Not every business process benefits equally from AI integration, and trying to embed AI into everything at once is a reliable way to produce confusion rather than efficiency. The starting point is identifying the workflows where the return is highest and the risk of disruption is lowest.
There are three criteria I use to evaluate whether a workflow is a good candidate for AI integration.
The first criteria is volume. How often does this task happen? A task that occurs once a quarter is not worth building a shared workflow around. A task that happens multiple times per week across multiple team members is. Client email drafting, proposal production, supplier brief processing, social content creation: these are high-volume workflows where the time saved per instance multiplies across the team.
The second criteria is consistency. Does this workflow produce a similar type of output each time, with similar quality standards? If so, a shared prompt framework and a set of quality standards can make the output consistent across the team without requiring every team member to develop the same level of AI skill independently. If the workflow is highly variable and depends on individual judgement at every step, it is harder to systematise and probably better left as individual practice for now.
The third criteria is stakes. What is the consequence of getting it wrong? For client-facing workflows, the verification standards from Modules 7 and 8 apply regardless of who is producing the output. When you extend AI use to a team, you need confidence that every team member applies the same verification rigour. Start with lower-stakes workflows to build the habit and the trust, then extend to client-facing workflows once the standards are established and tested.
For most travel advisory businesses, the workflows that meet all three criteria are: routine client email drafting, social media content production, supplier brief processing and summarisation, and internal operational communications. These are the places to start.
