What AI governance looks like in practice.

Problem

Review cycles are slow and documentation inconsistent.

Solution

AI-assisted review with human approval gates at every stage.

Problem

Export-controlled data co-mingled with unclassified material.

Solution

Automated classification with human verification before any AI model access.

Problem

Technicians using unsanctioned AI tools to look up procedures.

Solution

Governed AI assistant with audit trail, approved data sources, and escalation path.

Problem

Detecting defects manually is slow and inconsistently documented.

Solution

AI-assisted inspection with human sign-off and full traceability to source data.

Problem

Procurement teams using AI tools on supplier data without governance.

Solution

Scoped AI access to approved supplier datasets, with approval workflow and export controls.

Problem

Scheduling decisions made by AI without audit trail.

Solution

AI recommendations with human approval and decision log captured for review.

Problem

Clinicians using general-purpose AI tools on patient data.

Solution

Approved, scoped AI access with data never leaving the clinical environment. Outputs reviewed before entry.

Problem

AI-drafted submissions without clear authorship trail.

Solution

AI-assisted drafting with versioned human review at every stage.

Problem

Researchers using cloud AI tools on identifiable data.

Solution

On-premise or private-cloud AI deployment with access controls and audit trail.

Problem

AI tools used in policy work without audit trail or approval record.

Solution

Governed workflow with version control, human review gates, and signed approval record.

Problem

Staff using public AI tools on sensitive internal documents.

Solution

Private deployment on sovereign infrastructure with access controls and query logging.

Problem

AI evaluation of supplier bids without documented rationale.

Solution

AI-assisted scoring with full decision trail and human sign-off before award.

Problem

Staff paste client financials into whichever AI tool is open, with no read on where that data goes or whether it trains on it.

Solution

Every tool in the firm gets a plain-language verdict on data handling before it touches a client file — not after.

Problem

New AI bookkeeping tools get adopted firm by firm, with no consistent check on vendor terms.

Solution

A named, prescribed stack — each addition chosen for fit, each carrying its own safety verdict.

Problem

Nobody has a weekend to read nine vendor privacy policies before tax season.

Solution

A 45-minute call and a report. The first fix is installed before the call ends.

Problem

AI scribes are spreading through dental practices faster than anyone is reading the vendor's data-handling terms.

Solution

A verdict on the scribe itself, plus the due-diligence checklist Ontario's privacy regulator now expects before adoption.

Problem

Scheduling and intake tools touch patient data with no one checking whether the vendor trains on it.

Solution

Front-desk tools verdicted the same way clinical tools are — data location and training policy, in plain language.

Problem

Every new AI tool the practice picks up adds another vendor policy nobody has read.

Solution

One review call. Every tool verdicted, three to seven additions prescribed, first fix running before you leave the call.

Problem

AI research tools invent case law that reads as real until a judge checks it.

Solution

A verification workflow around every AI-assisted filing — not a ban on the tools, a check on the output.

Problem

Mismatched or fictitious citations reach the court before anyone cross-checks them.

Solution

The tool gets a verdict, and the workflow around it gets a verification step, before the next filing.

Problem

Small firms adopt AI intake and drafting tools one at a time with no consistent safety check.

Solution

Every tool in the stack verdicted in one call, additions prescribed to fit, first fix running before you hang up.