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Half Day | Max. 15 Participants
Workshop Outcomes
- Review and supervise AI assisted work, which arrives looking finished regardless of whether it is
- Ask for the verification basis rather than the output, and recognise when someone has agreed rather than checked
- Distinguish team members who are genuinely capable from those who are merely confident
- Respond to shadow AI usage in a way that surfaces the practice without destroying reporting culture
- Apply an AI policy you did not write, and identify the gaps in it that need escalating
- Have an honest conversation with your team about how their roles are changing
- Recognise local systems and spreadsheets that may fall within the December 2026 privacy obligation
Topics Covered
- What current AI tools do well and poorly, and what failure looks like in someone else's work
- Supervising work you did not do, when the usual cues for effort and competence are absent
- Checking the load bearing facts first: numbers, names, dates, citations and commitments
- Confident inference: recognising output that fills gaps the source material did not cover
- Capable versus confident, and why the correlation weakens as people use these tools more
- Sampling rather than reviewing everything, and building a team that verifies its own work
- Shadow AI usage: separating the person from the practice, and what to escalate
- Applying policy you did not write, and feeding the gaps back upward
- The Australian position, and why local spreadsheets are often the systems most at risk of being missed
- The roles conversation: saying what is known, what is not, and why reassurance beyond the evidence costs you credibility permanently
Location
- Willing to work remote
- Willing to work interstate
- Willing to work internationally
How can this workshop be delivered?
- Face to Face
- Virtual
Overview
Supervisors and emerging leaders hold the hardest position in any AI rollout. They are accountable for work they did not do, produced with tools they did not choose, under a policy they did not write. They are also first to see everything. First to notice staff using tools without approval, first to be asked whether this means my job, and first to decide whether an output is good enough to pass upward.
None of those situations come with authority attached. The central skill is supervising AI assisted work, which is genuinely different from supervising ordinary work. It arrives structured, confident and polished, so the traditional signals of effort and competence are gone. Participants learn to ask for the verification basis rather than the output, to check the facts that carry consequence, to tell capability from confidence, and to handle shadow usage so it produces information rather than silence.
Workshop Outcomes
- Review and supervise AI assisted work, which arrives looking finished regardless of whether it is
- Ask for the verification basis rather than the output, and recognise when someone has agreed rather than checked
- Distinguish team members who are genuinely capable from those who are merely confident
- Respond to shadow AI usage in a way that surfaces the practice without destroying reporting culture
- Apply an AI policy you did not write, and identify the gaps in it that need escalating
- Have an honest conversation with your team about how their roles are changing
- Recognise local systems and spreadsheets that may fall within the December 2026 privacy obligation
Topics Covered
- What current AI tools do well and poorly, and what failure looks like in someone else's work
- Supervising work you did not do, when the usual cues for effort and competence are absent
- Checking the load bearing facts first: numbers, names, dates, citations and commitments
- Confident inference: recognising output that fills gaps the source material did not cover
- Capable versus confident, and why the correlation weakens as people use these tools more
- Sampling rather than reviewing everything, and building a team that verifies its own work
- Shadow AI usage: separating the person from the practice, and what to escalate
- Applying policy you did not write, and feeding the gaps back upward
- The Australian position, and why local spreadsheets are often the systems most at risk of being missed
- The roles conversation: saying what is known, what is not, and why reassurance beyond the evidence costs you credibility permanently
Who is this workshop for
Team leaders, supervisors, coordinators, emerging leaders and anyone accountable for reviewing work produced by others. It suits organisations that have trained their staff and briefed their executives, and left the supervisory layer to work it out from a policy document.
Particularly valuable where AI tools are already in use across teams, where shadow usage is suspected but unconfirmed, or where supervisors are fielding questions about job security without any information to answer them with.
No technical background is required. The session works best without senior managers present, since supervisors discuss shadow usage and workload pressure far more openly when their own managers are not in the room.
Handouts/materials provided
1. Verification questions for reviewing AI assisted work
2. Capable versus confident diagnostic
3. Escalation decision card, defining what to handle and what to raise
4. Conversation guide for questions about roles and job security
5. Local systems checklist, for surfacing spreadsheets and tools that may fall in scope
6. Shadow usage response guide
What to bring
No preparation is essential. Participants are encouraged to bring one recent example of work they reviewed that was produced with AI assistance, with any confidential detail removed. This becomes their working example through the supervision segments.
If the organisation has an AI or acceptable use policy, participants should bring it. Much of the session's value comes from translating general policy into the specific situations supervisors actually face.
Facilitator
About the facilitator
Over more than 30 years, Razz has helped organisations improve performance through technology, marketing and business transformation, working with IBM, Westpac, The Salvation Army, Jefferies and Company, Computer Based Solutions and Kenneth Cole, alongside thousands of Australian small and medium businesses.
His focus is not AI for its own sake. It is workflow automation, business systems, productivity and process improvement, with AI applied where it genuinely improves the outcome. The method is deliberately problem-first: identify the repetitive, manual and low-value work, quantify what it costs, then select the technology.
That approach is grounded in commercial and delivery discipline. Razz is a former CPA, an accredited PRINCE2 project manager, a member of the Australian Marketing Institute and holds a Master of Teaching, so sessions carry the financial rigour of a finance function, the delivery structure of a project office and the instructional design of a qualified teacher.
His facilitation style is practical and hands-on. Participants work on their own processes, their own numbers and their own documents, and leave with working examples and measurable outcomes rather than theory.
His philosophy is straightforward: AI should remove friction, improve decision-making, and let people spend their time on work that is actually worth their attention.
Key Expertise
- AI Strategy and Business Transformation
- AI WorkflowAutomation and Process Improvement
- Marketing Strategy and Customer Growth
- Business Systems Design
- Digital Transformation
- Prompt Engineering and AI Adoption
Industry Focus
- Financial Services
- Government
- Professional Services
- Retail
- Not for Profit
- Technology
- Small and Medium Business
Based in:
About the facilitator
Over more than 30 years, Razz has helped organisations improve performance through technology, marketing and business transformation, working with IBM, Westpac, The Salvation Army, Jefferies and Company, Computer Based Solutions and Kenneth Cole, alongside thousands of Australian small and medium businesses.
His focus is not AI for its own sake. It is workflow automation, business systems, productivity and process improvement, with AI applied where it genuinely improves the outcome. The method is deliberately problem-first: identify the repetitive, manual and low-value work, quantify what it costs, then select the technology.
That approach is grounded in commercial and delivery discipline. Razz is a former CPA, an accredited PRINCE2 project manager, a member of the Australian Marketing Institute and holds a Master of Teaching, so sessions carry the financial rigour of a finance function, the delivery structure of a project office and the instructional design of a qualified teacher.
His facilitation style is practical and hands-on. Participants work on their own processes, their own numbers and their own documents, and leave with working examples and measurable outcomes rather than theory.
His philosophy is straightforward: AI should remove friction, improve decision-making, and let people spend their time on work that is actually worth their attention.
Key Expertise
- AI Strategy and Business Transformation
- AI WorkflowAutomation and Process Improvement
- Marketing Strategy and Customer Growth
- Business Systems Design
- Digital Transformation
- Prompt Engineering and AI Adoption
Industry Focus
- Financial Services
- Government
- Professional Services
- Retail
- Not for Profit
- Technology
- Small and Medium Business
Based in:
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