AI Readiness, Workflow & Knowledge Systems
A 40-minute facilitated discussion on what AI readiness actually looks like across tools, skills, workflows and structure as one connected idea. The room works through where the team's AI use sits today, which foundations are in place, how AI is plugged into the workflow, where the knowledge debt has built up and what the single move that unlocks everything else needs to be. Five strategic inputs feed a take-home recap.
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Digital Tourism Think Tank
Where does the team’s AI work actually sit today?
Five maturity stages. Pick the stage that best describes the team today. This anchors the rest of the conversation: the foundations, workflow integration, knowledge debt and first-move work all takes a different shape depending on the starting point.
Pick a stage to see what it means in practice.
The stages are not a ladder to climb at speed. Each stage has its own foundations, workflow questions and knowledge constraints. Picking the right one is the first input the recap engine reads from.
Which foundations are actually in place?
Nine foundations across Tools, Knowledge and Process. Click each to cycle through Missing, Started or Done. The shape that emerges reveals which row of the stack needs work first.
Mark a handful of foundations to see the team’s foundations shape.
A picture of which foundations are already in place and which still need work. The shape that emerges reveals whether the gaps cluster in Tools, Knowledge or Process, and which row of the stack to invest in first.
Where does AI plug into the team’s workflow?
Five stages of the team’s content pipeline. For each stage, pick AI’s role: None, Drafts, Co-author or Agent. The pattern reveals where AI is genuinely integrated and where it is still sitting on the edges.
Assign AI’s role at each stage to see the integration pattern.
A picture of where AI is actually integrated into the team’s work and where it is sitting on the edges. The pattern reveals whether AI is being used at the start of the pipeline (drafting and ideation), at the end (publishing and distribution), or genuinely woven through.
How much knowledge debt has the team built up?
Four debt categories: tacit, distributed, outdated and missing. For each, set the level of debt the team currently carries. The pattern reveals where AI is being asked to work with knowledge the team has not yet captured.
Set the debt level on each category to see the team’s knowledge debt picture.
A picture of where AI is being asked to work with knowledge that has not yet been properly captured. The pattern of debt reveals whether the work to do is capture, consolidation, refresh, or whether the gaps are about knowledge the team should hold but does not.
Which move unlocks everything else?
Six candidate first moves. Pick the single one that, if the team committed to it over the next quarter, would unlock the rest. The pick becomes the priority signal the recap engine reads from.
Pick the single move that unlocks everything else.
The recap engine treats the chosen move as the priority signal. The biggest-unlock recommendation will address this move first. The other five are sequenced behind it and feed into the medium-term work.
Where the work goes next.
Three recommendations adapted to the inputs across Steps 01 to 05. Save the PDF to take the full reasoning back to the team. (Full recap engine activates once Steps 02 to 05 are built.)
A read of the team's current position.
Move through Steps 01 to 05 to see this card populate with a read on the team's current AI readiness position, the strengths it carries and the gaps that need closing.
The single biggest move available.
The unlock card surfaces the one move that would shift the team's position most. It looks at where the team is now and where it would need to sit to keep AI investment defensible over the next twelve months.
Cadence for the next six months.
AI Readiness, Workflow & Knowledge Systems