Case Studies

Outcome-driven representative cases showing how AI workflow design, secure controls, integrations, and human oversight improve enterprise operations.

Representative engagements with measurable results

Where client names are confidential, we label the work clearly as a representative case. Each example below includes the client context, challenge, solution delivered, timeline, tools, and measurable before-versus-after outcomes.

Representative Case

AI-assisted intake and routing for shared services

Australian enterprise shared services function handling HR, IT, and finance requests through fragmented email, portal, and workflow channels.

Timeline

12-week workflow redesign and deployment

ROI / efficiency

Estimated payback inside 8 months through reduced manual handling and fewer escalations.

Challenge

Manual triage, inconsistent intake data, and unclear routing were slowing response times and making it difficult for leaders to see where work was blocked.

Solution delivered

  • Redesigned the intake model around intent, urgency, data quality, ownership, and approval requirements.
  • Introduced AI-assisted classification with human review checkpoints for low-confidence, sensitive, or unusual requests.
  • Connected the workflow to operational reporting for queue health, ageing work, rework, and exception volume.

Measurable results

Reduced time-to-triage by 43% within the first 90 days.
Cut misrouted requests by 31% through structured intake and routing rules.
Improved SLA attainment from 72% to 89% across priority queues.

Before vs after

Time to triage

Before: 14.2 hours average

After: 8.1 hours average

Misrouted work

Before: 18% of requests

After: 12% of requests

SLA attainment

Before: 72%

After: 89%

Tools and technologies used

AI classificationHuman review checkpointsServiceNowWorkflow reporting
Discuss an AI intake model

Representative Case

Human + AI triage model for customer operations

High-volume customer operations team supporting service incidents and billing enquiries across multiple inbound channels.

Timeline

10-week pilot plus 4-week rollout

ROI / efficiency

Delivered annualised labour savings and service uplift worth 3.1x the initial pilot investment.

Challenge

Simple and complex cases were entering the same queue, supervisors were spending too much time reassigning work, and backlog growth was masking service risk.

Solution delivered

  • Designed an AI-assisted triage model to classify common enquiries and surface missing information before assignment.
  • Separated standard work from exception paths with explicit confidence thresholds, escalation rules, and supervisor oversight.
  • Added dashboards for queue mix, rework, backlog ageing, and AI-assisted routing performance.

Measurable results

Reduced backlog by 28% over one quarter.
Lifted first-touch resolution by 19 percentage points.
Freed approximately 1,100 supervisor hours annually from manual queue reallocation.

Before vs after

Backlog

Before: 4,800 open cases

After: 3,450 open cases

First-touch resolution

Before: 54%

After: 73%

Supervisor reallocation effort

Before: 22 hours per week

After: 6 hours per week

Tools and technologies used

AI triageKnowledge orchestrationHuman-in-the-loop reviewQueue analytics
Explore human + AI triage

Representative Case

Secure approval workflows with audit-ready AI assistance

Regulated operations team needing faster case progression without weakening controls, auditability, policy enforcement, or sensitive data handling.

Timeline

14-week controls and workflow implementation

ROI / efficiency

Lower rework and faster cycle times created an estimated 22% operating efficiency gain.

Challenge

Approval steps were handled through email and spreadsheets, creating inconsistent evidence trails, unclear ownership, and slow exception handling.

Solution delivered

  • Mapped approval journeys and policy exceptions into a governed workflow with role-based checkpoints.
  • Introduced auditable case histories, exception logging, human review points, and escalation triggers.
  • Created executive reporting for turnaround time, exception rate, approval bottlenecks, and control evidence.

Measurable results

Reduced average case completion time by 37%.
Cut approval chasing effort by 46% for operations staff.
Improved audit readiness with a single workflow record for each decision.

Before vs after

Case completion time

Before: 11.4 days

After: 7.2 days

Approval follow-up effort

Before: 13 hours per week

After: 7 hours per week

Decision traceability

Before: Split across email and spreadsheets

After: Single auditable workflow history

Tools and technologies used

Approval workflow designAudit loggingPolicy controlsServiceNow
Talk about secure workflow design