Workforce Planning IIS — Barwon Water

PRIMING
This is a drafting engagement. Treat the output as a draft for my review, not a final deliverable. We will iterate.

CONTEXT
I am the Project Lead for the Barwon Water Workforce Planning programme. The audience is the executive sponsor (P&C GM, Digital GM, Finance GM) plus the MD, who will sign off the charter. They are commercially literate, time-poor, and need a charter that protects the project's authority, scope, and resourcing through to end of August. The triple mandate is: (1) build a workforce planning methodology the organisation owns, (2) deliver PS28 financial inputs, (3) take a strategic position on workforce shape under AI / future-of-work conditions.

FRAMEWORK
Approach this as a senior P&C consultant preparing material for executive sign-off on a strategic programme. Use the Charter format outlined in Section 04 of the Workforce Planning IIS. Apply Working Principle 01.2 (defensibility earns the right to move fast) and 01.4 (the model is the system of record).

INSTRUCTIONS
Produce a 2-3 page charter with these headed sections, in this order: (1) Purpose (2 paragraphs); (2) Scope — in / out (bulleted); (3) Success criteria (3-5 measurable); (4) Governance (table: Sponsor / Steering / Working Group); (5) Milestones (5 phases × dates); (6) Key risks (top 5, with mitigations); (7) Assumptions; (8) Sign-off block. Max 1,000 words.

EXAMPLES
A good charter reads decisive, specific, and brief. Purpose names the triple mandate clearly. Scope is broad enough to flex but narrow enough to be defensible. Success criteria are measurable (e.g. 'PS28 financial inputs delivered by 31 August', not 'good outputs'). Risks are real (data quality, stakeholder bandwidth, strategic decision dependency) — not boilerplate.

RULES
Australian English. Plain language. Active voice. None of the banned phrases in Section 04 of the IIS. Where the charter references workforce shape strategy, frame as a Board-level decision, not a P&C decision. Flag any factual claim, dollar value, date, or named party you cannot fully support from the workshop notes.

ITERATION
After producing the draft, ask me three questions: what to refine, what to expand, what to remove. Wait for my answers before redrafting.

ASSUMPTIONS
If the workshop notes don't clarify the executive sponsor, the budget envelope, the working group composition, or the Steering Group cadence, pause and ask before producing.

WORKSHOP NOTES:
[Paste workshop notes — sponsor decisions, scope discussions, agreed governance, milestone constraints, named risks, working group commitments.]

PRIMING
This is a design engagement. Output is a structured guide for me to use as the interviewer. I will adapt phrasing in the room.

CONTEXT
I am preparing to run 12-15 structured 60-minute interviews with senior leaders at Barwon Water. The interviews surface workforce demand signals, capability gaps, single points of failure, and talent management concerns. The synthesis from these interviews feeds the demand-supply driver register and the capability framework.

FRAMEWORK
Approach this as a senior workforce planning interviewer designing a guide for cross-functional leader interviews. Apply Working Principle 01.5 (human intelligence and data intelligence are complementary). Use the question structure pattern from the Workforce Planning IIS.

INSTRUCTIONS
Produce a structured guide with 5 sections: (1) Business strategy assessment — 3 questions; (2) Organisational assessment — 3 questions; (3) Workforce requirements — 4 questions; (4) Talent management — 3 questions; (5) Open-ended close — 2 questions. For each question: (a) the question itself; (b) a one-line 'why we are asking'; (c) 1-2 follow-up probes. Add a 5-minute opening and 5-minute close.

EXAMPLES
Good interview questions are open, neutral, specific, and inviting. They surface signals that don't show up in HRIS. Example: 'Where do you have single points of failure — one person holding critical knowledge?' rather than 'Are there succession gaps?'

RULES
Australian English. Plain language. No leading questions. No jargon a senior operational leader wouldn't recognise. Maintain neutrality. Flag any question that risks surfacing personal information about specific employees — those are routed to a 1:1 conversation, not the interview record.

ITERATION
After producing the guide, identify three questions that are likely to elicit short answers and propose a probe to deepen each.

ASSUMPTIONS
If the leaders' areas of focus aren't clear, ask before producing — the guide may need light tailoring (e.g. capital delivery leaders vs operational leaders).

INTERVIEWEE CONTEXT:
[Optional. Paste names, roles, focus areas, and any pre-briefing context. If empty, produce a generic guide.]

PRIMING
This is a drafting engagement. Output is a v1 framework draft for me to validate with operational leaders.

CONTEXT
Barwon Water has existing capability scaffolding — a Leadership Framework, a 2030 Growth Framework with 12 organisational competencies, and 10 prioritised Organisational Capabilities. I am building a unified workforce capability taxonomy that combines four dimensions: technical/role skills, leadership, compliance credentials, and Growth Framework competencies — with a new AI / orchestration overlay added for 2026+.

FRAMEWORK
Approach this as a senior capability framework specialist building a taxonomy for a regional water utility. Use four proficiency levels: Foundational → Applied → Accomplished → Leading. Reference the existing Barwon Water frameworks listed in the context block.

INSTRUCTIONS
Produce a v1 capability taxonomy with: (1) 30-50 capabilities organised across the four dimensions plus the AI overlay; (2) For each: capability ID, name, one-sentence definition, the 4 proficiency descriptors, segment classification (Strategic / Core / Requisite / Non-core); (3) A role-to-capability mapping table for 10-20 representative roles; (4) Explicit flags on the new AI / orchestration capabilities. Output in a format that copies cleanly into Excel.

EXAMPLES
A good capability is durable across role changes, has clear proficiency progression, and has unambiguous segmentation. 'Asset Investment Planning' is a capability; 'Senior Asset Engineer' is a role. The proficiency descriptors describe behaviour at each level — not just words like 'better' or 'more advanced'.

RULES
Australian English. Don't conflate role and capability. 30-50 capabilities is sufficient — resist over-engineering. Flag any capability that overlaps significantly with an existing Leadership Framework competency.

ITERATION
After producing the framework, identify the three capabilities most likely to be challenged by operational leaders, and propose a refinement to each.

ASSUMPTIONS
If the position descriptions don't cover a major capability area (e.g. cyber, AI, climate adaptation), surface the gap and propose how to address it before producing the full taxonomy.

INPUTS:
[Paste position descriptions, the Leadership Framework excerpts, the 12 Growth Framework competencies, the 10 Organisational Capabilities list, and any internal capability literature.]

PRIMING
This is a drafting engagement for a Board-level decision paper. Output is a draft for me to iterate with the coalition GMs before submission.

CONTEXT
The Board must decide what workforce shape Barwon Water adopts over 2028-2033 in an AI-enabled environment. The Digital GM has framed one pathway ('Protect & Transform' — protect engineering, transform operations through AI, allow back-office to attrite). My job is to present 2-3 credible options for Board choice.

FRAMEWORK
Approach this as a strategic advisor drafting an options paper for ELT and Board. Apply Working Principle 01.7 (real options, not straw men). Stress-test each option equally — do not flatter any single one. Use the Strategic Options format from Section 04 of the IIS.

INSTRUCTIONS
Draft an 8-12 page options paper with: (1) Executive summary — 1 page (decision required, recommendation, key trade-offs); (2) Strategic context — 2 pages; (3) Each option in detail — 2 pages each: description and underlying philosophy, workforce shape implications, cost (5-year trajectory), capability outcome, inclusion impact, risk profile, change burden, timeline, conditions for success; (4) Comparison table — 1 page side-by-side; (5) Recommendation with rationale — 1 page; (6) Decision required — clear ask.

EXAMPLES
A good options paper presents real choices, with the cases for and against each clearly drawn. Recommendation is decisive — one or two sentences with no hedging. The strongest option is supported with evidence, not adjectives. Risks of the recommended option are surfaced honestly.

RULES
Australian English. Analytical, balanced, decisive tone. Stress-test each option equally — Option A (Digital GM's pathway) is examined as critically as Options B and C. Apply Constraint 02.5 (Fair Work obligations) and 02.6 (inclusion targets). Honest about uncertainties and risks.

ITERATION
After producing the draft, identify the three sections most likely to attract challenge from the Board and propose a sharper version of each.

ASSUMPTIONS
If you cannot articulate a clear recommendation, the options are not yet well understood. Surface this before producing the recommendation section.

INPUTS:
[Paste the intervention scenario pack, gap analysis, capability segmentation, financial implications by option, inclusion impact modelling, the Phase 2 interview synthesis themes, the Digital GM's framing email.]

PRIMING
This is a drafting engagement for a regulator-facing document. Output is a draft to be reviewed by Pricing/Strategy before lodgement.

CONTEXT
The Essential Services Commission applies the PREMO framework (Performance, Risk, Engagement, Management, Outcomes) to Victorian water price submissions. Workforce costs are a major component. The narrative I am drafting accompanies the workforce financial inputs handed to Link 8 (Revenue Model).

FRAMEWORK
Approach this as a regulatory submission writer with deep familiarity with PREMO. Apply Working Principle 01.4 (every assertion ties to evidence in the model) and 01.2 (defensibility earns the right to move fast).

INSTRUCTIONS
Draft the workforce planning narrative covering: (1) Workforce overview; (2) Strategic context; (3) Demand drivers — capital, digital, regulatory, climate, customer; (4) Supply context — Geelong labour market, retirement, attrition, EA outcomes; (5) Capability investment cases — prudency and efficiency arguments; (6) Productivity assumptions — including AI uplift, with stated confidence levels and rationale; (7) Workforce shape strategy — the pathway selected and the reasoning; (8) Financial trajectory — narrative explanation of FTE, labour cost (opex / capex split), contractor; (9) Sensitivity and risk; (10) Customer outcomes link. Aim for 15-25 pages.

EXAMPLES
Good regulatory narrative is plain, evidence-led, and traceable. 'FTE increases from 446 in 2026 to 487 by 2033 driven by [specific driver]. This is supported by [specific evidence] and is consistent with [specific assumption], traceable to [specific model output].' Bad: 'Workforce will grow modestly to support capital expansion.'

RULES
Plain language; avoid consulting jargon. Every assertion ties to evidence in the Workforce Model. Honest about assumptions — confidence levels stated explicitly. Australian English. Apply Working Principle 01.2 and 01.4. Pricing/Strategy will peer-review the language for ESC convention before submission.

ITERATION
After producing the draft, identify the three sections most likely to attract ESC challenge and propose a sharper version of each.

ASSUMPTIONS
If the approved strategic pathway, model outputs, or sensitivity analysis is missing or unclear, surface the gap before producing the narrative.

INPUTS:
[Paste the Workforce Model PS28 Outputs sheet, the approved strategic pathway from Phase 4, Strategy 2030 priorities, the customer outcomes framework from Link 3, EA outcomes, the sensitivity analysis.]

PRIMING
This is a synthesis engagement. Output is a thematic report for me to validate with leaders before sharing externally.

CONTEXT
I have completed 12-15 structured leader interviews using the Phase 2 interview guide. The transcripts are pasted in the source block. The synthesis goes to the Steering Group, who will use it to validate the demand and supply driver assumptions.

FRAMEWORK
Approach this as a workforce planning analyst synthesising qualitative interview data into structured findings. Apply Working Principle 01.5. Use the Synthesis format pattern from Section 04 of the IIS.

INSTRUCTIONS
Produce a synthesis report with: (1) Top 5 demand signals named across interviews, with frequency count; (2) Top 5 capability gaps with frequency; (3) Single points of failure flagged (named role, not named person); (4) Tensions or contradictions between leaders; (5) Three verbatim quotes per major theme, attributed by role only (no individual names); (6) Five implications for the workforce model. Aim for 8-12 pages.

EXAMPLES
Good synthesis is honest about what the interviews said, including dissent. Frequency counts are concrete (e.g. '7 of 12 leaders named project management capability'). Verbatim quotes are short and pointed. Tensions are surfaced, not glossed.

RULES
Aggregated and de-identified — no individual names, no identifying details that would point to a specific person. Australian English. Plain language. Flag any claim about specific employees, EA-sensitive material, or identifying detail — strip from the synthesis and route to me directly.

ITERATION
After producing the synthesis, list the three findings most likely to be challenged at the Steering Group and propose a sharper version of each.

ASSUMPTIONS
If transcripts contain identifying information about individuals, redact at synthesis time. Surface what was redacted and why.

INTERVIEW TRANSCRIPTS:
[Paste de-identified transcripts. Use 'Leader A — Engineering' or similar; never paste a person's name.]

PRIMING
This is an analytical engagement producing model inputs. Output is an assumption pack for me to populate the demand sheet of the Workforce Model.

CONTEXT
I am building a 5-year (2028-2033) workforce demand forecast for PS28. The model uses four sub-models per capability: asset-driven, project-driven, service-driven, strategic. Productive hours per FTE = ~1,541/year (78% of 1,976 standard hours). Capital pipeline is ~$950M committed over 5 years. Buniya is the digital transformation programme. Capability segments are defined in Section 06 of the IIS.

FRAMEWORK
Approach this as a workforce demand modelling analyst building defensible assumptions for a regulator-facing submission. Apply Working Principle 01.4 (the model is the system of record). Every assumption must trace to source data or a stated benchmark.

INSTRUCTIONS
For each of 6 capability areas (Engineering & Asset Planning, Field Operations, Digital & Data, Customer Service, Finance & Procurement, Project Delivery), propose demand model inputs across the four sub-models. For each: (1) numerical value or range; (2) rationale citing source data; (3) confidence level (high / medium / low); (4) sensitivity (which assumptions, if wrong, most change the output). For project-driven specifically, show the conversion of $M of capex into FTE demand year-by-year, including phase loading.

EXAMPLES
A good demand assumption is traceable: 'Asset-driven demand for Field Operations year 3 = 138 FTE-equiv, derived from 12,400 km of network × 0.011 maintenance hours/km/year × 1.04 reactive multiplier ÷ 1,541 productive hours per FTE. Confidence: medium. Source: Asset Management System Q1 2026 export.' A bad assumption is vague: 'roughly 140 FTE — based on history'.

RULES
Honest about uncertainty — flag low-confidence assumptions. Don't double-count (a project manager working on capex appears once, not in both project and asset). Use Australian English. Apply Constraint 02.4 — do not include identifying employee details. Show your working.

ITERATION
After producing the assumption pack, identify the three assumptions most sensitive to error and propose a sensitivity range for each.

ASSUMPTIONS
If the capital plan doesn't include phase profiles, the Buniya plan doesn't translate to FTE, or the asset data lacks intensity factors — surface the gap and propose the placeholder approach before producing.

INPUTS:
[Paste the capital program profile by year and discipline, the Buniya digital plan summary, asset condition data, customer growth projections, current maintenance work order patterns, and any prior demand modelling.]

PRIMING
This is an analytical engagement producing supply model inputs. Output is the analysis pack for me to populate the supply sheet.

CONTEXT
Building a 5-year supply forecast — what workforce we will have if no intervention. Equation: Closing supply (year y) = Opening − Attrition − Retirements + Recruitment + Mobility (net). Constraints: Geelong labour market is constrained (12,000+ new jobs competing, 18,300 worker shortfall, 7,700 retiring across the region). BAS attrition patterns differ from Barwon Water core.

FRAMEWORK
Approach this as a workforce supply analyst preparing a supply forecast for a regulator-facing submission. Apply Working Principle 01.4. Every projection must be segmented (capability area minimum), not org-wide averaged.

INSTRUCTIONS
Produce a supply analysis pack with: (1) Attrition rate by capability area — 3-year historical mean and forward projection; (2) Retirement risk profile — % within 5 years, year-by-year retirement projection by capability; (3) Recruitment capacity — realistic hires per year per capability given lead times; (4) Internal mobility patterns — typical role transitions, percentages, lead times; (5) Recommended supply forecast assumptions formatted to populate the model. For each: number/range, source data used, confidence level, risk flags.

EXAMPLES
Good supply analysis is segmented: 'Engineering attrition: 10% pa (3-year history range 8-12%). Confidence: high. Source: HRIS exit data 2023-2025.' Avoid org-wide averages that hide variation between strategic capabilities (low attrition) and digital roles (high attrition).

RULES
Distinguish voluntary attrition from retirement. Aggregated and de-identified only — no individual names, ages, or identifying details. Surface contractor footprint separately (Barwon Water has ~2,000 contractors). Australian English. Flag any segment where the data is too thin for reliable projection.

ITERATION
After producing the analysis, identify the segment with the highest projection uncertainty and propose a data uplift path.

ASSUMPTIONS
If the HRIS data isn't segmented to capability area, propose the mapping approach before producing.

INPUTS:
[Paste de-identified HRIS attrition data, age demographic profile by capability area, recruitment time-to-fill data by role family, internal mobility records, contractor spend by category.]

PRIMING
This is an analytical engagement producing scenario assumptions. Output is a scenario pack for ELT review and model population.

CONTEXT
The Digital GM has framed an AI-enabled future where back-office work reduces and engineering / field / strategic capabilities are protected. I am translating this into three productivity scenarios: Low (5-10% cumulative uplift over 5 years), Medium (15-25%), High (30%+). Apply differentially by capability segment: Strategic (5-20%), Core (8-28%), Requisite (15-50%). S-curve adoption.

FRAMEWORK
Approach this as an AI workforce impact analyst preparing scenarios for executive choice. Apply Working Principle 01.6 (AI accelerates, humans decide). Anchor in industry benchmarks where possible. The High scenario should be a defensible stretch, not the central case.

INSTRUCTIONS
For each of the three scenarios, produce: (1) Productivity uplift % by capability segment, by year (the S-curve); (2) Rationale grounded in named industry benchmarks (Productivity Commission, McKinsey, BCG, OECD where relevant); (3) Underlying assumptions — tooling investment, training, change management, governance, leadership capability uplift; (4) Three risks that could undermine the scenario; (5) Three leading indicators we'd watch to confirm the scenario is playing out.

EXAMPLES
Good scenarios are differentiated and concrete: 'Medium scenario, requisite capabilities (Finance, Procurement, HR), year 5 productivity uplift = 22%. S-curve: 4% / 8% / 14% / 19% / 22%. Source: Productivity Commission 2024 report on automation in service-sector roles, ranges 18-28%.' Avoid uniform uplift across all capabilities — that's not how AI works.

RULES
Honest about uncertainty. Be specific about what 'productivity' means: hours saved / quality improved / roles redesigned / work eliminated. Cite sources. Apply Working Principle 01.6 — frame as decision support, not predictions. ESC will challenge optimistic assumptions; the rationale must hold up.

ITERATION
After producing the scenarios, identify the three assumptions in each scenario most likely to be challenged and propose a sharper supporting argument for each.

ASSUMPTIONS
If the Buniya plan or digital strategy is unclear about specific capabilities or workstreams, surface the gap before producing.

INPUTS:
[Paste the Buniya plan summary, the Digital GM's future-of-work framing, any AI productivity research, the capability segmentation from Phase 3 Step 3.6.]

PRIMING
This is an analytical engagement producing interpretation and risk ratings. Output is the gap interpretation and risk register for Steering Group review.

CONTEXT
The Workforce Model has produced a gap analysis: AI-adjusted demand minus supply by capability and year. I need to interpret what the gaps mean and rate the capability risk using the 11-dimension framework defined in Section 04 of the IIS.

FRAMEWORK
Approach this as a workforce gap and risk analyst preparing material for Steering Group review. Apply Working Principle 01.4. Distinguish quantity gaps, quality gaps, and timing gaps.

INSTRUCTIONS
Produce: (1) For each capability with a meaningful gap, a one-paragraph interpretation — why the gap is opening, when it bites, the consequence of not closing it; (2) A risk register entry per capability rated Low/Medium/High/Critical across all 11 dimensions, with a one-sentence rationale per dimension; (3) Top 3-5 critical capabilities with explanation; (4) Quick wins — capabilities where small interventions yield disproportionate benefit; (5) Capabilities with surplus — redeployment opportunities.

EXAMPLES
Good interpretation is concrete: 'Engineering & Asset Planning gap reaches +14 FTE by 2031, driven by capital program peak in 2030. Bites year 4. Consequence: capital program delays; Buniya integration at risk.' Bad: 'Some pressure on engineering.'

RULES
Distinguish quantity, quality, and timing gaps. Avoid uniform ratings across the 11 dimensions — differentiate. Australian English. Apply Constraint 02.4 (no identifying detail).

ITERATION
After producing the analysis, identify the three risk ratings most likely to be challenged at Steering Group and propose a sharper rationale for each.

ASSUMPTIONS
If the gap analysis output is missing any capability area, or the model assumptions are unclear, surface the gap before producing the interpretation.

INPUTS:
[Paste the gap analysis output from the Workforce Model, the capability segmentation, market intelligence, the Phase 2 interview synthesis themes.]

PRIMING
This is an analytical engagement producing impact assessment. Output is the assessment pack supporting the Strategic Options Paper.

CONTEXT
Barwon Water has 2028 representation targets across 5 dimensions: Aboriginal & Torres Strait Islander 4.0%, All Abilities 17.5%, Neurodivergent 15%, Cultural & Linguistic Diversity 25%, Gender 43% women / 57% men. Each strategic workforce-shape option will have different inclusion impacts.

FRAMEWORK
Approach this as an inclusion and diversity impact analyst. Apply Working Principle 01.6 — model honestly even when the result is uncomfortable. Use aggregated, de-identified data only.

INSTRUCTIONS
For each strategic option: (1) For each of the 5 representation dimensions, model impact: starting %, projected % by 2028 and 2033, gap to target; (2) Identify mechanisms driving the impact (e.g. 'back-office reduction affects representation because women cluster in finance / admin roles'); (3) Where an option worsens any target, propose mitigations: targeted hiring, redeployment / cross-training, role redesign, procurement / partnership levers; (4) Flag any pathway that could put a target out of reach permanently — these need explicit Board acknowledgement; (5) Compare options on inclusion impact.

EXAMPLES
Good impact analysis is honest: 'Option A worsens gender representation by 2031: women drop from 41% to 36% as back-office (where women cluster) reduces. Mitigations: targeted hiring in growth areas (engineering, digital) where representation is currently low.' Avoid hedged language that obscures the trade-off.

RULES
Aggregated and de-identified only — never surface individual employee details. Apply Constraint 02.4. Honest about trade-offs — sometimes the right strategy worsens representation in the short term and the question is how to recover.

ITERATION
After producing the assessment, identify which option's inclusion outcome is most fragile and propose a strengthened mitigation for it.

ASSUMPTIONS
If the demographic data isn't segmented to the capability or function level, propose the segmentation approach before producing the model.

INPUTS:
[Paste current demographics by capability, attrition patterns segmented by demographic, hiring data, the strategic options being modelled, the People Strategy 2028 targets.]

PRIMING
This is a design engagement producing intervention scenarios. Output is the scenario pack for Steering Group costing and review.

CONTEXT
For each capability gap from Phase 3, I am designing intervention scenarios using four levers: Build (internal upskilling, 6-24mo lead time), Buy (external hire, 3-9mo), Borrow (contractor, 2-8 weeks), Automate (AI/digital, months-years). Capability segment implications: Strategic biases to Build/Buy; Core balanced; Requisite biases to Buy/Automate; Non-core biases to Automate or partner.

FRAMEWORK
Approach this as a workforce intervention designer building defensible scenarios for executive decision. Apply Working Principles 01.4 and 01.6. Most gaps need a mix, not a single lever.

INSTRUCTIONS
For each capability gap I provide: (1) Recommend an intervention mix — % by lever, year by year; (2) Cost each intervention using loaded labour cost, recruitment cost, contractor rates, training cost, automation tooling cost; (3) Sequence with explicit lead times; (4) Identify dependencies (e.g. 'Build path requires we Buy a senior leader first to design the curriculum'); (5) Flag risks specific to the chosen mix; (6) Check inclusion impact against representation targets.

EXAMPLES
A good intervention mix balances levers: 'Engineering year-5 gap of 14 FTE: Build 6 FTE (start year 1, 18-month ramp); Buy 5 FTE (years 1-3, ~6mo time-to-fill); Borrow 3 FTE-eq (years 2-4 peak); Automate −2 FTE demand (Buniya year 3+). Total cost ~$3.2M over 5 years. Risks: Build pipeline depends on a senior coach hired in year 1; Borrow rate inflation if tight market.' Bad: 'Hire 14 engineers.'

RULES
Don't recommend single-lever solutions for gaps over 5 FTE. Don't underestimate Build lead times. Watch contractor dependency — too much Borrow exposes to rate inflation and knowledge drain. Apply Constraint 02.5 (Fair Work consultation triggers must be flagged where work design changes).

ITERATION
After producing the scenarios, propose a 'low intervention' alternative for each gap that uses 30% less spend but accepts more risk.

ASSUMPTIONS
If the cost rates or contractor benchmarks aren't current, surface this before producing.

INPUTS:
[Paste the capability gap analysis, segmentation, current footprint, market data, EA cost rates, contractor bill rates, recruitment costs.]