy?.
- Decide who owns what. Journey ownership and permission tiers are worth settling early. See How journey permissions work.
- Get your data ready for AI, so AI helps rather than adds noise.
- Standardise the good habits into skills, so they survive people changing roles.
- Build a rhythm: a regular moment where journeys are reviewed and priorities are revisited.
Where to start
Read Data governance for AI: preparing your organization to scale first. It is the closest thing this section has to a foundation, because almost every scaling problem turns out to be a data and language problem underneath.