The world has grown increasingly concerned about artificial intelligence (AI) taking over human jobs since ChatGPT launched in 2022. That is because it can understand natural human language (written and spoken), search the internet for data, generate narratives from queries, solve mathematical problems, and make forecasts.  

 Those fears are partially founded. “While 92 million jobs might be eliminated by 2030, 170 million new roles will be created because of AI,” the World Economic Forum (WEF) said in February.  

 Additionally, many existing professions will change. “Over the next two to three years, 50% to 55% of jobs … will be reshaped by AI,” according to the Boston Consulting Group in April. “For many employees, this will mean that they retain the same or similar roles, but face radically new expectations for how they work and what they produce.”    

 For youth in low- and middle-income nations pursuing jobs in wealthy, developed nations, knowing which sectors AI will transform while still needing a human workforce could be the key to lucrative careers.  

Healthcare 

AI is already used in healthcare for early symptom detection, conversing with patients to identify illnesses, interpreting medical tests and scans, handling administrative work, and even planning ambulance routes, according to a WEF paper from August. 

AI is increasingly used in commercial medical devices, including those measuring blood sugar, blood pressure, and heart rate. “People who are generally healthy can use self-monitoring devices to optimize their mental and physical health,” the WEF noted. “Those with health issues will have access to a wide range of digital solutions.” 

However, it’s highly risky for AI to make treatment decisions without human oversight. “AI could potentially speed up [medical tests] … it could also provide unreliable or biased information,” said the WEF. As a result, human oversight is essential. 

The integration of AI will inevitably create new roles. First will be clinical AI engineers who “develop and deploy AI solutions … to improve patient care,” noted Bitesize Career, an educational career resource platform, in its 2026 survey. Next will be “medical device engineers, who come up with new technology [with the help of AI] to practically help patients by diagnosing, monitoring or treating them.” 

Also expected to be in high demand are “medical imaging specialists, qualitative health researchers, health data scientists, healthcare software developers, and professors or lecturers in AI and health,” the survey noted.  

Finance and accounting 

Finance, accounting, and other regulated, math-based jobs are ideal for AI. Farseer, a developer of financial forecasting software, explained, “AI won’t take over the finance function, but it will change who stays relevant. [AI] isn’t about losing jobs. It’s about losing tasks.” 

The jobs that will disappear “involve manual reporting, reconciliations, or variance analysis,” Farseer stressed. “Someone with better tools is already moving faster than you.” That is because AI “will solve problems better, spot risks sooner, and deliver more value to the business.” 

Examples of such roles include “entry-level and transaction-based, [such as] bookkeepers … junior accountants, [and] data entry and reporting support,” Farseer noted. 

Meanwhile, “demand is growing for finance professionals who can work with AI, ask better questions, and explain the story behind the numbers, [including] reviewing results, validating assumptions, and making recommendations,” the paper said. These tasks will create new roles within finance departments, including AI financial compliance auditors, algorithmic financial risk managers, and financial machine learning operations engineers. 

In the short term, “AI can’t fully replace finance jobs,” said Farseer. “It can’t apply judgment in unclear or unfamiliar situations; it struggles with unstructured data and open-ended business questions; it doesn’t understand ethics, relationships, or reputational risk; it can generate wrong or misleading results, especially with GenAI.” 

An example of a typical AI error is it “might flag something as a risk that’s perfectly normal or miss something critical because the pattern looks ‘safe,’ [as it can] misclassify transactions or overlook context,” Farseer noted.  

Tech and services 

Involving humans from the early stages of AI development through continual oversight is crucial “to ensure accuracy, safety, accountability or ethical decision-making,” according to an IBM note. “Machine learning has made astonishing strides in recent years, but even the most advanced deep learning models can struggle with ambiguity, bias or edge cases that deviate from their training data. Human feedback can help both improve models and serve as a safeguard when AI systems perform at insufficient levels.” 

IBM calls that hybrid model human-in-the-loop (HITL). It “allows AI systems to achieve the efficiency of automation without sacrificing the precision, nuance and ethical reasoning of human oversight.” 

WorkOS, a software developer, noted AI systems still suffer from “hallucinations [where] models can generate confident but incorrect or entirely fabricated information” and AI algorithms also can become biased if training data lacks diverse, comprehensive real-world data. In addition, AI systems “operating over long sessions or workflows can gradually drift from the user’s original intent.” Lastly, “ethical, legal, and safety concerns” arise when AI systems are unsupervised by humans, as they often violate national or corporate data privacy laws. 

Invariably, integrating humans within the AI system itself is spawning new roles, said Hirint, a hiring and HR digital platform. The list includes prompt engineers who converse with AI to generate desired results; data analysts who ensure no bias in AI training data; robotics engineers, primarily in factories that rely on AI; and AI trainers, legal experts and business development managers.  

Industry, logistics, supply chains 

AI is already being integrated into the production and movement of goods. In January, Bloomberg reported industry-grade AI use, particularly in supply chains and logistics, boomed to tackle global logistics bottlenecks after COVID-19 lockdowns. 

Another reason AI is widely favored is it reduces transportation costs. “The cost of goods moved is sometimes 30% or 40% of the cost of operation. [This] is prohibitive [when] scaling,” Lior Ron, COO at Waabi, a developer of AI self-driving vehicles, told Bloomberg TV in January. 

Another advantage is AI systems can “speak” to each other, ensuring products move from factories to markets with maximum efficiency. “If you reduce the friction in the system on manufacturing, distribution, and moving those goods around, you have unlocked … new economic opportunities,” said Ron. The result: greater manufacturing strength, predictability, and resilience. 

This transformation requires humans to oversee “the orchestration layer, where transverse [AI systems] coordinate decision-making and execution across functions,” said Paco Ribagnac of Capgemini Invent, the global digital innovation, consulting, and transformation brand of the Capgemini Group. 

Human supervision will also be essential in “The semantic layer where agents access the enterprise context they need to reason and act in alignment with how the organization actually operates,” Ribagnac noted. 

Other hires will supervise “The control panel where humans govern autonomy through guardrails and policies,” as well as “The interaction layer where humans engage with AI agents to oversee, steer, and refine their recommendations.” 

On the back foot? 

Low- and middle-income countries have yet to develop HITL AI models. “In most developing countries … the human layer; the expertise, accountability and institutional capacity that allow AI to be deployed in ways that work and can be trusted … barely exists,” a WEF paper said. “Its absence is both the central obstacle to AI diffusion and the most significant job-creation opportunity of the next decade.” 

That puts the Egyptian government under increased pressure to invest in developing HITL AI models across industries or risk an increasing brain drain in coming years. “AI does not wait for national strategies or dedicated legislation to be in place,” said the WEF paper. “It enters countries through software updates, digital transformation initiatives, vendor platforms, cloud services, procurement decisions, and existing institutional workflows.”