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Half Day | Max. 15 Participants
Workshop Outcomes
- Explain in plain language what current AI tools do well, what they do poorly and why the difference matters
- Predict before starting a task whether AI is likely to help or quietly hurt
- Map the repetitive and low judgement work in your own role, and rank it by what it actually costs
- Verify AI output properly, checking at source rather than asking the tool whether it was right
- Apply the confidentiality boundary confidently, knowing what may and may not be entered into which tools
- Recognise when something needs to be escalated, and to whom
- Take two or three practical starting points back to your own work
Topics Covered
- What AI actually is and is not, in plain English, with no jargon and no coding
- Why these tools generate plausible text rather than retrieve verified facts, and what follows from that
- Finding the friction in your own role: what is repetitive, what it costs, and what is worth changing
- Verifiable versus unverifiable tasks, and why that distinction determines where to start safely
- What you already have: AI features sitting inside software you use every day
- What goes in and what stays out: confidentiality, client data and personal information
- Verification as the core skill, including why you never ask a tool to check itself
- Automation bias: why we defer to confident systems, and why the risk grows as tools improve
- When to tell someone: recognising what is worth escalating and why organisations without a clear threshold simply hear less
- How AI changes the shape of a role, and the questions worth asking your own manager
Location
- Willing to work remote
- Willing to work interstate
- Willing to work internationally
How can this workshop be delivered?
- Face to Face
- Virtual
Overview
Most people using AI at work learned it alone, quickly, in the middle of doing something else. The decisions are small and constant. Should I put this into that tool? Is this output right? Do I need to tell anyone? Nobody supervises those decisions in real time, which is why capability at this level determines whether an organisation's AI policy means anything at all. This session builds that capability. It is a plain English, hands on workshop with no jargon, no coding and no assumed background. Participants work on their own real tasks throughout. The central skill is verification. These tools produce text that reads like competent work whether or not it is accurate, so the failure mode looks exactly like success. Participants also map the repetitive work in their own roles before any tool is discussed, which is how teams find the work that actually costs them time.
Participants leave with practical skills they can immediately apply to their own work, including how to identify worthwhile opportunities for AI, verify outputs, and use AI confidently without compromising quality or accountability. The workshop helps them turn everyday AI use into a more deliberate, efficient and responsible way of working.
Workshop Outcomes
- Explain in plain language what current AI tools do well, what they do poorly and why the difference matters
- Predict before starting a task whether AI is likely to help or quietly hurt
- Map the repetitive and low judgement work in your own role, and rank it by what it actually costs
- Verify AI output properly, checking at source rather than asking the tool whether it was right
- Apply the confidentiality boundary confidently, knowing what may and may not be entered into which tools
- Recognise when something needs to be escalated, and to whom
- Take two or three practical starting points back to your own work
Topics Covered
- What AI actually is and is not, in plain English, with no jargon and no coding
- Why these tools generate plausible text rather than retrieve verified facts, and what follows from that
- Finding the friction in your own role: what is repetitive, what it costs, and what is worth changing
- Verifiable versus unverifiable tasks, and why that distinction determines where to start safely
- What you already have: AI features sitting inside software you use every day
- What goes in and what stays out: confidentiality, client data and personal information
- Verification as the core skill, including why you never ask a tool to check itself
- Automation bias: why we defer to confident systems, and why the risk grows as tools improve
- When to tell someone: recognising what is worth escalating and why organisations without a clear threshold simply hear less
- How AI changes the shape of a role, and the questions worth asking your own manager
Who is this workshop for
Staff, teams and individual contributors across any function, including operations, administration, finance, marketing, customer service and human resources.
It suits organisations rolling out AI tools, organisations where staff are already using AI with or without approval, and teams who want a shared starting point and a common language before anything more formal begins.
No prior AI experience is required and no technical background is assumed.
The session works best without managers present. Shadow usage and workload pressure are the most valuable things it surfaces, and staff discuss both far more openly among peers. The anonymised friction map produced in the session can be provided to leadership afterwards.
Handouts/materials provided
1. Friction mapping worksheet for identifying and ranking repetitive work
2. Verification checklist
3. What goes in and what stays out card, covering confidentiality and personal information
4. Escalation prompt sheet
5. Plain English glossary of terms participants will keep encountering
6. Shared prompt library template
What to bring
Participants should bring one recurring task from their own work that they find tedious or repetitive. This becomes their working example throughout the session and is the single thing that makes the day useful rather than general.
A device with internet access is helpful for the hands on segments but is not essential. For virtual delivery, a stable connection and working audio are required.
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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