Analysis of AI's Impact on Jobs (2023–2028): Job Replacement, IT Job Cuts, New Jobs Created, and Adaptation Strategies for Professionals

The rapid advancement of artificial intelligence (AI), particularly generative AI and automation technologies, is reshaping the global job market. This analysis addresses the realistic impact of AI on jobs over the next five years (2023–2028), focusing on which jobs are likely to be replaced, the percentage of IT job cuts, new jobs created by AI, and whether existing professionals can adapt to these new roles. It includes real-world case studies to illustrate trends and provides actionable strategies for professionals to stay relevant in an AI-driven economy. The analysis is grounded in credible data from sources like McKinsey, Goldman Sachs, the World Economic Forum (WEF), and real-world examples, while critically examining potential biases in optimistic or alarmist narratives.

1. Jobs AI Will Likely Replace by 2028

AI is expected to automate tasks that are repetitive, rule-based, or data-intensive, particularly in white-collar and entry-level roles. Based on 2023–2025 studies, the following job categories are most vulnerable to replacement or significant transformation by 2028, with a focus on realistic projections rather than speculative extremes:

• Data Entry and Administrative Roles: 
  • Description: Tasks like data entry, record-keeping, and basic administrative coordination (e.g., scheduling, document management) are highly automatable due to AI tools like robotic process automation (RPA) and large language models (LLMs).
  • Replacement Likelihood: Up to 60% of administrative tasks could be automated by 2028, per a 2024 Institute for Public Policy Research study. 
  • Example: Data entry clerks and administrative coordinators are being replaced by AI tools like UiPath and Automation Anywhere, which process data faster and with fewer errors.
  • Case Study: Walmart (2025) – Walmart cut 1,500 corporate jobs in 2025, automating back-office functions like data entry and inventory logging with AI systems, citing cost efficiency.
• Customer Service Representatives: 
  • Description: AI chatbots and virtual assistants (e.g., those powered by OpenAI’s GPT models or Google Dialogflow) handle routine customer inquiries, reducing the need for human agents.
  • Replacement Likelihood: AI could automate 80% of telemarketing and customer service tasks by 2028, per techpoint.africa, with chatbots cutting costs by up to 80%.
  • Example: Companies like Amazon and British Telecom (BT) have deployed AI-driven chatbots, reducing human customer service roles.
  • Case Study: British Telecom (2023–2030) – BT announced plans to cut 10,000 jobs by 2030, primarily in customer service, replacing agents with AI-driven chatbots for routine inquiries. 
• Content Writers and Copywriters: 
  • Description: Generative AI tools (e.g., ChatGPT, Jasper, Copy.ai) can produce formulaic content like social media posts, marketing emails, and basic articles, threatening entry-level writing roles.
  • Replacement Likelihood: 81.6% of digital marketers fear AI will replace content writers by 2028, per finalroundai.com. Basic content creation tasks are already 70% automatable.

• Example: AI tools are used by companies like HubSpot to generate first drafts, reducing demand for junior writers.
• Case Study: Upwork Freelancers (2023) – A Washington University and NYU study found a 2% drop in writing-related freelance jobs and a 5.2% decline in earnings on Upwork post-ChatGPT launch, as clients opted for AI-generated content. 
• Junior Software Developers and Coders: 
  • Description: AI coding assistants (e.g., GitHub Copilot, Tabnine) automate routine coding tasks like debugging, writing boilerplate code, and unit testing, impacting entry-level developers.
  • Replacement Likelihood: Bloomberg research indicates 53% of junior developer tasks could be automated by 2028.
• Example: Companies are using AI to generate code snippets, reducing the need for junior coders in repetitive tasks.
• Case Study: Meta (2025) – Meta cut 5% of its global workforce (3,600 employees), with AI automating routine coding tasks previously handled by junior developers. CEO Mark Zuckerberg noted AI could act as a “mid-level engineer” by 2025. 
• Market Research Analysts: 
  • Description: AI tools analyze consumer data, predict trends, and generate reports faster than humans, threatening roles involving routine data processing.
  • Replacement Likelihood: 53% of market research tasks are automatable by 2028, per Bloomberg research. 
  • Example: Tools like IBM Watson Analytics automate data collection and visualization, reducing manual analysis.
  • Case Study: Shopify (2024) – Shopify reduced hiring for market research analysts by leveraging AI tools to analyze customer behavior, cutting costs but impacting entry-level roles. 
• Legal Assistants and Paralegals: 
  • Description: AI tools like Harvey and CoCounsel automate document review, contract analysis, and legal research, reducing the need for human paralegals.
  • Replacement Likelihood: 44% of legal assistant tasks are automatable by 2028, per a 2023 Goldman Sachs study. 
  • Example: Law firms use AI for e-discovery and contract drafting, streamlining repetitive tasks.
  • Case Study: Klarna (2023–2025) – Klarna initially replaced 700 customer service agents with AI but later rehired human staff due to quality issues, highlighting AI’s limitations in nuanced tasks. 
• Financial Analysts and Bookkeepers: 
  • Description: AI platforms like Bloomberg Terminal enhancements automate financial modeling, data analysis, and bookkeeping.
  • Replacement Likelihood: 20% of analytical roles in banking are at risk by 2030, with 60% of bookkeeping tasks automatable by 2028. 
  • Example: Robo-advisors like Wealthfront handle investment recommendations, reducing demand for human analysts.
  • Case Study: JPMorgan (2025) – JPMorgan automated routine banking tasks, with CEO Jamie Dimon warning that 20% of analytical roles could be replaced by AI by 2030. 
• Retail Cashiers and Logistics Workers: 
  • Description: Self-checkout systems and autonomous vehicles (e.g., Waymo) reduce the need for cashiers and drivers.
  • Replacement Likelihood: 65% of cashier tasks and 30% of logistics tasks could be automated by 2028, per Pew Research and WEF. 
  • Example: Amazon Go stores use AI for cashier-less checkouts, and logistics firms test autonomous delivery trucks.
  • Case Study: Amazon Go (2023) – Amazon’s cashier-less stores eliminated traditional cashier roles, with AI handling transactions via computer vision. 

Critical Note: Not all jobs will be fully replaced; many will be augmented, with AI handling repetitive tasks while humans focus on complex, creative, or interpersonal aspects. For example, while AI automates legal research, human lawyers are still needed for case strategy and courtroom advocacy.

2. Percentage of IT Job Cuts (2023–2028)

The IT sector is particularly vulnerable to AI-driven automation due to its reliance on data processing, coding, and routine technical tasks. However, the impact varies by role and seniority.

• Estimated IT Job Cuts: 
  • Goldman Sachs (2023): AI could automate 25–50% of workloads in IT-related roles, translating to 10–20% job cuts in the IT sector by 2030, with 5–10% expected by 2028 due to gradual adoption. 
  • Bloomberg Intelligence (2025): A survey of 93 major banks (e.g., Citigroup, JPMorgan) predicts a 3% workforce reduction by 2030, with IT roles like junior developers and basic IT support facing 5–10% cuts by 2028. 
  • Finalroundai.com (2025): Entry-level IT roles (e.g., junior developers, routine SOC monitoring) could see up to 50% task automation by 2028, leading to 15–20% job cuts in these subsectors. 
  •  
• Realistic Projection for 2023–2028: 
  • Overall IT Sector: Approximately 8–12% of IT jobs (e.g., junior developers, IT support, data analysts) are likely to be cut by 2028, driven by AI tools automating coding, debugging, and helpdesk functions. Senior roles requiring strategic oversight or complex problem-solving are less affected.
  • Entry-Level Impact: Entry-level IT roles face the highest risk, with 15–20% cuts possible due to tools like GitHub Copilot and AI-driven IT service management platforms (e.g., ServiceNow). 
  • Regional Context (India): Microsoft’s 2023 Work Trend Index reported 74% of Indian workers fear AI-driven job losses, with IT being a key sector. In 2023, India’s tech sector saw 136,831 layoffs, partly due to AI adoption in coding and support roles. 
• Case Study: IBM (2023–2025) – IBM predicted replacing 8,000 jobs with AI, primarily in HR and IT support, but by 2025, only a few hundred HR roles were automated, with increased hiring in software development. This suggests slower-than-expected AI replacement in IT due to quality concerns.

Critical Note: The 8–12% estimate accounts for gradual AI adoption, economic constraints, and the need for human oversight in complex IT tasks. Alarmist claims of 50%+ cuts are unlikely by 2028, as AI tools still require human supervision and integration costs can outweigh immediate savings. 

3. New Jobs Created by AI (2023–2028)

While AI will displace certain jobs, it will also create new roles, particularly in tech, healthcare, and education, requiring skills in AI development, oversight, and human-AI collaboration.

• Projected New Jobs: 
  • World Economic Forum (2023): AI could create 97 million new jobs globally by 2025, with 69 million new roles by 2027, offsetting 83 million job losses. Key growth areas include: 
    • AI and Machine Learning Specialists: ~1 million jobs by 2027.
    • Data Analysts and Scientists: Growth in healthcare and logistics.
    • AI Literacy Trainers: Educators teaching AI skills in workplaces.
    • Health Tech Implementation Specialists: Roles integrating AI in medical systems.
  • McKinsey (2023): 20–50 million new jobs by 2030, with 10–20 million by 2028, in sectors like AI development, cybersecurity, and AI ethics. 
  • LinkedIn (2023): A 12% increase in AI-related job postings (e.g., machine learning engineers, prompt engineers) across seven major economies from December 2022 to September 2023. 
• Specific Roles: 
  • Machine Learning Engineer: Designing and deploying AI models.
  • Prompt Engineer: Crafting inputs for generative AI tools.
  • AI Ethics Specialist: Ensuring responsible AI use.
  • Cybersecurity Analyst: Protecting AI systems from threats, a role less automatable due to human judgment needs.
  • Hybrid Roles: AI-assisted doctors, teachers, and legal professionals using AI tools for diagnostics, personalized learning, or research.
• Case Study: Amazon Upskilling 2025 (2021–2025) – Amazon invested $1.2 billion to retrain 300,000 employees for technical roles like machine learning engineers and data analysts, creating new AI-related positions within the company. 

Quantitative Estimate: By 2028, 50–70 million new jobs are expected globally, with ~10–15 million in IT-related fields (e.g., AI development, cybersecurity). In India, the IT sector could see 2–3 million new AI-related jobs, driven by global demand and outsourcing. 

4. Can Existing Professionals Adapt to New AI-Created Jobs?

Feasibility of Adaptation:

• Yes, Many Can Adapt: Professionals in IT, content creation, and analytics can transition to AI-related roles by upskilling in AI tools, programming, and data science. Key skills include: 
  • Technical Skills: Python, machine learning frameworks (e.g., TensorFlow, PyTorch), and AI model deployment.
  • Soft Skills: Critical thinking, creativity, and emotional intelligence, which AI cannot replicate. 
  • AI Literacy: Understanding how to use AI tools (e.g., ChatGPT, Copilot) to enhance productivity.
• Challenges:  
  • Skill Gap: 39% of workers globally worry about inadequate training, per PwC’s 2023 survey. Entry-level workers and older professionals may struggle to learn advanced AI skills. 
  • Cost and Access: Upskilling programs are expensive, and access is limited in developing regions like parts of India. 
  • Time Constraint: Rapid AI adoption (e.g., 70% of firms using AI by 2030, per McKinsey) leaves little time for retraining. 
• Success Factors: 
  • Proactive Learning: Professionals who take AI courses (e.g., Coursera, edX) or attend workshops can transition faster.
  • Employer Support: Companies like Amazon and IBM offer upskilling programs, increasing job security for adaptable employees. 
  • Hybrid Roles: Combining domain expertise (e.g., legal, marketing) with AI proficiency ensures relevance.

Strategies for Adaptation:

1. Learn AI Tools: Master tools like GitHub Copilot for coding or Jasper for content creation to stay competitive.
2. Upskill in High-Demand Areas: Enroll in courses for machine learning, data science, or cybersecurity (e.g., Google’s AI Professional Certificate).
3. Focus on Human-Centric Skills: Develop emotional intelligence, strategic thinking, and leadership, which are less automatable. 
4. Collaborate with AI: Use AI as a productivity tool rather than a competitor (e.g., coders using AI for debugging).
5. Network and Certify: Gain certifications and join AI-focused communities to stay updated on job trends.

Case Study: Klarna’s Reversal (2023–2025) – Klarna’s attempt to replace 700 customer service agents with AI led to quality issues, prompting rehiring of human agents. This shows that professionals who adapt by offering unique value (e.g., empathy, complex problem-solving) can remain relevant. 

Limitations:

• Low-Skilled Workers: Data entry clerks or basic IT support staff may struggle to upskill for complex AI roles due to limited technical backgrounds.
• Economic Barriers: In India, where 74% of workers fear job loss, access to affordable training is limited, particularly in rural areas. 
• Age and Adaptability: Older professionals (40+) may resist or find it harder to learn new technologies, per McKinsey. 

5. Is the Job Cut Real? Evidence of Existing Replacements

The job cut threat is real but nuanced. AI has already displaced jobs, particularly in repetitive and entry-level roles, but the scale is smaller than predicted, and quality issues often lead companies to retain human workers.

• Evidence of Job Cuts: 
  • 2023 Layoffs: In May 2023, AI caused 3,900 job losses in the US (5% of total layoffs), per Challenger, Gray & Christmas Inc. 
  • Tech Sector: 136,831 tech layoffs in 2023, partly due to AI automating roles like IT support and content creation. 
  • Freelance Writing: Post-ChatGPT, Upwork saw a 2% drop in writing jobs and 5.2% earnings decline, as clients used AI for content. 
  • Customer Service: Companies like Klarna and BT reduced human agents, with AI handling routine inquiries. 
• Counterevidence:  
  • Quality Issues: Klarna rehired human agents in 2025 after AI failed to deliver quality customer service, showing limitations in full automation.  
  • MIT Study (2023): Only 23% of automatable tasks are cost-effective to replace with AI, as human labor remains cheaper in many cases. 
  • Hybrid Adoption: 85% of office workers believe AI enhances rather than replaces their roles, per Jitterbit. 

Reality Check: Job cuts are happening, but the pace is slower than alarmist predictions (e.g., Anthropic CEO Dario Amodei’s claim of 50% entry-level job cuts by 2030). Economic constraints, quality concerns, and the need for human oversight temper AI’s impact. By 2028, job transformation (augmentation) is more likely than mass replacement. 

6. Analysis and Case Studies

Analysis:

• Job Replacement Scope: AI will primarily target repetitive, entry-level, and data-driven tasks by 2028, affecting 8–12% of IT jobs and up to 60% of tasks in administrative, customer service, and content creation roles. However, human-centric roles (e.g., strategic IT, cybersecurity) and complex tasks remain resilient. 
• IT Sector Impact: Entry-level IT roles (junior developers, IT support) face the highest risk (15–20% cuts), while senior roles and specialized fields like cybersecurity see growth. India’s IT sector, a global hub, could lose 100,000–150,000 entry-level jobs but gain 2–3 million AI-related roles. 
• New Job Creation: AI will create 50–70 million jobs globally by 2028, with 10–15 million in IT (e.g., machine learning engineers, AI ethicists). These roles require technical and soft skills, accessible to adaptable professionals. 
• Adaptation Feasibility: IT professionals with coding or analytical skills can transition to AI roles through upskilling, but low-skilled workers face barriers due to training costs and complexity. Employer-led programs (e.g., Amazon’s Upskilling 2025) are critical.
• Economic and Social Factors: In India, economic uncertainty and limited training access may exacerbate job displacement fears, but global demand for AI skills offers opportunities. Social dialogue and government policies can mitigate impacts. 

Key Case Studies:

1. Walmart (2025): Automated 1,500 corporate jobs, including data entry and inventory management, using AI to streamline operations. This highlights the vulnerability of repetitive tasks but also the need for human oversight in complex logistics. 
2. Klarna (2023–2025): Replaced 700 customer service agents with AI but rehired humans due to quality issues, showing that AI’s limitations create opportunities for skilled workers. 
3. Upwork Freelancers (2023): A 2% drop in writing jobs post-ChatGPT launch demonstrates AI’s immediate impact on creative roles, but writers with strategic skills (e.g., brand expertise) remain in demand. 
4. Amazon Upskilling 2025 (2021–2025): Invested $1.2 billion to train 300,000 employees for AI-related roles, creating a pipeline for machine learning engineers and data analysts, proving adaptation is possible with employer support. 
5. JPMorgan (2025): Automated 20% of analytical tasks in banking, but human analysts are still needed for strategic decisions, illustrating augmentation over replacement. 

7. Recommendations for Professionals

To avoid obsolescence and secure new AI-driven jobs:

• Upskill Immediately: Learn Python, machine learning, and AI tools via platforms like Coursera, edX, or Google’s AI certificates. 
• Leverage AI Tools: Use AI to enhance productivity (e.g., Copilot for coding, Jasper for writing) to stay competitive.
• Focus on Non-Automatable Skills: Develop critical thinking, emotional intelligence, and strategic planning, which AI cannot replicate. 
• Seek Employer Support: Join companies with upskilling programs (e.g., Amazon, IBM) or advocate for training at your workplace.
• Target Growth Sectors: Pursue roles in AI development, cybersecurity, or health tech, which are projected to grow. 
• Stay Updated: Follow AI trends via LinkedIn, WEF reports, and industry forums to anticipate job shifts.

Conclusion

By 2028, AI will realistically displace 8–12% of IT jobs, particularly entry-level roles like junior developers and IT support, while automating up to 60% of tasks in administrative, customer service, and content creation jobs. However, it will create 50–70 million new jobs globally, including 10–15 million in IT-related fields like AI development and cybersecurity. Existing professionals can adapt by upskilling in AI tools, focusing on human-centric skills, and leveraging employer-led training, as seen in Amazon’s program. Case studies like Klarna and Upwork show that while AI is already replacing jobs (e.g., 3,900 US layoffs in May 2023), quality issues and human expertise ensure continued demand for adaptable workers. The job cut threat is real but manageable with proactive adaptation, especially in India’s IT sector, where global demand for AI skills offers significant opportunities.

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Note: Predictions vary due to economic, technological, and regional factors. Professionals should monitor industry trends and invest in continuous learning to stay competitive. For further guidance, explore resources like Coursera, LinkedIn Learning, or employer-sponsored training programs.