Organisations often face a false choice: either build a bespoke activity taxonomy from scratch or adopt an external standard wholesale. The better answer is to use external data as a disciplined starting point and then make it real for your organisation.

Occupational sources such as the Standard Occupational Classification and O*NET provide a valuable backbone. They offer common occupational descriptors, task and work-activity data, skills, knowledge, and work-context information. This allows an organisation to start with a structured view of comparable work rather than relying only on inconsistent local job descriptions.

External taxonomies are especially useful in three ways.

First, they create a common language across countries, business units, and job families. A role may have different internal names in different parts of a company, but a shared occupational reference can help identify the underlying similarity.

Second, they make analysis more scalable. Instead of beginning each role with a blank page,analysts can start from a relevant occupational profile, select the activities that fit, and focus workshops on the differences that matter.

Third, they enable more informed benchmarking.

Public research on skills, task exposure,and changing occupations is normally expressed using standard occupational structures. A well-governed mapping lets an organisation translate that evidence into its own job architecture without pretending the external data is its workforce.

But the warning is important: a taxonomy is not a job observation. It does not know your products, systems, policies, customer expectations, risk appetite, or operating model. Two organisations with a similarly named role can have materially different work content. Blind adoption therefore creates false precision.

The right method is a controlled translation:

1. Identify the closest external occupational family or profile.
2. Extract the relevant work activities, tasks, skills, and context descriptors.
3. Compare them with internal job descriptions, process evidence, and practitioner insight.
4. Remove activities that do not occur; add activities that are distinctive to your organisation.
5. Consolidate over-detailed items into an analyst-friendly level of granularity.
6. Record the rationale, source, confidence, and date of the mapping.

This is where governance matters. People analytics, job architecture, and business representatives should jointly own the taxonomy. A small number of canonical activity definitions reduces duplication and makes comparisons possible; local variations should be retained when they genuinely affect how work is performed.

A useful principle is: standardise the language, not the lived experience. The aim is astable core activity catalogue that can be adapted to role context, not auniversal list imposed on every team.

Used this way,external taxonomy data gives activity-led workforce planning both speed and credibility. It provides a scaffold. Your people provide the building.

**Next article:**Once work is mapped, how can you assess AI exposure without confusing a scorewith a forecast?