Example output. This is an illustrative AI Readiness Diagnostic for a fictional client (Acme Logistics). Data and findings are sample only.
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Diagnostic Output · Phase 1 of 5D

AI Readiness Diagnostic for Acme Logistics

Where your business stands today across data, technology, process, people and strategy, what needs fixing to lift your readiness, and the highest-value AI moves for the next 12 months.

Client
Acme Logistics Pty Ltd
Engagement
VEC-2026-014
Diagnostic window
21 Apr – 9 May 2026
Engagement Partner
Damian Paterson
Checkpoint
CP3 · Diagnosis Review
Methodology
Vectruva 5D
Report version
v2.1 (draft)
Last updated
12 May 2026, 9:42 AM AEST
58 / 100 OVERALL READINESS
Level 3 of 5 · Defined
Diagnosis complete · CP3 ready

Capable foundations, fragmented execution

Acme has the data and platform fundamentals to start deploying AI in supply-chain operations, but is held back by siloed ownership, inconsistent master data, and no central governance for model risk. Two value plays can be live in 90 days. Six foundation gaps need fixing in parallel to lift your readiness and unlock the larger opportunities.

+11
vs. industry median (47)
3
quick wins identified
$4.2M
12-month opportunity
L1Initial
L2Developing
L3Defined
L4Advanced
L5Optimised
Complete the foundation actions in this report and your readiness moves from 58 / 100 (Level 3) 72 / 100 (Level 4 · Advanced) within 12 months, ahead of the FY28 board cycle.
Reviewed by: Damian Paterson · Engagement Partner Olivia Carter · Engagement Manager Marcus Webb · Independent Reviewer

Readiness by dimension

Data Foundation
L3 · Defined
Single source of truth for orders and inventory; SKU and location masters drift across WMS/TMS.
Technology & Platform
L4 · Advanced
Cloud-native ERP and modern WMS; lakehouse stood up Q4 2025. API coverage strong.
Process Maturity
L3 · Defined
S&OP cadence in place; demand-planning still spreadsheet-driven for >40% of SKUs.
People & Skills
L2 · Developing
No internal data-science capability; 3 analysts comfortable with SQL. Strong change appetite.
Strategy & Sponsorship
L3 · Defined
CEO has named AI a FY27 priority; no funded portfolio yet. CFO supportive, cautious on ROI.
Governance & Risk
L2 · Developing
No model-risk framework, no AI policy. Australian Privacy Act exposure on customer-facing use cases.

Key findings

9 of 14 hypotheses tested · 3 confirmed strengths · 5 gaps · 1 watch
Strength

Platform & data backbone is ready for AI workloads

Lakehouse and event-driven integration mean a forecasting or routing model could be deployed without re-platforming. ~80% of operational data is captured at transaction grain.

Evidence: 14 system integrations reviewed · 4 platform interviews · lakehouse coverage audit (Apr 2026).
High confidence
Gap

Master data drift between WMS and TMS limits automation

SKU dimensions and location IDs diverge across systems, with a ~12% mismatch rate on inbound. Any model touching slotting, transport rating, or yard moves will inherit this noise.

Evidence: 30-day SKU/location reconciliation sample · Ops Manager interview · TMS exception logs.
High confidence
Gap

No AI governance; model risk is implicitly owned by IT

No model inventory, no approval workflow, no monitoring. Acceptable for pilots, but a blocker for customer-facing use cases and any model touching pricing or credit.

Evidence: Policy review · CIO and Risk Manager interviews · benchmark against ASX-100 peers.
High confidence
Strength

Strong executive sponsorship and change appetite

CEO and COO have named AI a FY27 board priority. 78% of frontline supervisors surveyed said they would use AI-assisted recommendations "if it saved them admin time".

Evidence: Executive interviews · supervisor survey (n=64, 91% response) · board pack review.
Medium confidence (survey self-selection bias)
Gap

Forecasting accuracy is 18 pts below industry median

Weekly SKU-level MAPE sits at ~42% (industry median ~24%). Spreadsheet-driven planning for the tail of SKUs costs an estimated ~$1.6M/yr in excess inventory and expedited freight.

Evidence: 18-month demand vs. actuals reconciliation · planning team time study · inventory carry-cost model.
High confidence
Watch

Vendor lock-in risk on planning suite renewal (Jul 2027)

Current planning vendor is bundling an AI add-on into a 3-year renewal. Switching cost is real, but accepting it now locks Acme into closed forecasting models. A material decision worth pausing before signing.

Evidence: Vendor proposal review · contract terms analysis · 2 reference calls with peer customers.
Medium confidence (vendor roadmap unverified)

FixFoundation actions to lift readiness

Commission with your teams · moves you from Level 3 to Level 4 by Q1 FY28
# Foundation action Dimension lifted Maturity lift Owner Time to fix Why it matters
F1 Reconcile SKU and location master data across WMS and TMS Single source of truth, automated drift detection, exception workflow Data Foundation L3 L4 CIO + Head of Operations 6 months Blocks Opp #02
F2 Stand up AI governance (model inventory, approval workflow, monitoring) AI policy v1, model risk framework, Privacy Act review for customer-facing use Governance & Risk L2 L4 Chief Risk Officer + CIO 3 months Blocks Opp #04
F3 Build internal data-science capability 2 senior hires (lead + MLE), uplift programme for 6 analysts, vendor partnership for surge People & Skills L2 L3 Chief People Officer + CEO 12 months Strategic
F4 Establish AI portfolio governance and funding envelope Single AI portfolio, quarterly board reporting, funded FY27 budget line Strategy & Sponsorship L3 L4 CFO + CEO 90 days Quick win
F5 Mature demand-planning process beyond spreadsheets Replace spreadsheet planning for the SKU tail; embed forecast review into S&OP cadence Process Maturity L3 L4 COO + Head of Planning 9 months Pairs with Opp #01
F6 Pause planning suite renewal pending forecast pilot Avoid 3-year lock-in to closed forecasting model; reopen tender Q1 FY28 Technology & Platform L4 (retain) CIO + CFO Decision by Jul 2026 Time-bound

BuildValue opportunities to deploy AI

Sized using Vectruva opportunity framework · 12-month horizon · $3.8M to $4.9M range
# Initiative Value (12m) Effort Time-to-value Confidence Status
01 AI-assisted demand forecasting (top 200 SKUs) Probabilistic forecasts replacing spreadsheet planning for tail SKUs $1.6M – $2.1M Medium 90 days High Quick win
02 Dynamic slotting & pick-path optimisation (DC1) Daily re-slotting based on velocity + co-pick correlation $0.9M – $1.2M Medium 120 days Medium Quick win
03 Carrier rating & mode-shift recommender ML model recommending optimal mode + carrier per lane per day $0.7M – $1.0M Medium 6 months Medium Wave 2
04 Returns triage assistant (LLM-based) Customer-facing returns flow with auto-categorisation and refund routing $0.4M – $0.6M Low 4 months Low (needs governance work first) Blocked · gov
05 Network design refresh with AI-assisted scenarios Digital-twin model for east-coast network, preparing for FY28 lease decisions $2.0M+ (one-off) High 9 months Medium Wave 2

Recommended roadmap · Vectruva 5D

Aligned to Acme FY27 planning cycle
D1 · Discovery

Foundations

Complete · May 2026
  • Readiness diagnostic (this report)
  • Hypothesis library & data audit
  • Executive alignment session
D2 · Diagnose

Deep-dive on top 3

Jun – Jul 2026
  • Forecast model bake-off
  • Master-data remediation plan
  • AI governance gap close
D3 · Design

Pilot blueprints

Aug 2026
  • Demand forecasting pilot scope
  • DC1 slotting pilot scope
  • Model risk & AI policy v1
D4 · Deploy

Pilots in production

Sep – Dec 2026
  • Forecast pilot live (top 200 SKUs)
  • Slotting pilot live (DC1)
  • Governance framework adopted
D5 · Embed

Scale & transfer

Q1 FY27
  • Roll out to DC2 / DC3
  • Internal capability uplift
  • Wave 2 portfolio kick-off
!

We need your input on 2 figures before finalising this report

These assumptions shape the value estimates throughout the diagnostic. Please confirm or correct each one so we can lock the numbers and move to the roadmap.

Generated by: Data Profiler · Susan Wang Hypothesis Team · Mei Chen, James Okafor, Priya Sharma Financial Analyst · Anika Patel Independent Reviewer · Marcus Webb Engagement Manager · Olivia Carter