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How many jobs will AI create for humans?
Despite common fears of AI taking away jobs, it's poised to create millions over the next few years. Expect to see more job transformation and growth in strategy and engineering.
One of the defining workforce questions of the past few years is: "Will AI take my job?"
The answer is more nuanced than what either the optimists or the doomsayers suggest. The technology is real, its effect on workforces is measurable and the direction of change is mostly positive. Yet the timeline and distribution of that change across industries, career levels and geographies is uneven.
Post-ChatGPT, the first major quantitative alarm came from a 2023 Goldman Sachs report that estimated AI could automate 25% of work tasks across the U.S. and Europe, potentially displacing roughly 300 million full-time jobs. The report made global headlines, but its own conclusion was more nuanced: Historical precedent from prior automation waves suggests new jobs follow productivity gains, and net employment effects are likely positive over time.
It's important to note that the wave of tech-sector layoffs since 2022 is largely not attributable to AI. Most of it reflects a correction from pandemic-era over-hiring, when near-zero interest rates inflated growth assumptions, combined with sectoral slowdowns and pressure on unit economics. The evidence does not support AI as the cause. Mass displacement can't be attributed to a technology that most organizations are still running in pilot mode.
Below, take a data-driven look at the future of work and AI job creation, including what the research actually shows, where new AI and IT jobs are emerging and what IT leaders can do to ensure AI-driven roles benefit their organizations and their teams.
The AI and jobs debate
The question of how many jobs AI will create does not have a simple answer. AI is already creating substantial new work in engineering, infrastructure, governance and roles that barely had names a few years ago. It is transforming far more jobs than it is eliminating, and is leaning toward more employment in technology, not less.
A May 2025 study from the International Labour Organization (ILO) mapped nearly 30,000 tasks across global occupations. It found that one in four workers was in a role with some GenAI exposure, but only 3.3% were in the highest-exposure category. This implies that job transformation, not wholesale replacement, is the most likely outcome for the vast majority. Full automation remains constrained because many roles still require human judgment, context and accountability.
Where substitution occurs, it first shows up in hiring data rather than in headcount. Entry-level postings were hit hardest. Organizations are not retrenching employees en masse, but they are hiring fewer junior workers into roles AI can increasingly cover.
Startups and scale-ups are heralding this shift. They are resource-constrained and lack a large legacy head count, so they ask whether a smaller team plus an AI stack can do what previously required a much larger one.
How AI is creating new job opportunities
AI, as it's now known, is still in its infancy, which means it offers significant potential for new or evolved roles. This section explores eight potential areas where AI will create new opportunities.
1. AI strategy and business
Before models are built, someone must decide what gets built and why. AI product managers, consultants and heads of AI define the use case, align AI investment with business goals and translate technical capability into organizational value.
These are among the fastest-growing roles on LinkedIn's 2026 Jobs on the Rise list, and the rise of head of AI titles across major economies signals that organizations are treating AI strategy as a board-level priority. AI business strategy, including responsible AI and data governance, is also among the fastest-growing skill clusters in the U.S.
2. AI engineering and development
The most visible AI job creation is in the engineering layer. LinkedIn tracked 1.3 million new AI-related jobs globally in just two years, with AI engineers and forward-deployed engineers among the fastest-growing titles.
Forward-deployed engineers, a role that barely existed a few years ago, bridge AI model capability and real-world business integration -- the hardest part of making AI useful at scale.
3. AI infrastructure and data
An often-overlooked source of AI job creation is the infrastructure layer. LinkedIn documented over 600,000 net new data center jobs globally in the past year alone, spanning beyond software engineers to electricians, facilities managers and network operations specialists.
Cloud and AI platform engineers, data engineers and cybersecurity specialists are also seeing increased demand as AI systems require robust, secure data architectures to function.
4. AI deployment and operations
Beyond building models, a growing set of roles focuses on deploying and sustaining them in production. Data annotators -- specialists who provide feedback to improve model outputs -- are already a significant category.
Explainability experts, who help organizations document why AI systems make the decisions they do, will be increasingly sought after in regulated industries. Machine learning operations (MLOps) engineers are responsible for model versioning, monitoring and cost optimization, which are now seen as baseline requirements rather than specialist functions.
5. AI quality and validation
As AI models generate increasing volumes of code and content, the need for systematic verification grows in parallel. AI testers and QA engineers who can catch hallucinations, logic errors and edge cases are in rising demand.
As AI handles more execution, people are shifting into validation, exception-handling and oversight roles, such as model evaluators, human-in-the-loop validators and agent orchestrators.
6. AI governance, ethics and compliance
Perhaps the most underestimated source of AI job creation is the governance layer.
Risk and compliance management, data governance and responsible AI are among the fastest-growing skills clusters in the U.S., according to LinkedIn, and IT governance and compliance are among the hardest areas to staff.
Every AI deployment generates audit trails, compliance documentation and human review workflows. As regulatory frameworks like the EU AI Act impose mandatory requirements for transparency, that workload increases, particularly in financial services and healthcare.
7. Domain-specific AI roles
AI is catalyzing job creation in industries that the technology fundamentally reshapes. For example, autonomous vehicles require AI safety engineers, simulation specialists and fleet management architects -- roles that barely existed a decade ago.
Additionally, personalized healthcare sees demand for clinical AI specialists, precision medicine analysts and health data scientists. Smart city infrastructure is also creating openings for urban data engineers and AI-powered grid operators.
8. Future/emerging roles
The most reliable lesson from technology history is that transformative platforms create entire categories of work that were previously unimaginable.
Before the iPhone launched in 2007, mobile app development was practically nonexistent. Then, the internet gave rise to SEO specialists, social media managers, digital marketing strategists and data scientists -- none of which existed at a meaningful scale before the late 1990s.
The precise shape of tomorrow's AI-native careers is unknowable, just as the app economy was unknowable in 2005. Roles like prompt and context engineers, AI workflow designers and AI interaction designers are nascent today, but among the fastest-growing AI skills. They could be standard job titles within a decade.
The table below summarizes the eight categories covered above, with example roles and supporting evidence for each.
How IT leaders can shape AI-driven job creation
The employment outcome of any AI deployment is not technologically predetermined; it is a design choice. The same technology, deployed differently, produces different employment outcomes. That design choice sits within the CIO's remit.
To proactively identify where AI-driven roles can grow within the organization, CIOs must audit planned deployments for automation vs. augmentation intent before they go live and design role architectures around human-AI collaboration rather than substitution.
It also means creating deliberate apprenticeship pathways for junior workers. AI disrupts the traditional model -- assign routine tasks, build competence and promote -- as it handles routine work, so organizations need structured alternatives to preserve the pipeline.
Upskilling is urgent. Episodic training programs cannot keep pace with the rate of change in AI or with the pace of AI's shifting job priorities. IT leaders who embed continuous learning into job roles are better positioned to keep up.
Fostering a culture of innovation and human-AI collaboration is also the IT leader's responsibility. Organizations that capture the most value from AI treat it as a team architecture decision, not a head count decision. Investing in the skills, processes and governance structures helps people and AI systems work effectively together.
Challenges and ethical considerations
Despite AI's opportunities, the fear it brings is valid. The amount of change that AI causes will affect workforces and job roles themselves. Organizations must be aware of these challenges to maintain employees' trust.
The skills gap
Most enterprise learning and development programs are not structured to keep pace with the rate of skill change in AI-exposed roles. Partnerships with universities, community colleges, external specialists and bootcamps to build pipelines in areas like MLOps, AI auditing and governance analysis are becoming an operational necessity.
Investing in structured, ongoing employee training programs tied to specific role transitions, rather than generic AI awareness, helps organizations capture AI's value.
Ethical concerns and effects on existing jobs
AI deployment raises genuine ethical questions that IT leaders cannot afford to treat as someone else's problem. The distributional risk is not evenly spread: 9.6% of female employment in high-income countries falls into the highest GenAI exposure tier, compared to 3.5% of male employment, according to the ILO. Administrative and clerical roles, disproportionately held by women, face the most direct substitution risk.
Beyond demographic equity, questions about transparency abound: Do affected employees understand how AI is used in decisions that affect their roles? Are workers given meaningful opportunities to reskill before their positions are restructured? These choices carry ethical, reputational and regulatory weight, and IT leaders who co-own them with HR are better positioned to navigate them proactively.
Balancing automation with human-centric job creation
Leadership teams have a choice: Will they reinvest AI productivity gains into expanded services and new capabilities, or keep it purely as a cost reduction? This will determine whether AI will create or reduce jobs within the organization.
Organizations that treat AI as a growth and transformation strategy are better positioned to capture its productivity gains while preserving the human capability that makes those gains sustainable.
AI and the workforce of tomorrow
The most useful long-run framing for IT leaders is Jevons paradox. In 1865, economist William Stanley Jevons observed that more efficient steam engines led to more coal consumption, not less, because efficiency unlocked new uses and new demand.
As AI becomes cheaper and more capable, organizations will find applications that were previously not economically viable. This means more software built, more products created and more services offered. That expansion requires more engineers, data specialists, governance professionals and workers across the ecosystem.
The introduction of spreadsheet software in the 1980s was predicted to eliminate accounting jobs. Instead, it created an explosion in financial analysis roles because cheaper analysis unlocked demand for more analysis. The same dynamic is already visible in AI: As model inference costs fall, usage expands faster than head count shrinks.
Because skills gaps, not technology, are the primary barrier to AI ROI, HR is a critical stakeholder for AI value capture. Collaboration between IT leaders, HR teams and policymakers to ensure AI-driven job creation benefits society as a whole and is a practical requirement for organizations that want to be on the right side of this transition.
The near-term transition is where IT leaders should focus their energy. Skills are getting outdated faster than traditional training programs can respond, governance overhead is accumulating faster than most organizations can budget for and the entry-level pipeline needs deliberate redesign.
These are solvable problems, but they require proactive choices about how AI is deployed, who is reskilled and their reskilling pathways and how human-AI collaboration is designed. The leaders who treat those choices as a strategic priority, rather than a downstream HR consideration, are the ones who will help their organizations extract value from AI and create more jobs.
Kashyap Kompella, founder of RPA2AI Research, is an AI industry analyst and advisor to leading companies across the U.S., Europe and the Asia-Pacific region. Kashyap is the co-author of three books, Practical Artificial Intelligence, Artificial Intelligence for Lawyers and AI Governance and Regulation.
