Ninety-one percent of organisations across the world today are experiencing difficulty attracting digital talent, with 17% of employers reporting that they are experiencing this challenge to a great extent. The fourth industrial revolution is changing the world around us, and global demand for the right digital talent is outpacing the current supply.1 Given the exponential growth in the number of employers vying for talent with similar skills, it’s no wonder that talent acquisition strategies and existing benchmarking methodologies need to evolve.
For most organisations around the world, cash compensation remains one of the most effective tools for attraction and retention. Reward programs are becoming more competitive, creative and differentiated, however challenges around salary budget management (e.g., rewarding top performers, pay equity, and innovating compensation programs) remain.
There are three significant changes emerging around job design that we expect will inspire HR functions to upgrade their benchmarking methodology. They need to do this to reduce the risk of losing top talent to employers that can provide a more attractive offering, and increase the likelihood of their key talent staying and contributing to business transformation and driving organisation results.
- Jobs are being broken down into tasks and skills
With automation helping to break jobs down into tasks and skills, there are now a multitude of ways to get work done. Organisations are starting to re-examine their definition of a job and deconstruct jobs in their organisation into tasks, identify the value of tasks within jobs and determine how these tasks can be reconstructed into new, more optimal combinations.2
This approach to jobs is changing the very nature of survey benchmarking. Today organisations match jobs to survey benchmarks based on the assumption that work and skills required for that job are largely comparable. A good benchmark is at least an 80% match. Organisations use a single data point or range for an individual position.
In the future, instead of benchmarking jobs, we may see a resurgence of skills-based evaluation and pay. Instead of benchmarking an entire job, organisations will need to think more about the work they need to get done and the skills required to do that work. This may mean looking at the market rate of unique skills (which underpin the tasks to do the work) and tie them to the baseline price for a job. This aggregation allows organisations to capture the unique activities that are performed by humans in jobs and reflect differences between them and their competitors who use more or less automation or other alternative work sources.
- Skill diversity is valued over experience
Digital transformation is enabling new combinations of work and talent requirements, thus creating a new dichotomy of skills. There are ‘premier’ skills on one hand that are in high-demand but low supply (e.g., blockchain, AI, cyber security, business intelligence analytics). On the other hand are the more ‘traditional’ skills that are in high supply but low demand, such as processing and manual tasks that can be partially automated.
A study by McKinsey reported that the work hours for skills that are categorised as ‘higher cognitive’, ‘social and emotional’, and ‘technological’ would likely increase by a range of 26% to 60% within two decades.3 Higher cognitive skills include advanced communication, complex information analysis, critical thinking and creativity. Social and emotional skills include interpersonal skills, leadership and continuous learning. Technological skills include core technical competencies such as digital skills, advanced data analysis and mathematics, engineering, design and research. Many of these skills are non-repetitive and not easily automatable and, more importantly, are fundamental to jobs across a wide range of industries. At the rate that job requirements are changing, organisations are beginning to value skill diversity and greater levels of flexibility and adaptability from their employees over their past experiences or education.
What does this mean for benchmarking? Organisations need to be able to understand the demand for the skills required to get work done and the relative value of those skills so they can attract and retain talent with skills they need. They may need to be prepared to pay a premium for employees that have a breadth of skills (e.g., premium, cognitive, social and emotional) that are transferable from one task to another.
- Skills are becoming industry agnostic
Many of the new jobs invented in the fourth industrial revolution have introduced skills that can be applied across a number of industries. For example, AI-related skills are revolutionising products and processes across sectors from agriculture to telecommunications to life sciences.
There are jobs that, thanks to digital transformation, have gn outside of their usual playing fields. For example, digital marketing professionals have evolved from being mostly in the marketing and communications sector, to now existing in previously ‘unlikely’ industries for digital media, such as in pharmaceuticals, energy, and manufacturing.
In addition, some specialisations are being combined into new ‘hybrid’ jobs. For instance, individuals who are proficient in both AI and advanced mathematics can find opportunities not just in high tech or financial services, but also in health sciences, media and tourism.
The most common approach to compensation benchmarking has been industry focused. Now when benchmarking compensation, organisations may need to incorporate benchmark pay data from other industries or a broader market, and consider including non-traditional peers who are recruiting talent with similar skills. Staying with traditional checkpoints could negatively impact their competitive positioning in the market, and make it difficult to attract and retain the talent with the skills their organisation needs.
Organisations need to be able to determine the work they need to get done, the skills required to do that work, the supply and demand of the skills and a broader talent market against which to benchmark their compensation data. It is vital for compensation benchmarking methodologies today to be agile and automated, in order to provide timely support for leaders and managers in compensation decisions.
With all these factors to consider, the traditional benchmarking approach is becoming increasingly complex and requires more advanced technology to support the process. Currently however, many HR functions still rely heavily on a system of spreadsheets to help implement their benchmarking. The value of a job continues to be validated according to a common, market standard – and many compensation professionals today still carry out the task of benchmarking manually through line-by-line job matching and reporting.
Many of the HR technologies now available today can rapidly, securely and automatically process multiple, wide-ranging salary datasets. Adoption of this software can empower compensation professionals to more easily identify patterns, make revisions and updates based on market comparisons, and create budget models that show the cost implications of salary and merit increases over time.
In part two of this series, we will explore some of the emerging digital technologies and practices around compensation surveys, and how they can revolutionise the data collection process.
Sources:
1 2018 Artificial Intelligence and Digital Talent Compensation Survey
2 Reinventing Jobs, Jesuthasan and Boudreau, Harvard Business Review Press, 2018
3 World Economic Forum: The 3 skill sets workers need to develop between now and 2030
