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).

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Frameworks / playbooks mentioned or implied

NDIS audit readiness playbook (agent-driven)

1. Determine audit type and scope/modules (e.g., “Core Module 4”).
2. Pull relevant artifacts from:
   • ShiftCare (credential/qualification registers, mandatory training currency, rostered shifts, etc.)
   • SharePoint (policies, procedures, registers, participant folders, draft reports)
3. Run a gap analysis against current NDIS requirements.
4. 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.

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Key processes & operational recommendations (actionable)

• 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).

• 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.”

• 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.

• 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.

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Demo-driven capabilities (what the AI does)

1) Claude + ShiftCare MCP: compliance risk scanning (RAG)

• Prompt example:

  “If I had an NDIS audit tomorrow, what would I fail on?”

• 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)

• Prompt example: Produce an audit evidence pack for NDIS Practice Standards (Core Module 4) over last 6 months

• 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

(Presented as steering/operations metrics, not just compliance checklists.)

5) Reporting assistant (two-way conversation to fix rejected payment reports)

• 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”

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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

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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).

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High-level notes on AI tools & ecosystem (execution-focused)

• MCP requirement: any AI platform can be used if it supports an MCP server connector.

• Copilot specifics:

  • Standard Copilot for Microsoft (~$20–$30/user/month mentioned) does not enable MCP for ShiftCare.
  • Requires Copilot Studio (different subscription/licensing).

• 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.

• 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).

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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

• Staff shift notes

  • AI can help generate better shift notes via mobile (noted as possible; mentioned as part of a private demo)

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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