Summary of "Be Audit Ready Before EOFY: Using AI to Eliminate Compliance Risk | ShiftCare & aify Australia"
Core theme
Use AI connected to ShiftCare via MCP to reduce NDIS compliance risk and become “audit ready” before EOFY by generating:
- Risk findings
- Evidence packs
- Executive-ready reports
…using your operational data from ShiftCare plus policy/regulatory documents (e.g., SharePoint).
Frameworks / playbooks mentioned or implied
NDIS audit readiness playbook (agent-driven)
- Determine audit type and scope/modules (e.g., “Core Module 4”).
- Pull relevant artifacts from:
- ShiftCare (credential/qualification registers, mandatory training currency, rostered shifts, etc.)
- SharePoint (policies, procedures, registers, participant folders, draft reports)
- Run a gap analysis against current NDIS requirements.
- Produce outputs such as:
- Red/Amber/Green (RAG) audit-risk tables
- To-do lists and closure evidence inventories
- Auditor-readable evidence packs
Gap analysis approach (explicit)
Map:
- What the requirements are → Where the evidence lives → What’s missing / insufficiently closed
Continuous readiness (operational process)
- Suggested cadence: run an audit readiness report weekly (example given: Monday mornings) to catch issues as they emerge.
Key processes & operational recommendations (actionable)
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Connect AI to ShiftCare using MCP (read-heavy at present)
- Configure Claude by adding ShiftCare MCP as a “custom connector” using an MCP URL from ShiftCare help articles.
- Use exposed MCP endpoints to understand what the AI can access (updated frequently).
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Data residency & safety controls (compliance-critical)
- Do not use free LLMs for private/NDIS data.
- Use a paid business/enterprise plan with Australia data residency controls.
- If using tools like Claude/ChatGPT, ensure settings lock residency to Australia to avoid compliance risk.
-
Human-in-the-loop before sending to auditors
- Even when AI drafts evidence, require human review; “do not just hit send.”
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Automate evidence creation, but don’t outsource governance
- Use AI to generate drafts/evidence packs and accelerate workflows.
- Maintain operational oversight to prevent incorrect outputs.
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Improve outcomes by improving source data quality
- Agent outputs are heavily reliant on the quality/consistency of ShiftCare data.
- If ShiftCare implementation is rubbish or inconsistent, AI findings may degrade.
Demo-driven capabilities (what the AI does)
1) Claude + ShiftCare MCP: compliance risk scanning (RAG)
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Prompt example:
“If I had an NDIS audit tomorrow, what would I fail on?”
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Output style:
- RAG table (red/amber/green) sorted by audit risk
- Actionable tasks (framed as “what to do by 8:00 a.m. tomorrow” in the demo)
-
Example risk signals:
- Expired/soon-expiring worker credentials/qualifications
- Missing police checks
- First aid expiry within days
-
Demo scale mentioned:
- 483 shifts, 64 staff, across four pages
2) Evidence pack generation (auditor-readable documents)
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Prompt example: Produce an audit evidence pack for NDIS Practice Standards (Core Module 4) over last 6 months
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Evidence pack includes (example scope):
- Staff qualification register with expiry dates
- Mandatory training currency
- Shifts where a non-compliant worker was rostered
-
Delivery format:
- Structured, formatted output “ready to hand the auditor”
- Clickable breakdown of what’s inside
3) Co-pilot / Teams agents: proactive audit readiness + gap analysis across ShiftCare + SharePoint
-
“Audit readiness report” copilot:
- Asks for audit type, modules, and date
- Performs:
- Baseline vs current requirements
- Evidence inventory from ShiftCare
- Evidence inventory from SharePoint
- Gap analysis and to-do list
-
Example executive warning shown:
- 8 weeks to audit
- Three major non-compliances from a January certification audit still open or insufficiently closed
- “Auditor will walk in expecting closure evidence on all three.”
4) “Operational patterns to fix” (near-future findings)
- Prompt example: identify 3 operational patterns not yet compliance-failing but likely to become findings within 12 months
- Example patterns produced:
- Qualification governance lacks a renewal loop
- Metric example: 24 of 31 workers aren’t qualified
- Fund utilization issues
- Metric example: “19 days to expiry” (overdrawn/expiry signal)
- Complex needs participants supported by dangerously thin teams
- Qualification governance lacks a renewal loop
(Presented as steering/operations metrics, not just compliance checklists.)
5) Reporting assistant (two-way conversation to fix rejected payment reports)
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Case example:
- Participant support reporting was rejected four times
- AI agent used dialogue to find missing context (e.g., “Why was the mother not there?”)
- Outcome: report accepted after four rejections, reframed based on participation/cohabitation complexity and reporting requirements
-
Another “reporting agent” concept:
- Ensures reporting meets NDIA style/format expectations
- Avoids vague statements like “had a nice day”
Concrete metrics / KPIs referenced (operational indicators)
Operational scale (demo and examples)
- 483 shifts
- 64 staff
- 1388 shifts (example for a 6-month window evidence pack generation)
- 279 shifts (example for last 90 days, single-client evidence scope)
Compliance / readiness
- 24 of 31 workers not qualified (qualification governance gap)
- First aid ticket expiry in 6 days (credential/timeline risk)
- 19 days to expiry / fund utilization overdrawn signal
Timing / audit timeline framing
- Example evidence deadline: “8 weeks” to audit
- Demo date references included:
- “two weeks to two months from today”
- Illustrative audit thumbs-down date: 20 July 2026
- Another scenario due February 25, but overdue within the demo narrative
Marketing / product positioning insights (business execution)
- ShiftCare positioning: best-in-class for capturing NDIS operational/compliance data in a “nicely structured” way for compliance reporting.
- AI platform positioning: use AI that fits your environment and supports MCP; prioritize security/compliance controls.
- Key differentiator: connect AI to structured enterprise systems (ShiftCare + SharePoint), rather than feeding large documents into an LLM (avoids “too many blanks” where AI makes its own decisions).
- Performance claim (time saved):
- Locking up evidence typically takes ~1 week to 1.5 months, depending on organization size.
- AI prompts reduce this substantially (demo framed as minutes / 1–10 minutes depending on data volume).
High-level notes on AI tools & ecosystem (execution-focused)
- MCP requirement: any AI platform can be used if it supports an MCP server connector.
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Copilot specifics:
- Standard Copilot for Microsoft (~$20–$30/user/month mentioned) does not enable MCP for ShiftCare.
- Requires Copilot Studio (different subscription/licensing).
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Write capability (read-only vs read/write):
- MCP to ShiftCare is described as read-only currently.
- ShiftCare is working toward write permissions (e.g., updating training/certification outcomes, uploading data back).
- The current state is read-heavy; write/workflows are still a work-in-progress, with expanding support for form responses and staff completion tracking.
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Combining multiple AI tools:
- Possible to connect more than one AI platform.
- Prompts/workflows don’t necessarily have to be rewritten entirely each time (demo indicates reusability).
Other business examples mentioned (non-audit, compliance adjacent)
-
Care Signals product (ShiftCare)
- Real-time triage of care notes:
- Flag missed incidents (create incident tickets)
- Review notes for completeness/auditability
- Phase 2 concept: “conversational intelligence” to prompt workers to fill gaps (without replacing their authorship)
- Mentions note clean-up to improve professional quality
- Real-time triage of care notes:
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Staff shift notes
- AI can help generate better shift notes via mobile (noted as possible; mentioned as part of a private demo)
Presenters / sources
- Paul — host/facilitator (introduced agenda, handled questions, ran polls)
- Dave (aify Australia) — demo and AI/compliance expertise; ShiftCare MCP connection, agents, audit readiness/coplilot workflows
- Matt (ShiftCare) — CEO; attended and referenced for organizational context
Category
Business
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