annual support cases
Operational scope used to frame service and AI requirements at SOTI.
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SOTI
Technical Operations Transformation
Operational diagnosis, cross-functional requirements and controlled AI enablement across a complex enterprise support environment.
Changes implementedAI benefits projected
- Role
- Technical Support Manager, EMEA — leading operational diagnosis, technical requirements, customer recovery and cross-functional change.
- Maturity boundary
- Case distribution, escalation playbooks, delivery artefacts and operating governance were implemented. The C$750K–C$1.2M efficiency range is a projected annual opportunity from a VP Product-endorsed business case, not a realised saving.
01SignalsCases · SLAs · customer risk
02DiagnosePatterns · priorities
03DefineScope · owners · measures
04ImplementWorkflow · enablement
05ReviewOutcomes · iteration
Customer operationsFrontline evidence → cross-functional change
CONTEXT
A large enterprise support operation needed clearer service mechanics, more consistent escalation control and a dependable route from frontline evidence to cross-functional implementation.
MY CONTRIBUTION
I used service data and frontline knowledge to prioritise systemic problems, created reusable delivery artefacts and coordinated work across Account Management, Sales Engineering, Professional Services, Product, Engineering, BI and Support.
WHAT IS TRUE NOW
Operating changes and a repeatable delivery approach were implemented across the service. AI-assisted classification and deflection reached requirements and business-case stage; projected benefits were not realised outcomes.
- Operational evidence
- Clear ownership
- Controlled automation
potential annual efficiencies
Projected business-case range for AI-assisted classification and deflection; not realised savings.
CONTEXT & PROBLEM
A high-volume service produced signals faster than it produced change.
Queue pressure, escalations, customer risk and technical complexity were visible in different places. Without a shared way to diagnose and frame the work, useful proposals could remain ad hoc and cross-functional ownership could become unclear.
DISCOVERY EVIDENCE
Turn operational noise into a portfolio of solvable problems.
I combined Salesforce reporting, case volumes, SLA breaches, queue coverage, escalation data, customer-health signals and frontline insight to find recurring failure patterns and prioritise where a change could matter.
- Separated symptoms in individual cases from systemic service problems.
- Connected customer risk to the operating conditions behind it.
- Used the evidence to define testable initiatives, not simply dashboards.
DECISIONS & REQUIREMENTS
Give every improvement a shared delivery language.
I introduced problem statements, project briefs, business requirements documents, workflows, success measures and review loops. These artefacts made scope, ownership, dependencies and evidence visible across business and technical teams.
IMPLEMENTATION
Change the service mechanics, not only the reporting.
The work included a revised case-distribution model aligned to customer tier, technical complexity and language capability, plus risk-based escalation and recovery playbooks joining customer-facing and technical teams.
- Clarified response priorities, queue coverage and escalation control.
- Connected commercial and technical teams around customer recovery.
- Strengthened quality ownership and operating governance.
CONTROLLED AI ENABLEMENT
Define the system and evaluation before promising the efficiency.
For AI-assisted classification and deflection, I defined the data, metadata, knowledge, decision logic, evaluation, security and integration requirements with Product, Salesforce and Business Intelligence.
The business case projected C$750K–C$1.2M in potential annual efficiencies across an operation handling approximately 50,000 cases a year. It received VP Product endorsement, but the capability and savings are not presented as implemented.
SUPPORTING PROOF
- The reviewed CV source set separates implemented operational work from proposed AI enablement.
- Requirements covered data, metadata, KCS, decision logic, evaluation, security and integration.
- Business-case sponsorship is described as VP Product endorsement; no deployment or realised saving is claimed.
WHAT I CARRY FORWARD
Operational transformation is product work when teams and customers share the outcome.
The through-line was to diagnose from evidence, make ownership and requirements explicit, implement the smallest coherent system and review what changed in practice.
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