Role Overview
The Business Intelligence Lead is responsible for advancing IAM through the redesign of structured processes, business intelligence, and AI-enabled automation.
This role evaluates L1 and L2 IAM workflows, identifies inefficiencies and service gaps, and transforms operational challenges into measurable improvement initiatives. By modernizing workflows and introducing intelligent automation, this position enables IAM teams to operate with greater precision, scalability, and technical depth.
A key focus of the role is improving overall business impact, ease of service, and ease of engagement with IAM services. This includes enhancing the customer experience for internal stakeholders while strengthening the efficiency and reliability of IAM operations.
The role converts operational pain points into structured initiatives that deliver measurable business outcomes. This includes partnering with key business stakeholders to gather feedback, understand service friction points, and identify opportunities to improve the overall experience of engaging with IAM services.
The objective is to build an operational model in which repetitive work is streamlined, decision-making is supported by data, and operational capacity is reinvested in higher-value IAM functions.
Core Responsibilities
Operational Process Review & Redesign
- Assess L1 and L2 IAM workflows, including JML, provisioning, authentication support, service accounts, privileged access operations, and request management.
- Identify bottlenecks, rework loops, unclear ownership boundaries, manual dependencies, and control gaps.
- Develop structured problem statements defining:
- Current-state friction
- Operational or risk impact
- Root cause drivers
- Optimized future-state design
- Translate findings into phased improvement roadmaps with defined milestones and measurable outcomes.
Business Intelligence & Data Analysis
- Analyze IAM operational metrics (volume trends, SLA adherence, backlog drivers, repeat incidents, error patterns).
- Use quantitative insights to prioritize transformation initiatives.
- Establish baseline KPIs and define performance targets tied to efficiency, quality, and operational maturity.
- Create transparency into where effort is spent and where capacity can be reallocated.
AI & Automation Enablement
- Identify opportunities for:
- Rule-based automation
- Workflow orchestration
- AI-assisted triage and decision support
- Intelligent request intake and knowledge automation
- Design automation solutions leveraging Azure services (Logic Apps, Power Automate, Functions, AI services), APIs, scripting, and ITSM integrations.
- Partner with IAM engineering and platform teams to move from concept to implementation.
- Ensure automation enhances security posture, auditability, and compliance alignment.
Continuous Improvement & Capability Advancement
- Identify structural gaps in IAM services, documentation, and operational ownership.
- Reduce manual process dependency to improve consistency and throughput.
- Enable operational capacity to shift toward more complex IAM analysis, control validation, and higher-order problem solving.
- Establish repeatable frameworks for evaluating and improving new and evolving IAM processes.