CASE STUDIES

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A large APAC enterprise with multiple AI/GenAI pilots in production, aiming to scale into Agentic AI (autonomous workflows, multi-step decisioning, tool-using agents) across core operations.

The client’s leadership wanted to move beyond copilots to agent-driven automation—but needed confidence that the organisation was ready to scale safely.

Key concerns:

• Inconsistent controls across AI initiatives (team-by-team “best effort”) 
• Limited end-to-end evidence for risk, audit, and production sign-off 
• ata readiness gaps (quality, access, lineage, sensitive data handling) 
• Unclear accountability for agent actions and outcomes 
• Monitoring not designed for agent behaviours (tool calls, autonomy, drift, escalation)
   

QSolX executed an Agentic AI Readiness & Maturity Assessment using AI TrustX™ (6 Trust Pillars × 12 Capability Domains × 6 Lifecycle Phases: Design → Build → Deploy → Monitor → Govern → Optimize).

• Portfolio scan: current AI/GenAI use cases, platforms, vendors, and delivery patterns 
• Stakeholder interviews across Business, Tech, Risk, Compliance, Legal, Audit 
• Control and evidence review across lifecycle phases (what exists vs what’s needed for agents)
  Outputs
• AI TrustX™ maturity heatmap (current vs target) 
• “Agentic AI readiness” score and critical risk indicators 
• Priority use cases segmented by autonomy risk tier (low/medium/high)
   

We identified gaps across:

• Governance & accountability: decision rights, RACI, escalation, approvals 
• Model assurance: robustness, safety testing, agent evaluation, red-teaming 
• Security & privacy: tool access, secrets handling, least privilege, data boundaries 
• Operational controls: change management, versioning, incident response 
• Monitoring: agent telemetry, action logs, audit trails, outcome validation 
• Value realization: benefits tracking, leakage control, productivity vs workforce impact 

         Outputs

• Requirements Gap Table (capability-by-capability) 
• Control library enhancements needed for Agentic AI 
• “Production gate” definition for autonomous workflows
   

• 90-day stabilisation plan: foundations + quick wins 
• 6–12 month scale plan: operating model + toolchain + governance cadence 
• Use-case sequencing: what to scale first, what to delay, and why 

        Outputs

• Board/ExCo-ready roadmap (workstreams, owners, milestones, dependencies) 
• Implementation backlog (prioritised, costed at a high level) 
• Enablement plan for teams (product, engineering, risk, audit)
   

• Agentic AI Readiness Assessment using AI TrustX™ 
• AI TrustX™ Maturity Scorecard + Heatmap (enterprise + business-unit view) 
• Gap analysis across the 12 capability domains and lifecycle phases 
• Agentic AI control enhancements (accountability, tool governance, monitoring, auditability) 
• Scale roadmap (90-day quick wins + 12-month industrialisation plan) 
• Executive pack: risk/value view, prioritised use-case sequencing, decision points
   

• Clear, defensible answer to: “Are we ready to scale Agentic AI?” 
• Reduced delivery risk via standardised “trust gates” and evidence requirements 
• Faster path from pilot → scaled deployment by aligning teams on one playbook 
• Improved auditability: action logging, traceability, and accountability for agents 
• Better ROI discipline: prioritised use cases tied to measurable outcomes
   

• Agentic AI Guardrails Pack: autonomy tiers, human-in-the-loop patterns, tool permissioning, action constraints 
• Agentic Monitoring Blueprint: telemetry, evaluation metrics, drift/behaviour monitoring, incident playbooks 
• Pilot-to-Scale Accelerator: implement the top 1–2 use cases with full AI TrustX™ evidence pack 
• Annual AI TrustX™ Re-Certification: re-assess maturity, control effectiveness, and roadmap refresh 

A Singapore-based APAC enterprise in a regulated environment, scaling GenAI and advanced analytics across multiple business lines.

The organisation had strong AI momentum—pilots, vendor POCs, and growing business demand—but lacked a repeatable enterprise model to scale AI safely, audibly, and consistently. Key gaps included unclear cross-functional accountability, inconsistent controls and documentation, and data governance not being “AI-ready” (quality, lineage, access, and evidence).

Using AI TrustX™ (6 Trust Pillars × 12 Capability Domains × 6 Lifecycle Phases), QSolX implemented a practical, delivery-friendly governance model that enabled teams to move from experimentation to industrialisation:

• Enterprise AI Strategy & roadmap (12–24 months) 
• Responsible AI controls & evidence packs embedded across the AI lifecycle 
• AI governance operating model (RACI, councils, decision gates, KPIs) 
• Data governance framework (ownership, quality, lineage, access) 
• Enablement and advisory for Business, Tech, Risk, Compliance, and Audit

• Faster “pilot → production” via standard trust gates and reusable templates 
• Reduced late-stage rework through upfront assurance design 
• Clearer decision rights and audit-ready evidence per AI use case 
• Improved confidence to scale GenAI responsibly across the enterprise
   

• GenAI guardrails pack (copilot/agent readiness) 
• AI use-case factory (intake → prioritisation → approvals fast-track) 
• Tooling and vendor selection support (governance + assurance)

Client (anonymised)

A high-growth startup building AI/GenAI-enabled products and internal automation, preparing to scale delivery and meet enterprise customer expectations (security, governance, auditability).

The startup was moving fast—shipping features, testing new models/tools, and expanding use cases—but needed an enterprise-credible foundation without adding heavy bureaucracy.

Key gaps:

• No clear AI strategy or prioritised use-case roadmap (everything felt urgent) 
• IT/security policies not updated for AI/GenAI (data handling, vendor usage, access controls) 
• Unclear accountability for AI decisions (who approves what, and when) 
• Inconsistent evidence to reassure enterprise customers during due diligence 
• Limited monitoring/controls for models and GenAI workflows in production
   

QSolX ran a rapid AI TrustX™ maturity assessment (6 Trust Pillars × 12 Capability Domains × 6 Lifecycle Phases), then designed a startup-friendly operating model and policy pack.

• AI Strategy (6–12 months): business outcomes, priority use cases, build/buy/partner choices 
• Lightweight AI governance model: RACI, decision rights, release gates, monthly cadence 
• AI-ready IT policy pack: acceptable use, data classification, vendor/tool usage, secure development, logging & retention 
• Delivery playbook & templates: use-case intake, risk triage, go-live checklist, monitoring plan 
• Customer trust pack: concise governance/security posture statement + evidence checklist to accelerate enterprise sales cycles

• Clearer prioritisation and faster execution through a defined AI roadmap 
• Reduced risk from unmanaged tools/models via updated AI-specific IT policies
• Faster enterprise customer approvals with a “trust pack” ready for due diligence 
• Consistent delivery with minimum viable governance and evidence standards 
• Improved production readiness through monitoring and lifecycle gates
   

• GenAI guardrails pack (prompt/data controls, red-teaming, usage monitoring) 
• Vendor/tooling selection support (scorecards + target architecture) 
• Quarterly AI Trust Reviews + Annual AI TrustX™ Re-Certification (maturity uplift + ongoing assurance) 

Client (anonymised)

A startup / scale-up wanting to move beyond “chat” into actionable automation—an AI that can classify, decide, and trigger real workflows across tools.

They wanted an “agentic” capability that could:

• Take repetitive operational tasks (lead triage, routing, follow-ups, updates) and execute them consistently 
• Maintain reliability with fail-safes and traceability 
• Avoid spam/low-quality inputs and reduce noise 
• Scale operations without adding headcount UT-Capabilities
   

We built an AI-driven decision layer connected to an automation backbone—exactly aligning to the document’s “AI-Driven Logic + Workflow Automation + Robust Fail-Safes + Spam Filtering” pattern:

• Decision engine (AI-driven logic): classify inbound requests, extract intent, apply routing/qualification rules 
• Workflow executor (n8n automation): trigger the right downstream actions across operational tools 
• Smart responses: immediate customer acknowledgement + internal alerts for handoff where needed 
• Spam filtering: block junk requests while keeping the public experience smooth 
• Robust fail-safes: branching logic, fallback steps, and backup routes to ensure continuity
   

• OpenAI / Claude: intelligence layer for decision-making + content generation (responses, summaries) 
• n8n: orchestrates multi-step workflows across systems 
• Google Sheets + CRM integration: creates a single operational view + collaboration loop UT-Capabilities
   

• Faster handling of repetitive ops tasks (triage → route → notify → update) 
• Higher consistency (standard process executed the same way every time) 
• Reduced manual workload and cleaner operational data UT-Capabilities
   

• Conversion site + secure backend for intake (Bolt + Supabase + Vercel)  
• Marketing execution tie-in (Meta Business Suite / Ads Manager / Google Business Profile workflows)  
• Governance overlay using AI TrustX™: maturity baseline → gaps → roadmap → ongoing assurance (annual re-certification)