How do I use the filters to scenario model
and curate insights?

Filters (top centre of each chart) can be used to adjust the longevity and granularity of insights and set scenarios to reflect a workforce’s potential course of technology prioritisation and implementation. This is useful for refining and understanding key areas for transformation planning.

Filter options can be applied in any combination, include the time horizon (years 1-15), 17 technology types driving impact, organisation units, job categories, job levels, job categories, locations, gender and age brackets, and any data provided for custom filters.

Once selected, filters are persistent across the dashboard and all chart pages and can be reset and re-configured at any point. Filters will also apply to any data extracts you choose to export from Faethm.

How is automation defined?

Automation describes the capacity of technologies to replace a job (or a significant portion of it) and hence replace a worker’s tasks.

How is augmentation defined?

Augmentation refers to the capacity of technologies to partially supplement jobs, making workers more efficient at completing tasks. Augmentation can enable workers to gain capacity through efficiencies; this frees up time to focus on other value-adding pursuits.

Can a job be impacted by both automation
and augmentation at the same time?

Yes, a job can be proportionally impacted by both automation and augmentation. Different tasks in a job are subject to either automation or augmentation (or can remain unimpacted) depending on the nature of the work and the availability and adoption of technologies at a particular time.

For example, an Accountant completes numeric tasks that rely on straight-forward logic that are automatable today. However, they also carry out more abstract tasks which are eligible for augmentation, such as visualising and presenting data.

How is addition defined?

Addition describes the additional new jobs that may be required to enable the organisation to implement, govern, run and maintain emerging technologies, assuming that the adoption of emerging technologies will require a number of new roles.

How are the automation and augmentation

predictions made?

The Faethm prediction engine contains a machine learning (ML) algorithm that predicts the likelihood of a technology either automating or augmenting a task. We have rely on our expert panel to evaluate the jobs that are ‘immune’ from automation, and those that are vulnerable. Our team’s expert opinions train our machine learning tool to determine the risk of automation.

Some tasks, while easily automatable by emerging technology, may not be automated due to the nature of the job. Roles at low risk of automation, however, may be more likely to be augmented by emerging technology. For example, Nurses or CEOs are unlikely to be automated, rather many of their tasks can be augmented due to the interactive face to face nature of the tasks preferred in the job.

In some cases, a job will be impacted by both automating and augmenting technologies. A job is made up of different tasks, and each task may be impacted by different technologies at different timeframes. For example, while a job may be impacted first by augmenting technologies, it may later be automated due to the advancement and availability of technologies.

How are predictions made specific to our

workforce, country and industry?

Predictions are tailored to your workforce, by cross-referencing your organisation’s jobs to the Faethm jobs database. We then apply country and industry specific technology adoption rates to further align predictions with your workforce’s unique context.

Faethm uses the assessments from 115 individual technology adoption indicators to create a proprietary assessment of each country and industry’s technology adoption rates.

•Country-specific adoption rates have been created through research compiled by the World Economic Forum, INSEAD and Cornell University and MIT.

•Industry-specific adoption rates have been created through research compiled by McKinsey.

What assumptions (e.g. macroeconomic)
are considered?

Faethm’s predictions of the future impact of technology on jobs are based on a combination of multiple research sources (including Gartner, MIT, McKinsey and more) and an expert panel’s current understanding of the rapid growth and adoption rates of technologies.

Users apply Faethm’s scenario modelling and filters to evaluate technology impacts associated with the organisation’s strategic views. Users may do so using various factors including: applications of technology over time,  specific organisational unit context and geographic location of offices.

Faethm’s model is not designed to forecast all likely scenarios based on all possible events. We deliberately ignore macro-economic impacts, such as climate change, wars, political unrest, and recessions.  Users can download raw Faethm data (using the top right hand corner download tool), and include this as an input to develop more detailed scenario-planning that incorporates their own macro-economic models.

Does Faethm consider the emerging technologies already implemented?

Faethm does not directly factor in automation and augmentation initiatives already implemented by organisations. However, the user may provide up to date workforce data to Faethm to refresh the platform, and to ensure up-to-date job data are reflected. For example, if a client elects to deploy an RPA (robotic process automation) program that prompts the reskilling and redeployment of 80 accountants into new jobs internally, this change will be reflected in the client’s new workforce dataset once reloaded into Faethm.

How does Faethm determine prediction accuracy?

Predicting the future with complete certainty is impossible. However, Faethm's prediction accuracy scores at 96% for both precision and recall (a measure of evaluating performance and efficacy of data science models). This has been tested against real world job automation scenarios and data.

How are the ‘impacted’ FTEs calculated
and presented on each chart?

FTE equivalents are calculated by measuring the portion of a job impacted by automation or augmentation, then aggregating by the number of jobs across the workforce.

For example, assume a company has 5 Accountants. 20% of an Accountant’s role maybe automated due to AI/ Robotic technologies. The chart represents the aggregated amount of this 20% automation by the number of individuals in the role.  The 1 FTE impact figure for Accountants would be comprised of the consolidated portions of 5 augmented Accountants.

How frequently can I conduct an analysis?

There is no limit how frequently charts can be extracted from the Faethm database. We recommend that our clients choose a frequency based on cadence of internal meetings, strategic planning etc.

How frequently can I reload/update my workforce data into Faethm?

Data can be reloaded on a quarterly basis, unless otherwise stated in your contract.