As a provider of Human Capital services, the subject of Artificial Intelligence (AI) and its impact on the world of work continues to attract significant attention.
This conversation is unfolding in several ways:
- Organisations are positioning AI as a productivity enhancer while simultaneously discussing potential layoffs
- Entry-level employees are finding it increasingly difficult to access roles now being outsourced to AI agents
- Even specialised industries such as software development are seeing aspects of work automated
- Employees themselves are not helping matters when they rely on AI outputs without proper verification
This includes recent examples involving lawyers, global consulting firms and even contributors to South Africa’s draft National Artificial Intelligence policy.
Against this backdrop, we were particularly interested in a recent article published on PressPortal by Dr Chris Blair, titled:
“If AI is doing the work, should we still pay for the results?”
The article raises important questions around AI in the workplace, remuneration structures and performance-based pay in an AI-driven economy.
We have republished and explored the key arguments below and would welcome your thoughts on the discussion.
AI in the Workplace and the Future of Performance Pay
This article by Dr Chris Blair forms part of a broader exploration into leadership, work and the human consequences of technological change.
While much of the discussion around AI has focused on productivity gains, the article argues that the fundamentals of effective reward design remain largely unchanged.
In an era of hybrid intelligence, organisations should continue rewarding delivered results and the human initiative that harnesses technology effectively, rather than attempting to separate machine contribution from human contribution too precisely.
Recent research strongly supports this position.
The PwC Global AI Jobs Barometer (2025) found that AI-exposed industries experienced productivity growth nearly four times faster following the rise of generative AI. Wages in these sectors also grew twice as quickly as in industries with lower AI exposure.
Workers with AI-related skills reportedly commanded an average 56% wage premium in 2024 — a sharp increase from 25% the previous year.
The market, in other words, continues rewarding those who identify, adopt and utilise technology effectively.
The comparison is not entirely new. The consultant who mastered Excel modelling in the late 1990s was not penalised because spreadsheet software handled calculations more efficiently. They were rewarded for delivering stronger outputs and better commercial outcomes.
The same principle arguably applies to AI in the workplace today.
Should Organisations Shift From “Pay for Results” to “Pay for Contribution”?
One of the central questions raised is whether organisations should move away from traditional “pay for results” structures toward systems focused on “responsible contribution”.
At first glance, the concept appears philosophically appealing.
In practice, however, it introduces considerable complexity into remuneration systems already under pressure to remain agile and measurable.
Traditional pay-for-performance models rely on a relatively straightforward relationship between effort, output and business value.
AI does not necessarily break that relationship. It simply makes contribution harder to interpret.
The article argues that organisations should respond by sharpening accountability and performance expectations rather than attempting to calculate precisely how much work AI completed versus the employee.
This thinking is already reflected in executive remuneration structures.
Companies such as Microsoft and Salesforce have incorporated AI-related strategic objectives into incentive plans.
Importantly, these incentives remain tied to measurable business outcomes, including:
- Revenue growth
- Platform adoption
- Productivity gains
- Innovation metrics
The focus remains firmly on results.
Adjusting KPIs for AI-Augmented Productivity
Rather than trying to separate human and machine contribution in every workflow, a more practical approach may involve recalibrating key performance indicators (KPIs) to reflect the productivity gains enabled by AI.
As AI tools improve efficiency, organisations can reasonably raise performance expectations and output benchmarks.
For example:
- Sales targets may increase
- Report turnaround times may shorten
- Code production benchmarks may rise
- Analytical output expectations may expand
Employees who leverage AI effectively will naturally outperform peers and earn stronger variable pay outcomes.
Those who resist adoption may struggle to meet evolving standards.
This approach preserves the integrity of performance-based remuneration while recognising the realities of AI-enabled productivity.
Importantly, it also avoids creating governance systems focused on microscopic attribution analysis.
The Equity Debate Around AI Access
Another concern raised in discussions around AI in the workplace relates to uneven access to technology.
Critics argue that employees with better access to AI tools may gain unfair advantages.
The article takes a different view.
In high-performance environments, the ability to identify, adopt and utilise new tools has always been viewed as a demonstration of initiative and adaptability.
Rather than weakening rewards for high performers, organisations may achieve better outcomes by:
- Expanding AI access
- Improving training
- Building broader organisational capability
Where disparities genuinely exist — particularly at operational or junior levels — the appropriate response is capability development, not reducing incentives for those who adapt successfully.
This aligns with broader Human Capital trends already visible across many sectors.
Variable Pay Design in the AI Era
Modern remuneration practices are already evolving alongside AI adoption.
Many organisations now use AI tools internally for:
- Pay equity analysis
- Benchmarking
- Merit planning
- Incentive modelling
At the same time, some businesses are incorporating AI fluency or AI integration metrics into performance scorecards.
Examples include:
- Forecast accuracy improvements
- AI-driven revenue outcomes
- Productivity enhancement metrics
Crucially, these measures generally supplement traditional performance metrics rather than replace them.
Research also suggests that performance-based remuneration can improve how employees use AI tools, leading to stronger decision-making and better outcomes overall.
This reinforces the argument that organisations should evolve existing reward systems rather than abandon them.
What Boards and Remuneration Committees Should Focus On
The article argues that boards and remuneration committees should continue focusing on accountability, value creation and strategic execution.
Key questions include:
- Have we delivered measurable results?
- Are we rewarding employees with skills relevant to hybrid intelligence environments?
- Does the reward structure encourage innovation while maintaining accountability?
These questions keep governance frameworks grounded in commercial outcomes rather than overly theoretical debates around attribution.
The reality is that measuring the exact division of labour between human and machine contribution remains extremely difficult.
Organisations may achieve stronger outcomes by focusing on performance delivery and explainability instead.
Responsible Reward Governance in an AI-Driven Workplace
The article proposes four principles for modern reward governance:
Outcome Primacy With Process Transparency
Reward measurable business value while encouraging open discussion around methods and AI usage.
Access and Adoption Equity
Ensure employees in comparable roles have fair access to tools and training.
Accountability at Every Level
Maintain clear ownership of outcomes, particularly in areas involving ethics, judgement and risk.
Defensibility Through Results
Ensure remuneration decisions remain justifiable based on measurable impact rather than inputs alone.
These principles build on existing performance-based remuneration frameworks rather than replacing them.
Keeping Human Value Visible in the Age of AI
At its core, effective reward design still signals what organisations value most:
- Results
- Accountability
- Initiative
- Adaptability
AI can improve speed, efficiency and output quality, but it cannot assume ultimate responsibility for outcomes or replace human judgement entirely.
The larger challenge for organisations is not perfectly separating machine contribution from human contribution.
The challenge is ensuring reward systems continue motivating the human ingenuity required to direct technology toward meaningful commercial outcomes.
By remaining anchored in pay-for-results principles, organisations can maintain reward integrity while encouraging the adaptability needed to compete in increasingly AI-driven markets.
What This Means for Employers and Human Capital Teams
As AI adoption accelerates, organisations are increasingly reassessing workforce planning, productivity expectations and remuneration structures.
For many businesses, the challenge lies in balancing innovation with fairness, accountability and employee engagement.
At Decusatio Human Capital Solutions, we continue engaging with employers around workforce strategy, performance management and the broader impact of AI in the workplace.
The intersection between technology, productivity and Human Capital strategy is likely to remain one of the defining workplace conversations of the next decade.
We would welcome your thoughts on how organisations should approach remuneration and performance in the AI era.
Navigating AI in the Workplace Requires More Than Technology
As Artificial Intelligence continues reshaping the workplace, many organisations are grappling with difficult questions around productivity, workforce planning, remuneration, employee wellbeing and long-term talent development.
The challenge is not simply adopting AI tools; it is understanding how to integrate them into a Human Capital strategy that remains sustainable, accountable and people-focused.
At Decusatio Human Capital Solutions, we work with growing businesses to help navigate these changing workplace dynamics through services including outsourced HR support, workforce planning, recruitment, performance management, payroll support and broader Human Capital advisory solutions.
Whether your business is reviewing performance structures, adapting to AI-driven productivity shifts or trying to future-proof your workforce strategy, our team can help you develop practical solutions aligned with your operational realities and growth objectives.
If you would like to discuss how AI and evolving workplace trends may impact your business, reach out to the Decusatio Human Capital Solutions team here.