Find the useful work
Map the workflows, decisions and friction where AI may create a material improvement. Choose a small number worth testing.
Workplace AI capability consulting / Australia
AI capability that survives Tuesday.
A workshop can create interest. It cannot redesign the work, settle the difficult judgement calls or make useful practice happen afterwards. I help teams move from scattered experimentation to a way of using AI that is practical, responsible and connected to actual work.
GREG WOULFE / NEWCASTLE, AUStart here
This may be the problem if…
- People have access to AI, but useful adoption is patchy and hard to see.
- Different teams are inventing their own standards, prompts and risk boundaries.
- A pilot created activity, but nobody knows what decision it supports.
- HR or L&D has been asked to deliver AI training before the priority work is clear.
- Leaders want progress without pretending every task should suddenly include AI.
My point of view
The capability is not the prompt
Prompting matters, but it is a small part of the job. People also need to recognise a suitable task, provide useful context, inspect the output, protect sensitive information and decide when not to use the tool.
That judgement grows through practice around real workflows. It also depends on the surrounding environment: clear boundaries, access to the right tools, useful examples, manager support and enough permission to test without turning every experiment into a launch.
I start with the work because it gives the capability a reason to exist. We can then build what people need around the decisions, risks and opportunities that are actually present—not around a generic list of AI features.
How I approach it
A practical way through it
The shape changes with the organisation, but the work usually moves through four connected steps.
Set the standard
Agree what good use looks like, where human judgement stays essential and which information or tasks remain outside the boundary.
Practise for real
Build labs, examples and support around work people genuinely need to do. Make inspection and iteration part of the practice.
Test what changed
Look beyond attendance and logins. Measure the quality, speed, confidence, risk and workflow outcomes that justified the work.
Useful outputs
What the work can produce
Not a theatre-sized launch. A clearer operating response that people can use and leaders can make decisions from.
- 01
A prioritised map of AI opportunities tied to real workflows
- 02
Shared standards and practical guardrails for good use
- 03
Role-relevant practice labs, examples and performance support
- 04
A pilot with an explicit decision and evidence plan
- 05
Adoption and capability measures connected to work outcomes
Why me
Why I work across both people and technology
My background runs from frontline operations and global learning to consulting, technology and building software products. That mix matters here. AI adoption is not only a technical problem, and it is not solved by content alone.
I can work with the people closest to the process, translate the opportunity into something testable and stay involved long enough to see whether it holds up outside the workshop.
See the experience behind itStart with a conversation
Bring me the workflow, not an AI shopping list.
If you know something needs to change but the brief is still vague, that is a useful place to start. We can work out where AI belongs—and where it does not.
Talk it through