FLEXIBLE FIT Cross-industry fit · adaptable to unique operating models

Other / Not listed

Every organisation has critical decisions, workflows and data relationships that do not fit a standard sector label.

USE CASE

One trusted business view

AUD 40k–250k p.a.

TANGIBLE BENEFIT

Less searching and reconciliation, with one reliable view of the people, products, assets or services that drive the organisation.

VALUE BASIS

Recovered operational capacity plus fewer errors caused by duplicate or conflicting critical records.

IF NOTHING CHANGES

Teams keep rebuilding the same view in spreadsheets while decisions rely on stale, duplicated or disconnected facts.

ILLUSTRATIVE BUSINESS CASE15 people × 4 hours saved monthly × AUD 70/hour = AUD 50.4k of annual capacity before avoided errors.

AI READINESS

From trusted evidence to a controlled capability.

The organisation has named the customers, suppliers, people, assets, services and transactions that matter to its operation. Stable identifiers, relationship rules, ownership and approved sources are defined, while duplicates, missing links and conflicting facts are measured rather than hidden in manual reconciliation.

WHAT AI CAN DO

AI can assemble an authorised business briefing, identify conflicting or missing facts and recommend which records or relationships need attention first. It can help people navigate fragmented information without merging records, changing source systems or making accountable decisions on its own.

01Starting point

A team searches its CRM, finance platform, operational system and spreadsheets to understand one customer, supplier or asset, then manually resolves several versions of the same fact.

02AI in action

A permission-aware assistant links the relevant records, produces a source-referenced view and flags an identifier conflict, a missing relationship and an outdated status for review.

03Measured outcome

The accountable owner resolves the exceptions once and the team tracks preparation time, duplicate records, unresolved conflicts and summaries accepted without correction.

HUMAN CONTROL

Named business owners approve identity resolution, relationship changes and external actions; AI can surface evidence and options but cannot silently merge records or overwrite authoritative facts.

PROOF MEASURES

Time to assemble a trusted business view
Duplicate and conflicting critical records
Source-linked summaries accepted without correction

Assess this use case ↗

USE CASE

Operational exception control

AUD 50k–300k p.a.

TANGIBLE BENEFIT

Earlier detection and faster resolution of material exceptions, with less rework, leakage and operational delay.

VALUE BASIS

Exception handling time plus avoidable transaction errors, repeat work and delayed outcomes.

IF NOTHING CHANGES

Problems remain hidden until a downstream failure or complaint, and every investigation starts from fragmented evidence.

ILLUSTRATIVE BUSINESS CASE2,000 exceptions × 30 minutes avoidable handling × AUD 70/hour = AUD 70k of annual capacity.

AI READINESS

From trusted evidence to a controlled capability.

Operational events, expected results and reference data can be joined at the level where an exception occurs. Materiality thresholds, reason codes, workflow owners and resolution outcomes are captured consistently, providing enough labelled history to distinguish meaningful exceptions from normal variation.

WHAT AI CAN DO

AI can detect unusual or conflicting records, group recurring causes, estimate materiality and prepare an evidence-backed priority queue. It can recommend the next investigation step while people approve corrections, financial actions, customer contact and changes to operating rules.

01Starting point

Teams discover missing transactions, duplicate work or conflicting statuses after a customer complaint, delayed close or downstream failure, then investigate each case from scratch.

02AI in action

An exception assistant compares the event with approved rules and historical outcomes, ranks the material cases and explains the source fields and pattern behind each alert.

03Measured outcome

Owners resolve the highest-value cases first. The pilot measures detection time, avoidable rework, value recovered, repeat causes and false-positive alerts.

HUMAN CONTROL

Accountable operators approve every correction and external action; AI cannot change a transaction, invent evidence or alter a materiality rule without controlled approval.

PROOF MEASURES

Time from exception to resolution
Avoidable rework and value leakage
Repeat exceptions and false-positive rate

Assess this use case ↗

USE CASE

Responsible AI opportunity

AUD 50k–400k opportunity value protected

TANGIBLE BENEFIT

A bounded AI pilot tied to a real decision, with clearer evidence, human control and less avoidable investment rework.

VALUE BASIS

Decision or task capacity improved plus the cost of poorly scoped AI work avoided.

IF NOTHING CHANGES

Teams buy tools or build demonstrations without a measurable use case, trustworthy data or controls for real operation.

ILLUSTRATIVE BUSINESS CASE20 staff × 20 minutes saved per day × 220 days × AUD 70/hour = approximately AUD 103k of annual capacity.

AI READINESS

From trusted evidence to a controlled capability.

One valuable decision or task has a named owner, current baseline, permitted data, measurable target and explicit stop criteria. The organisation has defined the bounded AI role, evaluation examples, human review points, prohibited uses and monitoring needed to prove benefit without creating unacceptable operational, privacy or human risk.

WHAT AI CAN DO

AI can support the selected task through source-grounded summarisation, classification, prediction or recommendation within a clearly bounded workflow. It can expose confidence and evidence, while accountable people retain decisions and the organisation compares outcomes with the pre-AI baseline.

01Starting point

The organisation has several AI ideas but no agreed first decision, value baseline or evidence that its data can support a safe and useful pilot.

02AI in action

A readiness assessment scores the candidate decisions, selects one bounded workflow and tests an assistant against approved examples with source traceability, quality thresholds and human review.

03Measured outcome

The owner proceeds, redesigns or stops the pilot using measured time, quality, risk and adoption results rather than model activity or demonstration appeal.

HUMAN CONTROL

The business owner approves scope, decisions and deployment; sensitive data, high-impact decisions and external outputs remain subject to purpose limits, authorised review and a practical stop mechanism.

PROOF MEASURES

Measured improvement against the pre-AI baseline
Output acceptance and correction rate
Risk, escalation and stop-criterion breaches

Assess this use case ↗

OTHER / NOT LISTED

Choose one use case. Test its readiness.

Start assessment ↗