As we move through 2025, the pace of technological advancement continues to accelerate, reshaping our professional landscapes in profound ways. Artificial intelligence, once considered a distant technological frontier, has become deeply integrated into business operations across sectors, automating processes and augmenting human capabilities.
But this rapid evolution prompts an important question: Which roles might disappear from our workforce within the next decade? The conversation around AI-driven job displacement often fluctuates between alarmist predictions and overly optimistic dismissals. The reality, as with most significant transitions, likely falls somewhere in between.
Let's explore the careers most vulnerable to AI disruption by 2035, the nuances of this transformation, and how both individuals and organisations can navigate this shifting terrain.
Positions centred around routine data handling and administrative tasks stand at the forefront of AI disruption. Roles such as data entry specialists, filing clerks, and administrative assistants are increasingly seeing their core responsibilities automated through intelligent systems capable of processing, categorising, and retrieving information with minimal human oversight.
The algorithms underpinning these capabilities have become remarkably sophisticated, handling exceptions and edge cases that previously required human intervention. This evolution doesn't merely represent incremental improvement but a fundamental shift in how organisations manage information workflows.
The customer service landscape is undergoing a significant transformation driven by increasingly sophisticated AI solutions. Basic customer support functions: processing standard queries, handling routine complaints, and providing product information are now being managed by virtual assistants and chatbots that deliver consistent, 24/7 service across multiple languages.
These systems have evolved beyond simple decision trees to incorporate natural language processing capabilities that understand context, sentiment, and even customer history. The technology now enables personalised interactions that adapt to customer needs while maintaining consistency across thousands of simultaneous conversations.
However, complex problem-solving and emotionally nuanced customer interactions will likely continue requiring human touch, shifting the role of customer service professionals toward higher-value interventions rather than eliminating the function entirely.
Roles such as bank tellers, loan processors, and certain financial analysts face significant disruption from AI systems capable of handling transactions, assessing standard applications, and generating financial reports with increasing autonomy.
AI-driven financial platforms can now analyse vast datasets, identify patterns, and make predictions with accuracy levels that rival or exceed human performance in standardised scenarios. Automated advisors are already managing investment portfolios, processing loan applications, and providing basic financial guidance across various complexity levels.
This technology impact extends beyond retail banking to areas including insurance underwriting, claims processing, and financial compliance monitoring - functions traditionally requiring significant human resources.
The field of language services has been revolutionised by neural network-based translation and transcription technologies. These systems now approach or occasionally surpass human-level accuracy in many language pairs, with real-time processing capabilities that were unimaginable just years ago.
This advancement particularly impacts roles focused exclusively on straightforward translation or transcription tasks. The technology now handles idioms, cultural references, and technical terminology with increasing sophistication, reducing the demand for professionals handling routine language conversion work.
However, highly specialised fields requiring deep subject matter expertise—legal, medical, or technical translation—may continue requiring human oversight, though the role will increasingly shift toward editing and quality assurance rather than primary translation.
Entry-level coding positions face disruption through the emergence of sophisticated code generation systems and low-code/no-code platforms. These technologies enable non-technical users to create applications and websites through intuitive interfaces, significantly reducing the demand for basic development skills.
AI coding assistants have progressed beyond simple autocompletion to generating entire functions, optimising code performance, and identifying potential vulnerabilities with minimal human direction. These capabilities are reshaping the early career stages in software development, potentially eliminating traditional entry points into the profession.
The implications extend beyond individual careers to potentially restructuring the entire software development pipeline, with AI systems handling increasing portions of implementation work while human developers focus on architecture, requirements definition, and system design.
The retail sector continues its steady progression toward automated checkout solutions, with self-service kiosks becoming increasingly prevalent across various retail environments. This trend is accelerating with the implementation of more advanced technologies, including computer vision systems that can automatically identify products without scanning, and seamless payment processing through digital wallets and biometric verification.
Amazon's "Just Walk Out" technology and similar solutions from competitors demonstrate the feasibility of completely cashier-less shopping experiences, where customers simply select their items and leave, with payment processed automatically. As these systems become more cost-effective and reliable, the traditional checkout role faces significant pressure.
Bookkeeping, data entry, tax preparation, and routine financial reporting have been steadily automated through increasingly intelligent accounting software. Modern systems not only record transactions but can categorise expenses, reconcile accounts, flag discrepancies, and generate comprehensive reports with minimal human intervention.
AI-enhanced accounting platforms now interpret financial regulations, apply appropriate treatments to transactions, and even optimise for tax efficiency within compliance boundaries. These capabilities directly impact roles focused on transaction processing and basic compliance functions.
The accounting profession isn't disappearing but transforming, with emphasis shifting toward advisory services, strategic financial planning, and complex regulatory interpretation. Areas where human judgment and contextual understanding remain valuable.
Manufacturing continues its decades-long automation journey, with robotics becoming increasingly versatile, cost-effective, and intelligent. Modern industrial robots incorporate sophisticated vision systems, advanced tactile capabilities, and machine learning algorithms that enable them to handle increasingly complex assembly tasks with precision and consistency.
Collaborative robots (cobots) are expanding automation's reach into areas previously considered too variable or delicate for machines. These systems work alongside humans but progressively assume more responsibilities as their capabilities evolve. The trend is particularly pronounced in industries with standardised production processes, high volumes, and predictable workflows.
While full autonomous driving in all conditions remains challenging, steady progress in this field suggests that many professional driving roles including long-haul trucking, taxi services, and delivery driving will face significant disruption within the decade.
Self-driving technology continues advancing through a combination of improved sensors, more sophisticated decision-making algorithms, and enhanced mapping systems. These developments are creating viable autonomous solutions for controlled environments and specific routes, gradually expanding to more complex scenarios.
The transition will likely begin with limited automation in ideal conditions (highway driving, predetermined routes, controlled environments) before expanding to more complex urban settings. This phased approach means displacement will occur gradually rather than suddenly, but the trajectory appears clear.
Junior legal positions focused on document review, standard contract generation, and legal research face disruption from increasingly capable legal AI systems. These technologies can now process thousands of documents, identify relevant precedents, and draft standard legal instruments with efficiency that surpasses human capabilities in certain contexts.
Legal AI has progressed beyond simple keyword matching to understanding legal concepts, identifying relevant case law, and assessing document relevance with nuanced comprehension. These capabilities directly impact roles traditionally filled by junior lawyers and paralegals, particularly in document-intensive practices like corporate law and due diligence.
The legal profession will persist, but its structure may change significantly, with technology handling routine matters while human lawyers focus on strategy, negotiation, courtroom advocacy, and other aspects requiring judgment and persuasion.
While these predictions identify vulnerable occupations, several important qualifications must be considered:
Many roles won't disappear entirely but will undergo significant transformation. As AI handles routine aspects, human professionals may shift toward higher-value activities requiring judgment, creativity, and interpersonal skills. The customer service representative of 2035 might focus exclusively on complex problem-solving and relationship management, with AI handling all standard interactions.
Historical technology transitions have consistently created new jobs even as they eliminated others. The AI revolution will likely generate roles we cannot yet envision, from AI trainers and ethics specialists to new categories of creative and technical positions. The net impact on employment depends significantly on these new opportunities and society's ability to prepare workers for them.
The transformation will progress at different rates globally, influenced by labour costs, regulatory frameworks, and technological infrastructure. Markets with higher labour costs and fewer regulatory barriers will likely see faster adoption of automation solutions, creating a varied global landscape even within the same industries.
Many professions will evolve toward collaborative models where AI handles specific components while humans provide oversight, creativity, and judgment. This partnership approach may preserve aspects of traditional roles while fundamentally changing how work is performed.
Skills Diversification: Developing capabilities across multiple domains creates resilience against automation. Particularly valuable are skills combining technical understanding with domain expertise and social capabilities.
Focus on Distinctly Human Strengths: Cultivating abilities that remain challenging for AI such as creative thinking, complex problem-solving, ethical reasoning, and emotional intelligence. This provides some insulation against displacement.
Continuous Learning: Establishing habits of ongoing education and skills development becomes essential in a rapidly evolving landscape. This includes both formal education and self-directed learning.
Career Flexibility: Maintaining adaptability in career planning, including potential transitions between adjacent fields, creates options as specific roles evolve or diminish.
Strategic Workforce Planning: Developing long-term perspectives on skill requirements and potential automation impacts enables proactive management of workforce transitions.
Responsible Transition Approaches: Implementing thoughtful retraining programs and creating pathways for employees to move into emerging roles demonstrates ethical leadership while preserving institutional knowledge.
Human-AI Integration Strategies: Designing workflows that effectively combine human and artificial intelligence capabilities maximises the strengths of both while creating sustainable roles within the organisation.
Culture of Innovation: Fostering environments where employees actively participate in reimagining processes and identifying new value creation opportunities creates resilience against disruption.
The transformation we're discussing exists within larger economic, social, and political contexts. Policy choices regarding education, social safety nets, and technology regulation will significantly influence how these changes affect workers and communities.
History suggests that technological revolutions ultimately create more prosperity and opportunity, but the transition period can involve significant disruption for affected individuals and communities. Thoughtful approaches from policymakers, business leaders, and educational institutions can help ensure that the benefits of AI advancement are broadly shared.
For technology leaders, these developments present both challenges and opportunities. The organisations that thrive will be those that thoughtfully integrate AI capabilities while creating environments where human talent can focus on high-value activities that technology cannot replicate.
This requires a nuanced understanding of both technological possibilities and human capabilities, precisely the balance that distinguished technology executives must strike. The leaders who successfully navigate this transition will be those who approach it not merely as a cost-saving opportunity but as a fundamental reimagining of how work is performed and value is created.
Rather than viewing AI solely through the lens of replacement, forward-thinking executives are exploring how these technologies can augment human capabilities, create new service offerings, and solve previously intractable problems.
The AI revolution represents one of the most significant transformations in how we work since the Industrial Revolution. While certain roles face substantial disruption, this transition also creates unprecedented opportunities for innovation, productivity enhancement, and new forms of value creation.
For individuals and organisations navigating this landscape, the key lies not in resisting change but in proactively adapting to it, developing the skills, structures, and mindsets that will remain valuable in an increasingly automated world. By focusing on distinctly human capabilities while embracing technological advancement, we can shape a future of work that leverages the best of both human and artificial intelligence.
