If AI is redefining the very notion of work and employment, we can’t avoid the question of whether it will also lead us to rethink the measurement and remuneration of work and performance.
Today’s rigid remuneration structures, based on standardized pay grids and evaluation systems, are likely to be increasingly ill-suited to a world of fast-changing roles, where the difference is made on the combination of human skills and the use of AI (How to reinvent work and professions in the age of AI), with more value placed on the impact achieved than on the effort put in.
Redefining performance assessment
Traditional performance assessment models are showing their limitations in the face of today’s transformations, and relying solely on traditional productivity indicators or annual appraisals is no longer sufficient to measure the real impact of employees, especially in an environment where AI is changing the way people work, and even redefining the notion of work.
Performance assessment needs to evolve towards a more continuous and systemic approach, where results are measured in terms of the impact generated rather than the volume of work delivered (Productivity: what if quality was the new quantity?).
This means taking into account the ability of employees to adapt to new technologies and take advantage of them to improve their efficiency. In contrast to rigid models, performance needs to be assessed on the basis of feedback, in a more agile way, with shorter assessment cycles and cross-perspectives integrating the acquisition of new skills and impact (How do you measure employee performance without becoming a Care Bear?).
If we move away from fixed annual appraisals to continuous reviews, we’ll be better able to adapt to the pace of technological advances, changes in skills and the right man/machine mix.
It’s a question of rewarding initiative-taking and active learning, and of rewarding collaboration and the transmission of knowledge, particularly around AI skills, which will become real differentiators.
From fixed rewards to systemic rewards
Traditional remuneration models are often based on rigid criteria, such as seniority, hierarchical level or qualifications.
But if we think that, under the impact of AI, we’re going to deconstruct the notion of profession by fragmenting the tasks and jobs required to achieve expected results into simple activities, analyzing the contributions of AI, then reassigning missions according to the comparative strengths of humans and intelligent systems, the whole system shatters.
Some therefore advocate moving to a systemic reward model with flexible remuneration that takes into account not only the position held, but also the skills mobilized, the results achieved and adaptability to new technologies (Introducing The Systemic HR™ Initiative).
In this model, employees who deepen their mastery of AI, integrate it into their missions and increase not the quantity of work provided but its impact can thus claim faster salary progression and specific benefits.
Obviously, this won’t suit or please everyone, but it’s in line with the reality of tomorrow’s work as it’s taking shape and, in a way, it’s already a fact, since the market in some cases gives a salary “premium” of $45,000 to AI-savvy employees(Why your company is struggling to scale up generative AI).
This is an opportunity to reiterate an old saying that has perhaps never been truer: the employee will become an entrepreneur of himself.
Rewarding beyond salary
Financial remuneration, whatever anyone says, will remain central, but it may no longer be the only lever of attractiveness and motivation. AI may ultimately transform the expectations of employees, who will seek more varied forms of recognition that will help maintain their employability in a context of race against the machines.
Privileged access to training and skills upgrading may become a much more important motivating factor than it is today. Career opportunities will no longer be determined by pre-established career paths, but by the acquisition and application of new, directly operational skills.
Increased flexibility in work organization and greater autonomy will also be sought, but this is only logical: what was sometimes the prerogative of managers due to the autonomy attached to their function will begin to concern employees who become AI managers and will see the nature of their work change.
Bottom line
If businesses adapt, AI will not be a threat to employment, but rather a catalyst for the development of skills and careers, even if this will require a more entrepreneurial approach on the part of employees, provided this is recognized by the company.
If these replace rigid models with more dynamic approaches, they will not only foster employee engagement and retention, but above all a culture of innovation and continuous learning.
Image: Image : incentive by Lightspring via Shuterstock.
