Critical Path — 90-day execution
The journey from data-blind Monday to auto-Monday — owner-named, gate-named, kill-switch-named
Every week the Monday number doesn't land costs you. The path below changes that — but only if you act on it this week.
$6K–$20K
per week
cost of staying still
7–13 hrs
per week
lost to reconciliation
16 weeks
from Stage 1 start
to first auto-Monday
5 Decisions — Start Here
Schedule the Week 8 kill-switch review meeting
Bounded bet requires a named exit date. Without the meeting on the calendar, Stage 1 cannot start.
Owner: CEO / Managing Director — schedule it on your calendar before you close this tab
Name the IT contact accountable for confirming POS API access
Highest-uncertainty INFERRED input — resolve fastest to remove Stage 1 budget risk
Owner: TBD — name someone now
Approve Stage 1 budget of NZD $54,500 with named kill switches
Starts the predict-before-you-invest discipline immediately
Owner: CFO + CEO sign-off
Name 5 Documentation Sprint owners (supplier history, pricing logic, margin floor, catalogue selection, store-mix)
Institutional knowledge encoding is on the Stage 3 critical path — start naming owners in Stage 1
Owner: TBD — one owner per knowledge domain
Pick BI substrate from shortlist (Metabase / Hex / Power BI Premium)
Vendor commitment is the largest single Year 1 cost line
Owner: CTO / Tech lead
Why this page is designed this way
This page is a behaviour-change instrument, not a report. The pieces below are deliberate — and naming them is how trust gets built. You see the seams; nothing is hidden.
- Cost hook (top, amber): loss-aversion + fear-sells. Losses feel 2× as heavy as equivalent gains. Loss Aversion ↗ · Fear Sells ↗
- TODAY decision (§1, brand colour): hyperbolic discounting. People discount the future steeply — a deadline today outweighs a benefit next quarter. The Week-8 review goes on the calendar before this tab closes. Hyperbolic Discounting ↗
- Locked stages (§2): Zeigarnik effect. Open loops stay in working memory until closed. Stage 2 and Stage 3 are visibly locked so the mind cannot file them as “someday.” Zeigarnik Effect ↗ · IKEA effect — you are co-building the path, not receiving a deck. IKEA Effect ↗
- Positioned vs Exposed (§4): loss-aversion re-applied to the horizon. The five predictions are claims you do not control — but your readiness for them is. Each gets a binary state so the gap is visible.
Disclosing the persuasion is itself the differentiator. Manipulation hides the mechanism. Coordination names it.
The Journey: 4-Stage Path
Stage 0NOW
0–0.5 mo
$0
4 GO conditions → Stage 1 launch
Any condition unmet → STOP
Stage 1
1–4 mo
NZD $108K
UC1 Unified Intelligence + UC2 saleId ROI
Monday misses W8 / API blocked D30 / over $65K W12
Stage 2
4–9 mo
NZD $187K
UC4 Stock-Position + UC5 FX Tracker
Unlocks when: Stage 1 kill switch not fired — Monday number lands on time for 4 consecutive weeks (W8 + W12 checkpoints)
Stage 3
8–12 mo
NZD $333K
UC3 AI-Assisted Buyer Brief (pilot)
Unlocks when: Stage 2 complete + Documentation Sprint complete (all 5 knowledge domains owned and documented)
90-Day Execution Timeline
GO conditions
Day 14
GO / NO-GO
4 conditions checked
Day 35
First auto Monday
Dual-run with manual
Day 56 (W8)
Kill check #1
4 consecutive on-time reports OR stop
Day 112 (W16)
Prediction confirmed
Monday number auto-delivered
What's Coming — Positioned or Exposed?
Stage 1 complete = positioned for all 5. Without Stage 1: exposed on all 5. The predictions are not yours to control — your readiness is.
#1Discount tier of NZ retail will continue expanding share at 1–3 percentage points/year through 2027
⚠ ExposedSource: Stats NZ Retail Trade quarterly + The Warehouse Group NZX disclosures
If you wait
Your customer base grows while your decisions stay data-blind — competitors with BI substrate take share
If Stage 1 ships
Stage 1 BI substrate live — you see category mix shift in real time and act on it
#2NZ-listed retail will increase AI/data investment 30–50% over 24 months
⚠ ExposedSource: Industry pattern in NZ + AU retail transformation programmes 2024–2026
If you wait
Analytical gap between mid-market and large-format widens — talent and capital favour the prepared
If Stage 1 ships
Stage 1 demonstrates data-driven discipline — Stage 2/3 attract better hires and partners
#3FX volatility against major import currencies will remain in elevated band for 12–18 months
⚠ ExposedSource: RBNZ monetary policy outlook + global currency volatility post-2024
If you wait
Margin erosion goes undetected until quarterly close — too late to hedge
If Stage 1 ships
Stage 2 FX tracker (UC5) — daily margin exposure visible, hedging decisions become routine
#4AI-assisted retail buying (RAG over supplier history) will become standard in AU mid-market retail within 24 months
⚠ ExposedSource: Wesfarmers, Bunnings, Coles published data investments 2024–2026
If you wait
Buyer briefs remain individual judgement — institutional knowledge walks out the door on resignation
If Stage 1 ships
Stage 3 AI buyer brief pilot — supplier history queryable, buyer onboarding compresses 6mo → 6wk
#5NZ Privacy Act enforcement on consumer data will tighten progressively
⚠ ExposedSource: Privacy Commissioner NZ public guidance 2023–2025
If you wait
Customer data sprawl across spreadsheets and tools — audit-grade response impossible under pressure
If Stage 1 ships
Stage 1 unified governed substrate — compliance becomes a query, not a fire-drill
Your Next Step
Ready to commit to Stage 1? Two closing instruments:
The decision summary has the 4 GO conditions to check by Day 14.
The one-page plan has the commercial model, fees, and the kill switches in one screen.
Put this to work
Pressure-test this plan with your own AI assistant
For the Owner / IT LeadCopy this prompt. Paste into Claude, ChatGPT, or any AI assistant. The page context is already loaded — send it and get analysis tailored to your role.
I need to pressure-test a 90-day technology implementation plan for a 15-store NZ discount retail chain. THE 90-DAY PLAN: Weeks 1-4: Documentation Sprint — 5 named knowledge-domain owners (supplier history, pricing logic, margin floor, catalogue selection, store-mix). Weeks 1-5: BI Substrate data ingest pipeline (POS + ERP + e-commerce + catalogue → BigQuery). Weeks 3-8: UC1 Monday report automation — currently 7-13 hours manual, target ≤15 min auto, 3 consecutive weeks. Weeks 4-12: UC2 Catalogue saleId ROI Dashboard — currently zero attribution data on which promotions drive revenue. KILL GATES: Day 14: GO/NO-GO on 4 conditions (highest-risk: POS API access in writing). Week 5: First automated Monday report, dual-run with manual reconciliation. Week 8: Kill check — if Monday number has not landed 4 consecutive times, stop at NZD $30-40K sunk cost. Week 12: Budget gate — stop if spend exceeds NZD $65K. Week 16: Prediction confirmed when 4 consecutive Mondays land automatically by 8am. HIGHEST UNCERTAINTY: POS system API access is INFERRED, not VERIFIED. This is the single biggest schedule risk. WEEKLY COST OF STAYING STILL: NZD $6K-$20K per week (CFO + Finance Manager time + 2-4 day decision lag on every weekly call). What should happen in the first 48 hours of this project to de-risk the POS API access question? What is the most common failure mode for a 90-day BI implementation, and how does the kill gate structure above address or fail to address it?