Leading Australian Bank used Reejig to inform strategic workforce forecasting, driving visibility and work transparency across more than 115 Job Families and 750 unique job roles addressing over 30,000 employees.
What we uncovered
At a glance
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Company: Leading Australian Bank
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Industry: Financial Services
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Team: Strategic Workforce Planning & Organisational Design
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Focus Area: Workforce forecasting, AI-driven insights, role design, skills taxonomy alignment
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Primary Use Case: Using Reejig insights to inform a 3-year strategic workforce plan
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Key Capabilities Used: Work Intelligence insights, tasks & skills data, emerging vs. declining skills, role group analysis
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Outcomes: Better scenario modeling, greater confidence in forecast assumptions, enhanced focus on work redesign priorities, targeted AI Implementation investment opportunities for experimentation and application.
The problem
The workforce planning team needed clear, data-backed insights to inform multi-year strategic workforce forecasting, but their existing approach lacked the granularity, scalability, and alignment required for effective decision-making.
The bank’s Strategic Workforce Planning team was in the midst of developing a three-year workforce strategy. While they were clear on the standard steps of workforce forecasting, identifying drivers, modelling scenarios, sizing the workforce, and shaping talent strategies, they lacked a scalable, consistent way to integrate AI, emerging skills, and role-level task insights into their modelling.
Their legacy process created several challenges:
- Data was fragmented, living across multiple reports and internal documents with no single source of truth for tasks, skills, and AI potential.
- Scenario modelling lacked external validation, making it difficult to compare internal modelling with broader market shifts.
- Skills language wasn’t aligned, creating friction between strategic plans and operational teams who relied on different taxonomies.
- Role groups were evolving quickly, making it unclear when reruns, updates, or refreshes were needed.
- Leaders needed interpretable insights, not raw data, to guide prioritisation for potential role redesign work.
As one stakeholder explained, before the team began applying Reejig’s insights:
And when reviewing skills trends:
The downstream impact was clear: without a unified, scalable intelligence layer, decisions about the future workforce relied too heavily on manual interpretation, siloed reports, and inconsistent taxonomies, none of which could support the pace of change driven by AI and new role creation.
The solution
The bank used Reejig to layer AI-driven workforce intelligence onto its strategic forecasting process, enabling clear insights on emerging tasks, skills, role evolution, and AI potential across the organisation.
Reejig became a foundational input at two critical stages of the bank’s workforce strategy process:
1. Upfront insight generation
The team used Reejig’s Work Intelligence data to understand:
- How AI and technology advancements could reshape roles
- Which tasks and skills were emerging or declining
- Role-level AI potential and where human-centric tasks were strengthening
- Opportunities for redesign based on task granularity
2. Scenario modelling and validation
Once the bank modelled future scenarios internally, Reejig enabled them to:
- Compare internal modelling against external benchmarks
- Validate assumptions about role size, skills shifts, and location strategy
- Understand whether they were directionally aligned with broader market movement
Additional capabilities unlocked
- Reejig’s role group and job family analysis helped the team understand whether new roles required reruns or refreshes.
- AI potential insights revealed where new leadership roles introduced more human behaviours, reducing AI automation percentages, an important directional signal.
- Reejig supported data definition clarity, enabling stakeholders to locate skills definitions in raw data files and prepare for future taxonomy alignment.
- The upcoming Reejig Work Intelligence Agent will streamline reporting questions directly within the Work Intelligence interface.
Example campaigns/plays this enabled
- Strategic workforce forecasting validation using Reejig data as an independent benchmark layer
- Prioritisation of role redesign based on emerging tasks and skills
- Identification of human-centric roles where AI potential decreased
- Taxonomy alignment planning to prepare for future integration
- Scenario modelling refinement based on Reejig’s task-level insights
Reejig’s impact
Reejig fundamentally shifted how the bank approached strategic workforce planning.
Before Reejig
- Fragmented insights across documents and teams
- No consistent source of truth for skills, tasks, or AI potential
- Difficulty validating internal modelling
- Unclear prioritisation for role redesign
- Limited visibility on emerging and declining skills
- Manual, effort-heavy interpretation
After Reejig
- Clear, structured insights on tasks, skills, and AI-driven workforce shifts
- Stronger scenario modelling through external benchmark comparison
- Increased confidence in workforce forecasts
- More informed prioritisation of roles for redesign
- Growing alignment around future taxonomy requirements
- Faster, more meaningful discussions with HRBPs and business stakeholders
As one team member noted about the enterprise report and insights:
Although no quantitative metrics were provided in the transcript, the qualitative outcomes were significant:
- Enhanced forecasting clarity
- Better alignment across Strategic Workforce Planning, OD, and HR
- Improved understanding of AI’s impact on roles
- A foundation for future integration and taxonomy alignment
- A scalable approach to evolving job families and role groups
Client Contributors to this case study: Head of Strategic Workforce Planning & Organisational Design Lead
