Resources for the Field of Artificial Intelligence

Get an overview of the current career opportunities in AI and how the field is expanding to new industry sectors.

The Evolution of AI

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Emerging as a formal field in the 1950s, artificial intelligence refers to systems engineered to simulate human cognitive functions. Intersecting the components of perception, representation, recognition, reasoning, planning, learning, and optimization, AI has changed the way we live our lives and function as a society.

Over the past 70 years, AI has experienced several evolutions and transformations and has emerged as a multidisciplinary and interdisciplinary field closely intertwined with big data, high-performance computing, ethics, and regulations, attracting widespread adoption.

AI is developing continuously and is expected to become even further embedded into the fabric of our lives. The applications of this technology vary, and new research is discovered every day.

Where Is AI Headed?

Not only is AI personal, as we utilize its powers through our daily devices and more, but the technology also elevates several major areas of computing. This includes areas such as healthcare, transportation systems, the energy sector, and security.

Healthcare/Life Sciences

Within the industry of healthcare, AI is being applied across activities, including clinical decision support, medical imaging and diagnosis, clinical documentation, drug development, disease surveillance, and health-system management.

According to Foresee Medical, a major impact in the medical field was made when AI was used to aid practitioners by predicting what proper treatments and procedures would most likely work with individual patients, i.e., precision medicine. AI continues to expand in precision medicine and biomedical research, with newer applications including AI models for genomics, protein science, drug discovery, and virtual-cell modeling.

In addition to the massive push for AI in healthcare during the COVID-19 pandemic, AI is moving toward routine integration in healthcare, including the broad adoption of AI tools for automatically generating clinical notes across many hospital systems. Job options such as AI health developer and bioinformatician are changing the way medicine is practiced in societies across the world.

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Transportation/Aerospace

AI transforms global transportation by providing capabilities for autonomous mobility and intelligent aerospace systems. With the power of AI, transportation is elevated with the machine’s ability to make smart decisions in real time. AI is enabling autonomous mobility through advances in computer vision, sensor fusion, edge computing, machine learning, simulation, and real-time decision-making. Autonomous driving is now in commercial deployment, particularly in robotaxi services, while AI-powered drones are in use for logistics, infrastructure inspection, disaster response, and other applications.

In aerospace systems, AI’s role is continually expanding, with autonomous systems increasingly used to support navigation, sensing, mission operations, and long-duration exploration. In terms of jobs, self-driving technology calls for machine learning engineers who specialize in autonomous driving, as well as security engineers, behavior prediction engineers, and project managers specializing in simulation. As autonomous systems in transportation and aerospace become more complex, there is also increasing demand for expertise in edge computing, AI safety and assurance, simulation and validation, cybersecurity, robotics, and human-AI interaction.

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Energy

As emerging research on AI within energy continues to grow, more tools using the technology are created, pushing the energy sector forward. These tools provide power-load and generation forecasting, grid optimization, predictive maintenance, and demand management. 

AI is also increasingly used to help utilities integrate variable renewable energy sources, optimize power distribution, and anticipate changes in electricity demand. This means that cities can utilize the power of AI to detect widespread changes in consumption, energy, and power before they happen. At the same time, the rapid expansion of generative AI and AI data centers is creating unprecedented demand for electricity and water, creating critical sustainability challenges and making energy efficiency, power management, and new sources of generation increasingly important to AI’s continued growth. Further, as energy infrastructure becomes more connected and automated, cybersecurity, reliability, and AI governance are emerging as critical considerations for the sector.

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Privacy and Security

As cyberattacks become increasingly sophisticated and AI systems become more widespread, cybersecurity and privacy have become critical considerations in AI’s development and use. AI is now automating routine security tasks, detecting threats, analyzing large volumes of data, and helping security teams respond more quickly. However, it also enables attacks that can be carried out with greater speed, scale, personalization, and automation, including sophisticated phishing and deepfakes, AI-generated malware, data poisoning, prompt injection, and attacks targeting AI agents

As connected systems and AI become more deeply integrated, organizations must protect their infrastructure and data, as well as protect the AI systems and components themselves from manipulation, data leaks, privacy breaches, and other emerging threats. This creates growing demand for professionals, including security engineers, information security analysts, and AI security specialists, who can secure AI systems, use AI for cyber defense, and protect organizations against AI-enabled attacks.

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AI Industry & Careers Forecast

As AI capabilities advance, demand is also growing for professionals who can develop, implement, manage, and secure these systems. According to the Stanford Institute’s 2026 AI Index, 88% of surveyed organizations reported using AI in at least one business function in 2025. This continued expansion is driving AI investment and increasing the demand for workers with specialized AI skills. The World Economic Forum’s Future of Jobs Report 2025 identified AI and machine learning specialists among the fastest-growing occupations through 2030, while AI and big data rank as the fastest-growing skills.

The US Bureau of Labor Statistics has forecasted that jobs within the field of computer and information science are expected to increase 20% by 2034, with approximately 3,200 openings projected each year. Further, PwC’s 2026 Global AI Jobs Barometer found that workers with AI skills command an average 62% wage premium, underscoring the growing value of AI expertise across industries. The PwC report also noted that “The more AI is deployed, the more distinctly human expertise is valued,” including in crucial areas AI can’t replicate, such as leadership, judgment, creativity, and teamwork.

As AI becomes increasingly integrated into jobs across sectors, software engineers who can work with data, evaluate and manage AI tools, and address their security and responsible use will be in high demand.

Data Scientists

The field of data science intertwines domain expertise, programming, and statistics to develop insights from data. Along with using traditional statistical methods, data scientists are increasingly working with machine learning and AI. Their work includes collecting, processing, analyzing, and modeling data, as well as translating results into meaningful insights that can inform decisions and solve organizational problems.

Machine Learning Engineer

Machine learning engineers design and produce AI systems, developing and implementing automated machine learning models and the software systems that support them. Though similar to data scientists, since they too handle large sets of data, machine learning engineers typically focus more heavily on building, deploying, scaling, and maintaining production machine learning systems, while data scientists generally focus more on analyzing data, developing models, and communicating insights. Depending on the organization, these roles sometimes overlap significantly.

AI & Machine Learning Architect

In this position, engineers design and oversee the architecture of AI and machine-learning systems, translating business and technical requirements into scalable, secure, and maintainable solutions. Rather than focusing primarily on developing individual models, architects determine how models, data pipelines, applications, infrastructure, and deployment systems work together across the full AI lifecycle. 

Business Intelligence Developer

Business intelligence developers build and maintain systems that transform organizational data into reports, dashboards, and actionable insights for an organization’s decision-makers. In addition to organizing, analyzing, and visualizing data, they work with business stakeholders and technical teams to determine what an organization’s needs are. Skills: Data analysis; business analysis; SQL; data modeling and visualization; business intelligence and analytics platforms; extract, transform, and load (ETL)/data integration; troubleshooting; and communicating insights to technical and nontechnical stakeholders.

Footnote: Salary data sourced from ZipRecruiter, 2026

Ethics in AI

According to former IEEE Intelligent Systems Editor-in-Chief San Murugesan, “[AI’s] impact could be profound, both good and harmful. So, it is imperative that we collectively and proactively address the ethical and social concerns associated with AI, ensure its responsible development, and use it judiciously and responsibly, knowing its capabilities, limits, and limitations for the benefit of all, including our environment.” 

As AI becomes increasingly embedded across sectors and daily human life, it is imperative to proactively address the ethical, social, and security concerns associated with its development and use.

Among those ethical issues are algorithmic bias and discrimination, privacy and surveillance, misinformation and manipulation, safety and security, impacts on workers and society, and AI’s vast energy demands and the environmental and public health issues they raise. As the OECD AI Principles point out, it’s no longer sufficient to simply adopt or develop AI tools; organizations must consider whether an AI system works as intended, as well as whether it should be used for a particular purpose, what data it relies upon, who might be harmed by its outputs, how its energy and resources consumption impacts environmental sustainability, and who is accountable for the decisions it makes.

It is also essential that AI be directed at social good and enlisted to solve societal challenges. IEEE’s AI-Driven Innovation for Cities and People initiative explores this territory, focusing on how standards and governance can support responsible AI deployment in urban environments.

Governments and organizations are now grappling with translating ethical AI principles and practices into real-world regulation. For example, the European Union’s AI Act is the world’s first comprehensive legal framework for AI; this risk-based regulatory framework began phasing into application in 2025, with additional requirements taking effect through 2027.

Ultimately, the long-term success of AI depends not only on what the technology can do, but also on whether it is developed and deployed in ways that protect human rights, safety, privacy, and the broader public interest.

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AI Related Research & Insights

AI has the power to change our lives continuously over time. Stay up to date with the technology’s progress by accessing our Tech News blog, which is updated daily with the latest insights, trends, and research related to all things computing.