Feature on pages 10-11
Published perspective on why AI trust requires evidence, controls and approval artefacts. The feature starts on page 10.
Featured in AIIA Connector, pages 10-11
AI approval needs more than security.
A secure AI system can still stall when the evidence trail is missing.
Security protects the system. Evidence determines whether AI can be trusted, procured and scaled.
AI Strategy Co. helps approval-sensitive organisations find the missing evidence, controls and decision artefacts before AI pilots move from demo to approval.
Publication proof
Trust is the next capability constraint for AI.
AI Strategy Co. was featured in the June 2026 AIIA Connector, pages 10-11, on why approval-sensitive AI needs evidence trails before systems can be trusted, procured and scaled.
- purpose, ownership and operating limits
- data use, sensitivity and controls
- vendor assurance and DDQ responses
- monitoring, escalation and decision artefacts
Navigation note: opens the June 2026 AIIA Connector. The AI Strategy Co. feature starts on page 10 and continues across pages 10-11.
AIIA membership
Part of Australia's digital economy and technology industry conversation.
PAN participation
Participation in policy advisory network discussions across digital economy and state government themes.
The approval gap
Technically ready can still be approval-blocked.
Many AI pilots stall because the approval evidence is fragmented across security, procurement, legal, privacy, risk, data, vendors and business owners.
Technically ready
The system can look ready
- Demo works in a controlled setting
- Security review has started or completed
- Vendor claims are documented
- Business value appears plausible
- Pilot sponsor wants to proceed
Approval-ready
The decision trail is complete
- Use case, purpose and owners are documented
- Controls and assurance are mapped
- Evidence is available for review
- Monitoring and escalation are clear
- Decision artefacts are ready
What approval teams need
The evidence trail has to connect across teams.
The approval gap is usually a disconnected set of answers. The practical requirement is a coherent evidence trail that risk, procurement, legal, privacy, security, data, vendors and business owners can all work from.
01Purpose and business value
02Ownership and decision rights
03Data sources and sensitivity
04Controls and residual risks
05Vendor and DDQ responses
06Monitoring and escalation triggers
07Approval note or decision memo
Who this is for
Built for approval-sensitive AI decisions.
Banks, insurers and public sector
For teams trying to move AI from pilot to production while satisfying risk, audit, procurement and executive review.
Vendors selling into regulated buyers
For vendors that need stronger DDQs, evidence packs and assurance narratives to support enterprise sales cycles.
Technology and delivery partners
For partners whose AI projects may be slowed by governance, procurement, risk or audit evidence questions.
30-minute review
Book an AI Evidence Gap Review.
In 30 minutes, we identify whether your AI pilot, vendor response or approval pathway has enough evidence to move forward.
What exists
We identify the evidence, artefacts and controls already available.
What is missing
We locate gaps likely to slow risk, procurement or executive approval.
Who may block
We clarify which stakeholders need confidence before the decision moves.
What to create first
We recommend the smallest useful artefact that can move the approval forward.
Scope boundary: this is advisory and evidence-focused. Legal, security, privacy and technical implementation remain with the relevant accountable teams.
If the gap is material
Move into a focused mAIGO sprint.
The follow-on pathway is a fixed-scope mAIGO Evidence Pack Sprint for 1–3 AI use cases, vendor responses or approval-sensitive pilots.
AI use-case fact sheet
Risk and materiality summary
Evidence gap register
Control themes and ownership map
DDQ or assurance response structure
Approval note or decision artefact outline
About the advisory model
AI Strategy Co. leads the decision path. mAIGO handles the governance lane.
This page is for approval-sensitive AI decisions where evidence, controls, DDQs and decision artefacts matter.
Independent AI decision support
AI Strategy Co. helps enterprise buyers decide what to adopt, what to avoid and what to fix first across AI options, adoption readiness and pilot recovery.
Governance advisory for approval-ready AI
mAIGO is the advisory governance lane for evidence packs, control mappings, DDQs, approval artefacts and regulator-aligned assurance support.
FAQ
Common questions before booking.
What should I bring?
Bring a short description of the AI use case, vendor or pilot, plus the main approval concern: risk, procurement, privacy, legal, audit, executive sign-off or customer assurance.
Who should attend?
The best fit is the AI sponsor, product or project owner, risk/governance lead, procurement owner, vendor lead or partner manager responsible for moving the decision forward.
Is this a legal or security review?
The review focuses on governance evidence, approval readiness and decision artefacts. Legal, security, privacy and technical control testing remain with the relevant accountable teams.
What happens after the review?
When the evidence gap is material, the next step is a fixed-scope mAIGO sprint to produce the evidence pack, DDQ structure or approval artefact needed for the decision path.
Next step
Find the missing evidence before approval stalls.
Use the AI Evidence Gap Review to identify what is missing across evidence, controls, DDQs, ownership and decision artefacts.