Based on an exclusive virtual interview with Mohammad Abu Al-Rob, CEO of Procapita Group and founder of Zenithr, this article examines how artificial intelligence is reshaping business and the future of work.
Artificial intelligence will not create one uniform employment future. Its effects will differ between industries, occupations, companies and workers. The organisations most likely to benefit will be those that turn automation into stronger skills, better decisions and new sources of commercial value.
AI and the future of work are often discussed as a contest between machines and employees. That framing is too narrow. A more consequential divide is emerging between organisations that use AI to redesign work and those that treat it mainly as a way to reduce labour costs.
Both approaches may improve short-term efficiency. However, companies that invest in workforce capability, responsible governance and new business models are more likely to build lasting competitive advantages.
This distinction is particularly important in the Gulf. Governments and businesses across the region are investing heavily in digital infrastructure, cloud computing, data centres and artificial intelligence. The World Bank reported in December 2025 that 5G coverage exceeds 90 percent across GCC countries, giving the region a strong technological foundation for AI adoption. Infrastructure alone, however, cannot create an AI-ready economy. Skills, leadership and implementation quality will determine whether that investment produces sustainable growth.
Image credit: Zenithr | Cropped by Team GBN
AI changes tasks before jobs
Public discussion usually begins with a direct question: how many jobs will AI eliminate?
The question matters, but it can conceal how technological change actually enters the workplace. Most occupations are not single activities. They are collections of routine, analytical, interpersonal and decision-making tasks.
A finance professional may gather data, prepare reports, interpret risk and advise management. A customer-service employee may answer common questions, investigate unusual complaints and protect a client relationship. AI can automate some of these activities, accelerate others and leave the most sensitive responsibilities with people.
This means occupational exposure should not be confused with complete replacement.
One in four workers is employed in an occupation with some exposure to generative AI.
About 3.3 percent of global employment falls within the highest exposure category.
Clerical occupations remain the most exposed.
Job transformation is considered more likely than the complete disappearance of most occupations.
Most jobs still contain responsibilities that require human involvement, contextual understanding or accountability.
Transformation does not guarantee job security. A position may continue to exist while requiring fewer employees. Companies may retain current staff but reduce future recruitment. Some occupations will grow, others will decline, and many will be substantially redesigned.
The World Economic Forum estimates that economic, demographic and technological changes could create 170 million jobs and displace 92 million by 2030, producing a net gain of 78 million roles. However, the same research found that 40 percent of surveyed employers expect to reduce staff where skills become less relevant, while 50 percent plan to transfer employees from declining positions into growing ones.
A positive global employment balance does not mean that every worker, company or country will benefit equally.
Productivity is not a strategy
AI can deliver measurable improvements in performance.
Research published by the non-profit National Bureau of Economic Research examined the introduction of a generative AI assistant among 5,179 customer-support agents. Access to the system increased productivity by approximately 14 percent on average and by 34 percent among novice and lower-skilled workers. The impact on highly experienced employees was limited.
The findings indicate that AI can spread practical knowledge across an organisation. New employees can receive guidance based on successful previous interactions, improve faster and handle more difficult assignments sooner.
However, greater productivity does not automatically lead to more employment.
When a company can produce the same output in fewer working hours, it has several choices:
Expand into new markets.
Improve customer service.
Develop new products.
Increase salaries or employee benefits.
Retain the financial savings.
Reduce future recruitment or headcount.
Technology creates additional capacity. Management decides how that capacity is used.
This distinction is critical. An AI programme should not be judged only by how much time or money it saves. It should also be measured by whether it improves decisions, creates customer value and strengthens the organisation’s long-term capabilities.
Reskilling needs real roles
Image credit: Zenithr | Cropped by Team GBN
IKEA provides a useful example of how automation can be connected to workforce redesign.
Ingka Group reported that its AI-powered Billie chatbot resolved approximately 47 percent of the customer enquiries it received between 2021 and 2023. The company also supported 8,500 employees in moving towards areas including remote interior design, digital sales, relationship-building and more complex customer support.
The significance of this case is not that automation protected every role. The important point is that the company linked automation to a commercially relevant destination for reskilled workers.
Many corporate reskilling promises fail because training is offered without answering three basic questions:
Which new roles will trained employees enter?
Will the organisation fund those positions?
How will the new work create measurable value?
A certificate is not a workforce transition strategy. Reskilling becomes meaningful only when employees can move into clearly defined roles with genuine demand, responsibility and career potential.
Abu Al-Rob’s central argument is that AI should strengthen talent acquisition, assessment, development and performance management rather than remain a disconnected technology experiment. AI must solve identifiable problems and improve how organisations make decisions about people.
AI readiness comes first
A common mistake is to begin with software procurement.
Executives see competitors announcing AI projects, approve a pilot and only later decide what business problem the technology should solve. This creates expensive demonstrations that attract attention but deliver limited operational value.
A stronger approach begins with a measurable need, such as:
Reducing recruitment delays.
Improving the quality of candidate assessment.
Identifying workforce skill gaps.
Personalising employee development.
Strengthening succession planning.
Helping managers make faster decisions.
The organisation must then determine whether its data, processes, employees and governance systems are ready to support the technology.
Effective AI readiness requires five foundations:
A defined business problem: The organisation must know what outcome it wants to improve.
Reliable data: Information must be accurate, relevant, secure and legally usable.
Workforce capability: Employees need training, clear responsibilities and confidence in the system.
Human accountability: Managers must know when to accept, question or reject an AI recommendation.
Measurable results: Performance must be compared with a clear baseline rather than assumed from activity levels.
The World Economic Forum reports that 63 percent of employers consider skills gaps a major barrier to business transformation. It also found that 85 percent plan to prioritise workforce upskilling, 70 percent expect to hire people with new skills, and nearly 40 percent of current job skills could change by 2030.
These findings expose an organisational contradiction. Companies expect employees to adapt continuously, but many do not provide sufficient time, structured training or credible career pathways.
Continuous learning cannot remain an instruction directed only at workers. It must become a funded corporate responsibility.
Human oversight still matters
The rise of AI does not automatically make every human skill more valuable. Value depends on how work is designed.
Judgement, leadership, communication, creativity and emotional intelligence remain important because business decisions involve uncertainty, competing interests and consequences that cannot be reduced to statistical patterns.
Humans are still required to:
Interpret context.
Build trust.
Negotiate competing priorities.
Take ethical responsibility.
Challenge unreliable output.
Accept accountability for final decisions.
This is particularly important when AI is used in recruitment, promotion, employee assessment and performance management.
Image credit: Zenithr | Edited and designed by Team GBN
An AI system may apply the same process consistently, but consistency does not prove fairness. A biased rule can also be applied consistently. Historical data can reproduce previous discrimination, while an algorithm may identify correlation without understanding the social or organisational cause behind it.
Responsible AI requires meaningful human oversight, regular testing for unequal outcomes, clear explanations and an appeal process for people affected by errors.
Human involvement should not mean approving a recommendation after the algorithm has already made the effective decision.
The GCC skills test
The GCC has advanced digital infrastructure, strong government support and access to the investment required for rapid AI adoption. Its challenge is to convert those advantages into broad workforce capability.
A joint ILO and ESCWA report published in May 2026 found that AI could generate productivity gains and new employment opportunities across the Arab region under favourable conditions. It also warned that weaker policy responses could deepen inequality, exclude vulnerable groups and displace large numbers of workers.
The report identifies a need for capabilities ranging from basic AI literacy to advanced technical, management and governance skills.
For GCC businesses, the transition involves several regional priorities:
Supporting workforce nationalisation through genuine skills development.
Preparing both national and expatriate employees for redesigned roles.
Improving Arabic-language AI capabilities.
Testing systems across different languages, nationalities and cultural contexts.
Helping small and medium-sized enterprises access AI expertise.
Protecting workers from unfair automated decisions.
Developing managers who can lead human-AI teams.
AI adoption should not create a small layer of specialists above a workforce with limited opportunities to progress. It should expand the region’s capacity to create, manage and govern technology.
Five questions for leaders
Executives should evaluate AI programmes using more than cost savings or the number of processes automated.
Every credible programme should answer five questions:
What exact problem are we solving?
Which tasks and jobs will change?
Where will saved time and talent go?
What protects employees and customers?
What evidence will prove success?
Learning to work with AI is also becoming a fundamental professional capability. However, technical familiarity alone will not be enough.
Workers will be better positioned when they combine AI literacy with:
Industry knowledge.
Critical thinking.
Communication.
Creativity.
Ethical judgement.
Personal accountability.
The competitive divide
Image credit: Zenithr | Edited and designed by Team GBN
The future of work will be determined by choices about investment, workforce design, training, governance and the distribution of productivity gains.
Companies that use AI mainly to reduce costs may become leaner in the short term. They may also weaken employee trust, reduce internal expertise and create future talent shortages.
Organisations that use AI to redesign work, strengthen people and develop new services may become more productive and more valuable.
The decisive divide will not be between humans and machines. It will be between organisations that understand how to combine them and those that do not.
