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Artificial intelligence can help people process information, support selected decisions and tasks, and explore scientific questions faster. Its clearest current benefits are practical and specific: assisting some workplace functions, supporting health research and clinical tools, and helping students and teachers work with information. But adoption and promising demonstrations do not prove that AI improves every outcome. Results depend on the task, data, oversight, access, and safeguards.
What AI can do—and what counts as a benefit
AI systems can find patterns in data, generate or summarize text, and help classify information or make predictions. These capabilities may extend human capacity when they are applied to a suitable task. They do not by themselves establish better decisions, higher-quality services, or fairer outcomes.
Evidence also comes in different forms. A survey records what organizations or workers report; a benchmark measures performance on a defined test; an authorization permits a device to be marketed for specified uses; and a real-world outcome study examines effects on people in practice. Those are not interchangeable. A sensible assessment asks what was measured, for whom, and under what conditions.
How AI may help at work
AI is used in functions such as service operations, supply chains, software engineering, marketing, and sales. Stanford HAI’s 2025 AI Index reports that 78% of surveyed organizations used AI in 2024, up from 55% in 2023. Generative AI use in at least one business function was reported by 71% of respondents in 2024, compared with 33% in 2023. These figures show adoption, not that every deployment was effective.
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Respondents whose organizations used AI reported cost savings in some functions: 49% in service operations, 43% in supply-chain management, and 41% in software engineering. These percentages are the shares reporting savings, not the amount saved. Among respondents who reported cost savings, most estimated that savings were below 10%; among those reporting revenue gains, the most common increase was below 5%. The reported effects were often modest, and survey responses do not establish that AI alone caused them. Stanford HAI 2025 AI Index
Workers’ own assessments are another useful but limited signal. In OECD surveys, four in five workers said AI improved their performance at work, and three in five said it increased their enjoyment of work. These are respondents’ perceptions, not a universal or experimentally measured effect. OECD, Using AI in the Workplace
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How AI is used in health and science
Health applications include support for screening and diagnosis, clinical care, health research and drug development, public-health interventions, disease surveillance, outbreak response, and health-system management. The World Health Organization says AI could also help rural and resource-poor settings bridge access gaps, but cautions against overstating that possibility or using it to displace core investments needed for universal health coverage. WHO, 28 June 2021
There are signs of growing use, but they should be read precisely. Stanford HAI reports that the number of FDA-authorized AI-enabled medical devices reached 223 by 2023, compared with six by 2015. Authorization counts indicate that devices received regulatory authorization; they do not show that all such devices improve patient outcomes in routine care. AI also supports scientific discovery and research using synthetic data, while strong results on clinical-knowledge benchmarks remain test results rather than proof of better care for patients. Stanford HAI 2025 AI Index
WHO Director-General Dr Tedros Adhanom Ghebreyesus put the opportunity and risk together in the organization’s 2021 announcement: “Like all new technology, artificial intelligence holds enormous potential for improving the health of millions of people around the world, but like all technology it can also be misused and cause harm.”
How AI can support education
Students’ reported uses of generative AI include research, essay editing, and brainstorming, according to Stanford HAI’s 2026 AI Index education chapter. These are examples of current practice, not evidence that AI improves learning for every student. Stanford HAI 2026 AI Index
Potential benefits include personalized tutoring, lower barriers to finding knowledge, and help for teachers creating materials tailored to learners. The OECD describes these as potential gains, while noting that adoption in education is slow and previous educational technologies have not always delivered their promised results. Teacher preparedness is also uneven, so access to a tool does not guarantee that educators have the time, training, or support to use it well. OECD, 14 November 2024 Stanford HAI 2025 AI Index
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Potential benefits for science and society
The OECD identifies ten priority potential benefits, including accelerated scientific progress, productivity gains, and better sense-making and forecasting. These are prospective benefits, not settled outcomes across sectors. If AI helps researchers analyze large datasets or supports planning amid complex information, it may contribute to progress; whether that contribution becomes a public benefit depends on how reliable, accessible, and well-governed the system is. OECD, 14 November 2024
Best Value
What determines whether AI’s benefits are real?
Before adopting or relying on an AI system, assess the particular use rather than asking whether AI is beneficial in general. These questions help distinguish a credible fit from a compelling promise:
- Evidence maturity: Is the claim prospective, based on a benchmark or survey, or demonstrated through real-world outcomes?
- Task fit: Is the system suited to the specific work, and does it have appropriate, reliable data?
- Scale and distribution: How large is the benefit, and who can access it or may be left out?
- Cost of error: What happens when the system is wrong, and can a person identify and correct the error?
- Privacy and security: How are sensitive data collected, used, stored, and protected?
- Accountability and oversight: Who checks the output and remains responsible for decisions?
- Wider impact: What are the effects on inequality, concentrated power, and the environment?
Risks and safeguards matter as much as capability
In health, WHO flags unethical collection or use of health data, bias encoded in systems, patient-safety risks, cybersecurity, and environmental concerns. These risks can undermine the very benefits a tool is meant to provide. The organization calls for safeguards and warns that AI should not be treated as a substitute for necessary health-system investment. WHO, 28 June 2021
The OECD also identifies broader risks, including cyberattacks, manipulation, disinformation, fraud, incidents in critical systems, concentrated power, and worsening inequality or poverty. Its policy framing emphasizes risk management, safety investment, and clearer accountability, including liability. AI can support people and institutions, but it cannot by itself resolve structural problems or guarantee that benefits are shared fairly. OECD, 14 November 2024
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Further reading
For a deeper look at adoption, evidence, and sector-specific examples, consult Stanford HAI’s 2026 AI Index and 2025 AI Index. The OECD’s assessment of potential benefits, risks, and policy priorities provides a complementary view of future possibilities and governance.
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