The Problem

Most teams are underusing AI or using it without structure

Research shows a significant gap between what AI tools can do and how most teams actually use them. The capability is there. The adoption isn't.

This isn't because teams don't care. It's because they've been left to figure it out on their own, without structure, without shared standards, and without anyone showing them what good looks like in their specific context.

Ad-hoc experimentation

A few people on the team are using AI. Most aren't. There's no shared approach, no consistency, and no way to scale what's working.

Generic training that doesn't stick

Teams sit through "intro to AI" sessions, nod along, and go back to doing things the same way. The training was too abstract to act on.

No framework for deciding what to automate

Everyone has ideas about where AI could help, but there's no structured way to evaluate which processes are actually worth automating first.

What a Workshop Looks Like

Half a day. Six structured blocks. Your team builds something real.

Block 1

Understanding the landscape

Where AI capability actually is versus how most teams use it. Setting context for why structured adoption matters more than scattered experimentation.

Block 2

Tools and setup

Getting everyone on the same tools, understanding the differences between interfaces, and learning to choose the right model for the right task. Everyone leaves set up and ready.

Block 3

Building shared context

Setting up shared projects, knowledge bases, and team structures so AI has the context it needs to produce genuinely useful, consistent outputs across the team.

Block 4

Mapping and scoring processes

Teams map their own workflows, then score each one for automation potential using a practical framework: repetitiveness, structure, output clarity, and time cost.

Block 5

Hands-on building

The core of the workshop. Teams build real automations for their highest-scoring processes using their own data and real-world examples. Not hypothetical exercises.

Block 6

Show, tell, and roadmap

Teams demo what they built, share learnings, and leave with a clear adoption roadmap for the weeks ahead. Momentum doesn't stop when the workshop ends.

Outcomes

What your team leaves with

Working automations

Not theory. Your team builds real automations during the session using their own processes and data. They walk out with something they can use immediately.

A scored process list

Every team maps and scores their workflows for automation potential. This gives you a clear, prioritised backlog of what to build next, not just gut feel.

Shared team standards

Projects, naming conventions, and shared knowledge bases are set up during the session. The team leaves with a consistent foundation, not individual experiments.

Model fluency

Your team understands which AI model to use for which task, how to manage usage limits, and how to get better outputs by structuring inputs well.

Connected tools

Teams learn how to connect AI to the tools they already use, pulling data from project management, docs, and email into automated workflows.

An adoption roadmap

A clear plan for the weeks after the workshop: what to build next, what to refine, and how to keep momentum going so the training compounds over time.

Fit

Who these workshops are for

A good fit if...

You have a marketing team, agency team, or leadership team that's using AI inconsistently or not at all
You want structured, guided adoption rather than leaving people to figure it out individually
You care about practical outcomes and want your team to walk out with something they can use immediately
You have repeatable processes that could benefit from automation but aren't sure where to start

Not the right fit if...

You're looking for a high-level "future of AI" keynote or awareness session
Your team doesn't have processes that are repeatable enough to benefit from structured AI adoption
You want someone to build your AI workflows for you rather than teaching your team to build them

Hands-on AI training that your team will actually use