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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchAI could eliminate some tasks and reshape some jobs in Pakistan, while helping workers and businesses produce more in others. A LUMS conference panel captured both sides of that debate, but it did not measure nationwide job losses. The available figures offer broader context, not a Pakistan-specific forecast: the outcome will depend on which tasks employers automate, how workplaces change, and who can access skills and technology.
What the LUMS panel debated
At the “AI & the Human Condition” session during the 13th Asian Management Research Conference (AMRC 2026), organized by LUMS’s Suleman Dawood School of Business, Dr Muhammad Adeel Zaffar, the school’s dean, chaired a panel featuring Aamer Ejaz of JazzWorld, Ali Farid of the Securities and Exchange Commission of Pakistan, Asif Akram of Systems Limited, and Dr Maurizio Sobrero of UAE University, according to TechJuice’s event report.
The case for job displacement
The report attributes a replacement warning to Ali Farid, identified as an SECP commissioner. It says he pointed to financial research and automated negotiations as areas where AI could replace roles and urged academic institutions to teach emotional resilience. The report does not supply transcripts, identify the underlying studies, or quantify Pakistani job outcomes, so this is a speaker’s warning rather than measured evidence of displacement.
The case for jobs changing
Asif Akram, identified in the report as Systems Limited COO, argued for companies to invest in reskilling and redesigning processes. That view focuses on how work may be reorganized: AI could take over parts of a job while people remain responsible for other tasks. Whether such a transition preserves jobs depends on how employers implement the technology and whether workers can move into the changed roles.
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The opportunity and governance arguments
The report attributes to Yasser Bashir an argument that Pakistan should move beyond acting mainly as a service provider and develop homegrown AI solutions through research, startups, and university-industry collaboration. Aamer Ejaz is reported as emphasizing mathematics, science, and coding, as well as teaching students to ask the “right questions.” Panelists also discussed ethical frameworks, liability systems, and professional certifications for high-stakes fields such as healthcare, law, and autonomous mobility. These are reported proposals and priorities, not evidence that a particular Pakistani policy or certification regime is in force.
Why AI exposure is not the same as a job loss
Several separate steps sit between AI being capable of a task and a worker losing a job. A task may be technically automatable, but an employer must choose to adopt a tool, reorganize work around it, and decide what happens to the time or labor it frees. AI may substitute for some tasks, complement others, or raise output enough to change demand for a business’s products and workers.
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- Exposure: Some tasks in an occupation overlap with what AI can do. This does not show that an employer has adopted AI.
- Adoption: A workplace uses AI in a real process. Adoption may be limited by cost, reliability, connectivity, regulation, or the need for human oversight.
- Task substitution or complementarity: AI may perform a task previously done by a person, or help that person do it faster or differently.
- Employment effect: The employer may reduce hiring, change roles, retrain staff, expand output, or combine these responses. The net effect across an economy also depends on new demand and new work.
The International Labour Organization’s 2025 research brief, Generative AI and jobs: A 2025 update, estimates that “One in four workers across the world are in an occupation with some degree of GenAI exposure, but because of the continued need for human input, most jobs will be transformed rather than made redundant.” That is a global estimate of occupational exposure, not a count of jobs already lost in Pakistan. Read the ILO brief.
What the available numbers say—and what they do not
| Measure | Reported figure | How to interpret it |
|---|---|---|
| AI-related job postings in South Asia | Lightcast’s share rose from 2.9% to 6.5% between January 2023 and March 2025; demand for AI skills grew 75% faster than demand in other postings over that period. | The World Bank says these postings disproportionately represent high-wage, urban, white-collar positions. They show a change in a slice of hiring, not demand across all workers or a Pakistan-only trend. World Bank discussion. |
| Jobs at risk of GenAI automation by country income group | The World Bank’s 2026 World Development Report launch announcement gives estimates of 4.5% in low- and middle-income countries and 14.2% in high-income countries. | These are broad income-group estimates, not Pakistan rates or predictions of net employment change. World Bank announcement. |
| Jobs with potential productivity gains in developing economies | 16.2% could see productivity meaningfully boosted by AI, according to the same 2026 announcement. | This is a potential productivity estimate, not evidence that the gains have occurred in Pakistan or will translate directly into more jobs or higher wages. World Bank announcement. |
Read together, the estimates describe different things: postings signal hiring demand for AI skills in a particular market segment, while exposure and automation estimates model overlap between work and AI capabilities. None, by itself, answers how many Pakistani workers will lose jobs or how much the country’s productivity will change.
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What will determine whether AI destroys or creates opportunity?
The disagreement at LUMS is best understood as a set of conditions, rather than a choice between two certain outcomes.
- The tasks in a role: Work made up of repeatable, information-based tasks may be easier to automate in part. Roles that rely on judgment, accountability, interaction, or physical work may change differently, though no occupation is defined by only one task.
- Adoption and workplace redesign: A tool’s capabilities do not determine how quickly firms use it. Employers decide whether to automate, augment staff, retrain workers, or redesign processes—and those choices shape the effect on jobs.
- Access to infrastructure and training: Workers and firms need access to suitable digital infrastructure, tools, and learning opportunities to benefit. Uneven access can leave some workers exposed to disruption without a clear path into new or changing work.
- Where productivity gains go: Higher output could support business growth, new services, or exports, but productivity gains do not automatically become new jobs or higher pay. Their employment impact depends on demand and how organizations distribute the gains.
- Institutions and safeguards: Rules for accountability and professional practice matter particularly when AI is used in high-stakes settings. The LUMS report raises these governance issues but does not establish the current status of specific Pakistani rules.
What Pakistani workers and employers can take from the debate
For workers, the practical question is not simply whether an occupation is “safe” from AI. It is which parts of the work may change and what skills help someone adapt. The panel report points to mathematics, science, coding, and reskilling; it also records a call for emotional resilience. Those are priorities raised by speakers, not a quantified guarantee of future employment.
For employers, the reskilling argument implies that adopting AI involves more than buying or deploying a tool. Companies also have to decide how processes change, which tasks remain human-led, and how affected employees can transition. For policymakers and educators, the panel’s emphasis on research, startups, university-industry collaboration, and safeguards frames a broader question: whether Pakistan can build local capability while preparing workers for changes already under way.
There is not yet a Pakistan-specific measured rate of AI-driven job displacement, a sourced count of AI-exposed Pakistani roles, or a causal estimate of AI’s effect on national productivity or exports in the evidence available here. The LUMS event report is useful for understanding the arguments raised at the conference; it is not a labor-market survey. Global and income-group estimates can inform the discussion, but they cannot be presented as a forecast for Pakistan.
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