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An AI workshop should end with a task your team can test.
Here is one exercise I would use for a small business team:
Input: a quotation request that names the product but leaves out the quantity.
AI prepares: the details it can identify, a list of missing information and a draft response asking for the quantity.
People check: whether it invented a price, assumed a quantity or promised a delivery date. Those details must come from an approved source or a person.
Then change the input: add an unclear product name, a conflicting instruction or a question the business guide cannot answer. Does the workflow stop and ask, or quietly guess?
The useful takeaway is a repeatable task, a checklist for checking the output and a clear handover to a person. Teaching that process is a different service from building and connecting a production system.
For a team session, bring one repetitive task, anonymised sample inputs and an example of an acceptable result. Keep customer details and confidential documents out of an unapproved AI tool.
I have put together a short team brief and workshop guide here:
https://futurespanai.com/guides/choosing-an-ai-workshop
Lau Kuan Ee · FutureSpan AI · Independent AI & digital consultant in Singapore
futurespanai.com
Choosing an AI Workshop for Your Singapore Team | FutureSpan AI
Choose workshop goals, practical exercises and outputs for your team. Understand the four-hour group scope, preparation and how learning differs from implementation.
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A strong specialist skill is useless if the wrong system gets invoked. Long-running AI work breaks in boring, expensive ways.
That is not a prompting problem. It is a routing and authority problem.
How I can help you:
I build project routers for teams using multiple AI workflows, agents, or specialist assistants so that they work with each other as a team.
I design source-of-truth resolutions so your agents retrieve the current rules instead of improvising or relying on assumptions or stale chat data.
I create reusable skill registries and specialist handoff logic hardened by determined and dedicated flow states reducing the need for model reliance on its own internal and displaced memory context.
I separate project states with individualized canonical states bound by rules living under essential model instructions and routed properly so unrelated workflows do not contaminate one another, but exist with awareness of the role each project plays, enabling a persistently growing ecosystem to develop organically.
I add governance wrappers, escalation hooks, and recovery mirrors around existing AI stacks so that trust is designed at the root, and variance is eliminated with rule based source archiving and implementation.
Trust means closing your eyes at night and sleeping soundly because your agents got your back. And I can put it all together for you, precision results sourced and undisputed because I got tired of doing things the hard way.
Phillip, agree that routing is where it breaks. In our builds the worst bugs came from an agent pulling an old rule from chat history instead of the current source. Pinning one source of truth per project fixed more than any prompt change did.
I don't think every business needs an AI agent.
That's probably not the popular opinion these days, but it's the one I've come to after building automation workflows.
A lot of the conversations I have start with the same request:
"We want an AI agent."
So I ask a different question.
"What's slowing your team down today?"
The answers are rarely about AI.
It's usually someone copying information between tools.
Someone sending the same emails every week.
Someone updating spreadsheets by hand.
Or someone chasing people for updates.
Those are workflow problems.
Sometimes a simple automation fixes them.
Sometimes AI is the right next step.
The trick isn't choosing the newest technology.
It's choosing the one that actually solves the problem.
That's why I spend more time understanding the process than deciding which AI model to use.
The technology is important.
The workflow is what people actually live with every day.
What's your view on this?
Do you start with AI, or do you start by fixing the process?
