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Rethinking performance in the human-AI workplace

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©Charles Deluvio / Unsplash Rethinking Performance in the Human-AI Workplace

Human-AI hybrid teams – where employees work alongside artificial intelligence agents – are becoming increasingly common across industries. Measuring the performance of these blended teams is crucial for corporate leaders to understand how AI impacts productivity, quality, and employee effectiveness. Yet traditional productivity metrics alone no longer suffice.

Take marketing teams, for example. An AI lead-scoring system might generate significantly more qualified prospects, but conversion rates may still drop. This reveals how focusing on the wrong metrics can obscure true performance. Executives need frameworks that capture the full picture, from speed and accuracy to employee engagement and innovation.

However, measuring performance in human-AI teams is inherently complex. Unlike traditional teams, outcomes depend on both human effort and AI assistance, making attribution difficult. There can be unpredictable interactions – recent research in radiology showed that AI assistance improved some clinicians’ diagnostic accuracy but actually worsened others’ performance. Factors like user experience, trust in the AI, and work division all influence results.

To understand success in the AI era, organisations must adopt a holistic approach to evaluation, ensuring that metrics capture not just raw output, but also the quality of collaboration and the human factors at play.

When leadership touts AI mainly as a workforce reduction tool, it’s unsurprising that employees hesitate to report AI’s full capabilities. In such environments, standard KPIs can feel like a threat, since every gain by AI seems to come at a human cost.

The human reporting bias: when relevance feels at risk

As AI systems outperform humans on traditional metrics, some employees react defensively – even dishonestly – to protect their sense of relevance. In extreme cases, workers resist by sabotaging metrics and AI tools: a recent survey found one in ten employees admitted to tampering with performance data to make AI look worse.

This behaviour often stems from fear that objective metrics will highlight AI’s superior performance and diminish the human role. It’s a modern echo of the Luddite impulse – just as textile workers once smashed machines, today some frustrated staff intentionally provide poor-quality data to AI systems or deliberately underuse them to avoid being outshone. 

When leadership touts AI mainly as a workforce reduction tool, it’s unsurprising that employees hesitate to report AI’s full capabilities. In such environments, standard KPIs can feel like a threat, since every gain by AI seems to come at a human cost.

Reframing metrics around collaboration

To counteract this bias, organisations must rethink what they measure. Rather than pitting human and machine performance directly against each other, forward-thinking managers need to introduce new metrics that value human-AI collaboration.

AI Adoption Rate is one useful metric. Tracking how readily and effectively employees integrate AI tools into their work reframes performance in terms of adaptation and learning. For instance:

By measuring openness to working with high-performing AI agents instead of competing against them, companies send a clear message: embracing AI is itself a performance strength. This shift allows employees to demonstrate their relevance in new ways. An employee who rapidly learns to leverage an AI assistant effectively can be recognised, even if the AI handles a chunk of their old tasks. 

Success measured through collaboration encourages honesty and engagement. Employees see their value reflected not just in what they do alone, but in how well they team up with AI.

Communication strategies: reassurance and inclusion

Introducing new performance measures and AI tools is a human exercise, not just a technical one. Leaders must accompany this change with clear, empathetic communication that reassures employees of their continuing value.

Frame AI as augmentation, not replacement

Consistently emphasise that AI is a tool to enhance employees’ work, not to render them obsolete. Show how AI can automate mundane tasks – data entry, routine queries – so that humans are freed to focus on higher-value creative, strategic, or interpersonal work.

Communicate early and often that the goal is to elevate human roles, not eliminate them. Highlight real success stories. For example, a team that used an AI scheduling assistant to handle logistics while team members spent more time on complex problem-solving. Such stories demonstrate that human judgment remains central to success

Involve and inform the team

Fear thrives in silence. Be transparent about AI adoption and invite feedback. Explain why new metrics are being introduced and how they benefit both the organisation and its people.

Many workers resist change simply because they feel excluded from it. By listening and incorporating frontline input, managers demonstrate respect for employees’ knowledge – reinforcing that human insight remains valuable in guiding AI deployment.

Provide training and growth opportunities

Training show investment in people, not just in technology. Nearly half of employees say they want more formal AI training, viewing it as the best way to boost adoption.

Offer workshops, tutorials, or one-on-one coaching that show how to use new AI tools in daily tasks. This not only builds confidence but also sends the message that mastering AI is a path to advancement, not a dead-end. Managers should really emphasise how mastering AI tools builds transferable skills and opens new career paths.

Celebrate wins where humans and AI succeed together. The narrative should be one that makes employees partners in the AI journey, values their input, and calms fears with a vision of a collaborative future.

Mind the messaging from the top

Leadership must set a reassuring tone. Mixed messages – praising employees as “our greatest asset” one day, then highlighting AI-related layoffs the next – will severely undermine trust. It’s crucial that executives avoid boasting about workforce cuts attributed to AI, even hypothetically, when addressing staff.

Instead, highlight positive outcomes like improved customer service or new market opportunities created with human-AI collaboration. Celebrate wins where humans and AI succeed together. The narrative should be one that makes employees partners in the AI journey, values their input, and calms fears with a vision of a collaborative future.

Incentivising collaboration: aligning metrics and rewards

Even with the right metrics and messaging, humans need tangible incentives to fully buy into human-AI collaboration. Employees naturally ask, “What’s in it for me?” – so performance systems must reward collaboration, not penalise it. This may require reinventing both performance evaluations and compensation in the AI era.

Tie AI use to performance reviews

If AI adoption matters, it must be part of evaluations.. Some pioneering companies, like Shopify, ask employees to rate peers on how effectively they use AI tools to enhance output. By explicitly incorporating AI collaboration into reviews and 360-degree feedback, leaders make it clear that creatively leveraging AI is as valuable as communication or teamwork.

Public recognition also helps. Sharing AI usage metrics by team (e.g., announcing which departments have the most AI power users each week) encourages friendly competition and knowledge-sharing. When AI-powered productivity is celebrated, participation becomes a point of pride.

Link AI proficiency to career advancement

At For example, Amazon’s robotics division, employees must demonstrate the effective usage of AI and automation for promotion. This ensures that rising leaders are those who embrace technology, not avoid it.

Even without formal requirements, managers can fast-track those who spearhead AI pilot projects or become the go-to person for AI-driven processes. The message to all staff: working well with AI is becoming a core job skill that will be rewarded with career opportunities.

Recognise and reward human-AI team wins

Celebrate achievements that result from collaboration rather than siloed effort. Some firms now award “Best Human-AI Collaboration” projects, highlighting innovative uses of the technology..

Recognising these successes encourages a culture where using AI is seen as creative problem-solving, not cheating or doing less work. It also helps employees take pride in their augmented accomplishments. When employees see that AI adoption leads to recognition, advancement and collective success, genuine engagement follows.

The self-reinforcing cycle

In practice, aligning incentives might involve some trial and error, but the core principle is straightforward: make the human-AI partnership pay off for the human so that employees have every reason to report accurately on AI’s performance and to leverage it to the fullest.

Over time, this creates a self-reinforcing cycle – honest data on AI benefits drives further adoption, which leads to greater productivity gains that everyone shares. By updating performance measures and incentive systems to fit the AI-enhanced workplace, managers can inspire genuine human-AI synergy.

Organisations that get this right will see AI adoption accelerate in a healthy way, with employees fully on board – not just because they have to, but because it genuinely advances their standing and success within the company.

Checklist for Managers: Building AI-ready performance systems

Month 1: Foundation

Month 2: Measurement

Month 3: Incentives

Ongoing

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