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

§1

5 Decisions — Start Here

Today

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

Day 3

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

Day 7

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

Day 14

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

Day 14

Pick BI substrate from shortlist (Metabase / Hex / Power BI Premium)

Vendor commitment is the largest single Year 1 cost line

Owner: CTO / Tech lead

§1A

Adaptive Rule: Bridge Confidence, Not Fantasy

Tech strategy

Bridge confidence comes from strategy good enough to explain the first data route from POS to loyalty/KYC to BI, the first proof, the first kill switch, and the first adaptation.

SME leap

The owner cannot become a BI architect before deciding. The bet is responsible when AI helps level the intelligence gap, the downside is capped, and the proof arrives before belief runs out.

Operating rule

Adapt quickly, preserve flow, and revise the destination if reality proves the old target wrong.

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.

§2

The Journey: 4-Stage Path

Stage 0NOW

0–0.5 mo

4 GO conditions + atoms-to-bits trust test → Stage 1 launch

Any condition unmet or still vague → STOP or shrink the bet

Stage 1

1–4 mo

NZD $108K

UC1 Unified Intelligence + UC2 saleId ROI

Monday misses W8 / API blocked D30 / vendor fit fails / over $65K W12

LOCKED

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 and weekly adaptation notes show the flow is stable

LOCKED

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) + buyer-brief baseline ready

§3

90-Day Execution Timeline

W10

W12

W14

W16

Documentation Sprint

Institutional knowledge

BI Substrate

Data ingest pipeline

Monday report loop

UC1 Business Analysis

Weekly report automation

Dual-run → auto

UC2 saleId ROI

Attribution pipeline

Catalogue optimise

Gates

◆GO / NO-GO

●First auto Monday

◆Kill check #1

▲Budget gate

●Prediction confirmed

◆Decision◆Kill check▲Budget gate●Milestone

GO conditions

POS API access confirmed in writing (IT lead named, Day 3)

Stage 1 budget of NZD $54,500 approved with named kill switches (CFO, Day 7)

BI substrate vendor selected from shortlist: Metabase / Hex / Power BI (CFO, Day 14)

5 Documentation Sprint owners named — supplier history, pricing logic, margin floor, catalogue selection, store-mix (Owner, Day 14)

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

§4

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

⚠ Exposed

Source: 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

⚠ Exposed

Source: 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

⚠ Exposed

Source: 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

⚠ Exposed

Source: 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

⚠ Exposed

Source: 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

§5

Your Next Step

Ready to commit to Stage 1? Two closing instruments:

← Decision Summary One-Page Plan →

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 Lead

Copy 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.

KNOWN UNKNOWNS TO RESOLVE EARLY:
- POS/API or scheduled-export access.
- BI vendor fit for Crackerjack data, licensing, and support burden.
- saleId attribution join quality across campaign, POS, item, store, and margin data.
- Loyalty/KYC join quality: can customer response be known responsibly enough to improve incentives?

UNKNOWN UNKNOWNS TO HANDLE WEEKLY:
Every week must ask what reality changed, whether the route should change, and whether the destination itself needs revision. The rule is adapt quickly, preserve flow, and stop funding scope that no longer serves the destination.

BRIDGE CONFIDENCE:
Good tech strategy is what makes the bridge credible, but most SMEs cannot validate every technical premise before starting. Stage 1 is therefore a bounded leap of faith: prove enough strategy fast, cap the downside, and adapt before drift becomes spend. The strategic stack runs from atoms to bits: store visit → POS transaction → loyalty identity → BI substrate → AI interpretation → better customer incentive.

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 test whether the tech strategy is good enough for a bounded SME leap of faith? How should POS, loyalty/KYC, and BI vendor fit be tested before Week 2? How should the weekly adaptation review catch weak strategy before the Week 8 kill gate?

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