The Impact of Artificial Intelligence on UAE Employees: Work Relief or Increased Burden?
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Khaleej Times
www.khaleejtimes.com

The Impact of Artificial Intelligence on UAE Employees: Work Relief or Increased Burden?

Artificial intelligence (AI) offers employees new opportunities to accelerate work, but for many specialists in the United Arab Emirates (UAE), the saved time does not always lead to a reduction in workload.

Despite 76 percent of workers in the UAE expressing enthusiasm for the changes AI will bring, compared to 65 percent globally, and 72 percent noting more opportunities for innovation than a year ago, the pressure to complete a greater volume of tasks continues to rise.

According to the Korn Ferry UAE Workforce Report 2026, based on the opinions of 500 professionals in the country, 70 percent of employees report a significant increase in their workload over the past two years. These results highlight a growing dilemma in the workplace: as AI automates routine tasks and opens up opportunities for innovation, are employees benefiting from increased efficiency, or are they simply expected to do more?

Najiba Suleiman Abubaker, Senior Manager of HR and Culture at Hotpack, believes that companies must ensure that the implementation of AI does not automatically lead to an increase in employee responsibilities. She notes that AI can simplify work by handling repetitive and laborious tasks, allowing specialists to focus on problem-solving, making better decisions, and performing meaningful tasks. However, she emphasizes that saved time should not automatically mean additional duties, and organizations must find a balance between efficiency and staff well-being.

According to her, HR departments need to collaborate closely with business leaders to assess the quality of work, set realistic expectations, and analyze workloads, rather than focusing solely on final results. Furthermore, managers require training to fairly distribute tasks and evaluate performance amid changing job roles due to AI. Employees must also understand how to use this technology responsibly, especially when dealing with confidential data.

Abubaker concludes that regular feedback helps HR determine whether the technology is reducing pressure or, conversely, intensifying it. Ultimately, AI should improve the human work process itself, not just the volume of that work.

Aws Ismail, co-founder of Onyx AI Solutions, believes the problem often lies not in the technology itself, but in how companies utilize the saved time. He states plainly that many companies simply fill the freed-up time with additional work, which is a management issue, not an AI one. If a person saves two hours and is then given two new assignments, nothing improves.

Ismail advises organizations to use AI to eliminate tedious and repetitive duties so that employees can concentrate on tasks requiring human judgment and interaction. For example, at Onyx, an AI recruiter handles initial call screening, allowing recruiters to spend more time communicating with candidates and stakeholders. In his view, the goal should be to increase productivity, not just efficiency and output volume.

He also believes that performance evaluation methods must change. When employees are evaluated primarily on the quantity of work completed, AI can only increase the number of tasks expected of them. Instead, they should measure the value they bring, and he believes HR should lead this process. Teams need to be asked what part of their work causes fatigue, and that should be handed over to AI first.

Ismail asserts that businesses must decide for themselves how the saved time will be used—to improve work quality, develop new skills, or establish a more sustainable pace. He stresses that AI represents a huge opportunity to improve work, but only if companies allow their employees to feel this benefit and genuinely enjoy using the technology, rather than just focusing on pure profit.

Shabil Ummer, HR Director at V Group International, points out that AI has helped employees regain time, not increase their workload. He argues that this only happens when one principle is followed: we must use AI, not let AI use us. Across retail, wholesale, e-commerce, and support divisions, company employees use AI for document drafting, data analysis, and report preparation. Tasks such as handling first-level customer inquiries, which previously took hours, are now done in minutes.

Ummer notes that the freed-up time allows workers to focus on customers, teamwork, and business problems requiring human judgment. AI also helps managers distribute project responsibilities more fairly according to employee capabilities. He believes organizations need to rethink the concept of a productive workday as AI integrates into daily operations. He insists that companies must change daily expectations, but the metric must remain work quality, not its quantity. A good day is defined by the value created, not the number of tasks completed.

For HR departments, the task is to ensure that increased efficiency translates into tangible benefits for employees, not just a new set of goals. In Ummer's view, HR's role is to set boundaries so that the time saved by AI is directed towards improving work, training, and customer service, rather than simply completing more tasks.

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Despite the undeniable influence of artificial intelligence (AI) on modern society, there remains a sharp debate over whether its overall impact is positive or negative. Just a year or two ago, the internet was flooded with AI-generated images that quickly entered corporate advertising. Soon after, social media platforms like YouTube were filled with AI-generated thumbnails—visuals that initially attracted viewer interest but now increasingly cause audience fatigue and skepticism.

However, outside of consumer social media feeds, artificial intelligence holds significant potential to transform corporate operations, provided it is implemented strategically. In an exclusive interview with IOL, Paul Banting, co-founder of growthguru.ai, analyzed the common narrative that AI has become an overrated tool that does not deliver expected productivity growth.

Banting stated that the problem is not that AI is overrated, but that it is being judged against the wrong promise. He emphasized that productivity gains are real and supported by research. According to a McKinsey survey on AI adoption in corporations, 80% of respondents noted improved individual productivity, yet only 37% claimed that AI directly contributed to increased profit for their organization—a metric that has remained virtually unchanged over the past year.

Therefore, in Banting's view, it is more accurate to say that AI is not overrated, but fundamentally misunderstood. Leaders mistakenly assume that saved time automatically translates into revenue growth. In reality, companies must actively direct the hours freed up by AI toward achieving tangible, high-return commercial opportunities.

While most public attention naturally focuses on optimizing outdated workflows, Banting warns that task efficiency should not become the primary goal of the enterprise. Around 79% of executives expect AI to drive substantial revenue growth by 2030, compared to 40% today. Nevertheless, only 24% have a clear idea of where this future revenue will originate. The true objective, Banting explained, is using AI to identify urgent commercial gaps and capture high-value market opportunities ahead of competitors.

The Growthguru.ai platform was developed precisely to bridge this strategic gap, focusing on business development rather than general workflow automation. Instead of offering a single universal assistant, their platform utilizes specialized AI agents targeting specific commercial levers: Perky helps partners access relevant incentives, discounts, and co-financing from suppliers; Maya develops go-to-market strategies and co-marketing initiatives; Alex guides partners through certifications and supplier tiers; and Lexi tracks important enterprise updates, such as critical Microsoft platform releases.

Nevertheless, alongside the commercial potential lies a darker side—AI-enabled crime, where issues of accountability and legal liability remain unresolved. Banting argues that criminals using AI for financial fraud must be held responsible for their actions, regardless of the tools they employ. However, if proprietary or third-party enterprise AI causes operational or financial damage, the responsibility lies directly with the business that implemented it. Legal liability cannot simply be shifted to the model provider. While software developers must create secure and compliant architectures, the choice of implementation rests entirely with the enterprise. BCG confirmed this finding in its applied AI benchmark.

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Amazon is reassessing how it measures the use of artificial intelligence by its software engineers. According to a new internal guideline, the company considers metrics such as the amount of tokens used, tool adoption, and volume of code modifications to be 'deeply flawed' indicators when set as objectives.

Despite this change, the company does not intend to abandon AI. The focus of the alteration lies in the evaluation: instead of rewarding those who use more tools or generate a higher volume of code, Amazon seeks to measure the effect of the work on customers, businesses, and teams.

One of the identified challenges is the phenomenon known as 'tokenmaxxing,' a term used to describe the excessive use of tokens with the aim of inflating internal indicators. Tokens represent the units that models use to process and generate data, and they also affect the operational costs of these tools.

As reported by Fortune, Amazon warns that turning this consumption into a goal can result in increased expenses without guaranteeing a real improvement in results. This problem is not merely theoretical; company employees have even used AI agents in superfluous activities just to boost their numbers in an internal ranking that was discontinued in May.

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The guidance also applies to the opposite: Terkowitz considers valuable the discovery of areas where AI is not being effective. The company maintains the incentive for engineers to learn and integrate AI into their daily routines, believing that the technology will remain central to software development, alongside knowledge, technical experience, and the ability to judge whether a specific solution is truly relevant.

This discussion transcends the limits of the company itself. A recent study showed that only 3% of organizations managed to exceed their savings targets through productivity gains provided by AI, even with most planning to expand their investments in this technology.

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