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Job architecture

Build a clear, consistent structure for intentional workforce planning by clarifying roles, organizing data, and enabling adaptability to shifting markets and new technologies.

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What is job architecture?

Job architecture is the framework that defines and organizes roles across your organization, grouping them by family, level, and makes it easier to understand the work they’re meant to deliver.

It creates a clear, consistent structure that improves workforce planning, brings transparency to pay and progression, and helps you manage costs and identify skill gaps. With this foundation, you can respond to immediate pressures with confidence while building toward ongoing workforce intelligence.

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The challenges of building a job architecture

With thousands of positions to align, organizations often struggle with:

  • Keeping structures current as work evolves, especially when job architecture is treated as a one-time project
  • Navigating regional variations, inconsistent titles, and overlapping roles that make standardization difficult
  • Working around outdated systems
  • Outdated HR systems and unstructured data that lacks necessary fields for job architecture
  • Inadequate job leveling that leads to pay compression and regional disparities

We had people with different titles doing the same work, and people with the same title doing different work; they had different grades and different compensation. It was chaos. It was impossible to plan the organization.

Orgvue partner

Turn messy positions data into a structured architecture with Orgvue

With Orgvue, you can rapidly group thousands of position records into clearly defined roles, clusters, and job families, creating structure where none existed before.

  • Strengthen your architecture with Orgvue’s role grid builder, defining role families and levels that support skills planning, reward decisions, and career pathways.
  • Automate the hardest and most time-consuming work with Henshaw Roles AI, converting messy position data into coherent role clusters in a fraction of the time.

Simplify organizational data to enable intentional planning

AI Automated clustering accelerates the creation of role data, making the process faster, more reliable and auditable, tailored to your organizational context.

Organization and AI transformation

Clustering roles gives you a macro view of the workforce, reducing complexity and creates the foundation for workforce, succession and skills planning.

  • Create framework for new job structures, pay grades and career paths
  • Reveal how demand shifts as work changes
  • Identify opportunities to retrain and reallocate resource

Mergers and Acquisitions

Aggregate labor cost, headcount and job level data from multiple sources and standardize across businesses for consistent views and assessment.

  • Reveal available skills at different levels in the organization across specific job families
  • Compare pay practices across the organizations
  • Instead of layoffs, use job architecture to identify transferable skills and redeploy

Workforce Planning

Fast track your planning baseline with a job architecture that links work to roles and skills.

  • Identify current and future skills gaps
  • Build training, succession, and promotion plans
  • Feed plans into HR systems for execution

“Henshaw AI has significantly accelerated our Org Design and workforce planning efforts, expediting an otherwise manual approach, and reducing the timeline by at least six months.”

How Orgvue simplifies job architecture

1

Unify and assess current positions data

Orgvue brings together data from finance, HCM, and regional systems into a single, trusted view so organizations can assess their starting point and build toward ongoing workforce intelligence.

Answer questions like:

  • Which grades have the most positions?
  • How many positions and position titles exist?
  • Where is title proliferation creating unnecessary complexity?
An Orgvue screenshot titled 'How are my positions organized today?'

Understand the position data in more detail

An Orgvue screenshot titled 'Role Grid'

View clustered roles across levels to reveal gaps and areas of risk

2

Aggregate positions into roles with AI and build the role grid

  • Use Henshaw Roles to group similar positions into role clusters that reflect how work is done
  • Transfer clusters into the role grid, cross-referencing them with organizational level and role complexity
  • Refine groupings in the grid, consolidating or creating new jobs as the structure is validated
3

Review the role grid

Use Orgvue’s pre-built analysis templates to assess and correct issues in your taxonomy.

Answer questions like:

  • Are there red flags, duplications, or roles without positions?
  • Where do the same roles appear across multiple managerial levels?
  • Which role titles show wide variation in cost or accountabilities?
An Orgvue screenshot titled 'Roles Taxonomy Review'

Reveal risks in your role taxonomy and correct them as you build the architecture

An Orgvue screenshot titled 'Are positions on target for role cost and role span?'

Flag where positions sit against role cost and role span targets

4

Add additional role data

  • Enhance your data infrastructure with more granular fields to support micro-level design
  • Set targets for accountabilities, activities, and skills at each level to strengthen role clarity and future planning
  • How many unique job families are there in my organization?
  • How many unique jobs are in each job family?

FAQs

What are role clusters and job families?

Job families are groupings of roles with similar skills, and professional qualifications and exist across a number of levels in the organization, like Finance, Marketing or Engineering. A role cluster is a grouping of roles or positions that represent an organizational capability, defined by the work it enables, such as Junior Software Engineers (consisting of Junior front end engineers, junior back-end engineers, junior QA engineers).

In Strategic Workforce Planning, Succession Planning or Skills Initiatives, these groupings provide a macro-level view for planning but range from broad job families to granular job levels to incorporate each organization’s specific content.

Why is Henshaw AI helpful to Job Architecture?

Henshaw is Orgvue’s AI capability designed to accelerate organizational clarity and empower faster, smarter decisions about work and workforce. It applies generative AI and machine learning directly within Orgvue to automate structural insight, reduce manual effort, and reveal opportunities for change.

Henshaw will enable users to:

1. Work more efficiently – augment more tasks with AI
2. Manage and interpret the data more intelligently
3. Drive to outcomes more decisively – confidence in output

How does Henshaw Roles differ from importing an external job taxonomy?

By learning from the business’s titles and only then clustering and naming, Henshaw produces Role Clusters that are faithful to the actual capabilities of your unique business. Henshaw Roles gives an accurate picture of the organization, specifically because it learns from your org context rather than forcing a generic schema. This makes the output far more relevant to planning for that business.

Why is it better than human-derived outputs?

Automated, mathematical driven and AI powered Role Clustering is simply more reliable than human judgment. People bring bias (familiar titles, politics, recent examples) and make inconsistent calls when titles are messy, or workloads are large. Our method applies the same rules to every position and produces clusters that are reproducible and auditable. The result is a faster, fairer, and more defensible view of your real capabilities.

How secure is my data?

Orgvue is a Software as a Service (SaaS) solution. This ensures that all of our customers benefit from robust and consistent security measures, data management policies, and service level commitments. More details are available from our Orgvue Trust Center.

All Orgvue Henshaw capabilities are opt-in only, per tenant and per capability. This ensures separation of data per customer and per AI use-case. This means no data is shared back to any LLMs (Large Language Models). Information on OpenAI Privacy is available at: https://openai.com/enterprise-privacy/

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