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Full Day | Max. 20 Participants
Workshop Outcomes
- Establish an honest organisational baseline, including AI already in use through vendor updates nobody authorised
- Map and rank the friction across functions, using the people who actually do the work rather than the process as documented
- Produce a negative list identifying where AI will not be applied in your organisation and why
- Assess data, systems, governance and people readiness, and identify what must be fixed before anything else is possible
- Build a first pass AI register covering standalone tools and features embedded in existing software
- Assess exposure to the automated decision-making transparency obligation commencing 10 December 2026
- Assign named human accountability for every consequential automated decision
- Agree a sequenced roadmap covering the first ninety days, the following nine months, and what will not be pursued
Topics Covered
- What AI enablement actually is, and how it differs from adoption and from consulting
- Position: establishing where the organisation genuinely stands, including shadow usage
- Value: finding the friction across functions and ranking it by what it costs
- The four value types, and why anything fitting none of them is a hobby rather than an initiative
- The negative list: deciding where AI will not be applied, and why a defensible no enables a confident yes
- Readiness across data, systems, governance and people, and the legitimacy of a twelve-month foundations answer
- Risk: data exposure, accuracy, privacy, intellectual property, discrimination, vendor risk and accountability
- The Australian regulatory position: no AI Act, no safe harbour, and what actually binds
- The 10 December 2026 privacy obligation, including why it captures spreadsheets and legacy rules engines
- Decide: build, buy, wait or stop, and the contract terms that matter
- Implement: why pilots stall short of production, and designing for production from the outset
- Measure: baselines, separating usage from adoption from value, and deciding what released capacity is for
- The internal AI lead: why the role comes last rather than first, and who it should be
- Sequencing the first ninety days across functions
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 organisations approach AI in the wrong order. Tools are bought before the work is mapped. Policy is written after everyone is already using something. An internal expert is appointed before anyone knows what the role is for. These are failures of sequence, and they are expensive.
This session works through the full enablement pathway with a cross functional group in one room, which is the only way the sequence holds.
AI enablement means building an organisation's own capacity to make good decisions about AI, rather than having those decisions made for it. Participants establish an honest baseline, map the friction across functions, decide where AI will not be used, assess readiness, and build the beginnings of an AI register. Regulatory exposure is addressed in current terms, including the automated decision-making obligation commencing 10 December 2026. The session is non-technical and produces organisational artefacts.
Workshop Outcomes
- Establish an honest organisational baseline, including AI already in use through vendor updates nobody authorised
- Map and rank the friction across functions, using the people who actually do the work rather than the process as documented
- Produce a negative list identifying where AI will not be applied in your organisation and why
- Assess data, systems, governance and people readiness, and identify what must be fixed before anything else is possible
- Build a first pass AI register covering standalone tools and features embedded in existing software
- Assess exposure to the automated decision-making transparency obligation commencing 10 December 2026
- Assign named human accountability for every consequential automated decision
- Agree a sequenced roadmap covering the first ninety days, the following nine months, and what will not be pursued
Topics Covered
- What AI enablement actually is, and how it differs from adoption and from consulting
- Position: establishing where the organisation genuinely stands, including shadow usage
- Value: finding the friction across functions and ranking it by what it costs
- The four value types, and why anything fitting none of them is a hobby rather than an initiative
- The negative list: deciding where AI will not be applied, and why a defensible no enables a confident yes
- Readiness across data, systems, governance and people, and the legitimacy of a twelve-month foundations answer
- Risk: data exposure, accuracy, privacy, intellectual property, discrimination, vendor risk and accountability
- The Australian regulatory position: no AI Act, no safe harbour, and what actually binds
- The 10 December 2026 privacy obligation, including why it captures spreadsheets and legacy rules engines
- Decide: build, buy, wait or stop, and the contract terms that matter
- Implement: why pilots stall short of production, and designing for production from the outset
- Measure: baselines, separating usage from adoption from value, and deciding what released capacity is for
- The internal AI lead: why the role comes last rather than first, and who it should be
- Sequencing the first ninety days across functions
Who is this workshop for
Organisations wanting a shared, cross functional starting point rather than separate conversations in separate rooms.
The room should include leadership, operations, and the functions where the work actually happens, along with anyone responsible for privacy, risk or technology. Representation matters more than seniority. Leadership knows the process as designed; the people doing the work know it as performed, and the gap between those two is where most of the value sits.
It suits organisations under pressure to act without an agreed direction, organisations where AI has arrived through vendor updates nobody authorised, and those that need to understand their exposure to the December 2026 privacy obligation before it commences.
Suitable for corporate, government, not for profit and larger small and medium business contexts. No technical background is required.
Handouts/materials provided
1. Friction mapping worksheet, structured for cross functional use
2. Negative list template, for recording where AI will not be applied
3. Readiness assessment across data, systems, governance and people
4. AI register starter, covering standalone tools and embedded software features
5. Risk and regulatory register template
6. AI Readiness checklist
7. Accountability map, assigning named ownership to automated decisions
8. Sequenced roadmap template covering ninety days, twelve months and not now
What to bring
No preparation is essential, though the session is considerably more productive with material in the room.
Useful items include any existing AI or acceptable use policy, the organisation's privacy policy, a technology or systems inventory, and a list of the main software platforms in use.
Participants are also encouraged to bring one recurring task from their own area that they find repetitive or costly. These become the raw material for the friction mapping segment, and they are the reason a cross functional room produces a better map than a leadership team working alone.
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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