Artificial intelligence has moved from a test project to a standard business tool. In 2025, 88% of organizations surveyed by McKinsey said they used AI in at least one business function. A year earlier, the figure was 78%.
Use is not even across the economy. Software developers, banks, doctors, lawyers, journalists, retailers and manufacturers each face different costs, rules and risks. This article looks at the latest survey data for each of these industries.
The figures come from different surveys with different methods. They should not be read as a perfect side-by-side comparison. They do show where AI use is deepest and where it still has limits.
Overall Business AI Adoption Statistics
The Stanford 2026 AI Index draws on McKinsey’s global State of AI survey. It found that 88% of organizations used AI in at least one business function during 2025. That compares with 55% in 2023 and 78% in 2024.
Generative AI has grown faster. Regular use by organizations rose from 33% in 2023 to 71% in 2024 and 79% in 2025. More than half of respondents said AI was used in at least three business functions.
| Year | Organizations using AI in at least one function | Organizations regularly using generative AI |
|---|---|---|
| 2023 | 55% | 33% |
| 2024 | 78% | 71% |
| 2025 | 88% | 79% |
Using AI in one area is not the same as rebuilding a company around it. Stanford found that AI agent deployment stayed in the single digits across almost all business functions in 2025.
That makes 2026 a year of change. The first wave of AI assistants is giving way to a second wave of more independent AI agents.
AI Adoption by Industry at a Glance
The table below gives one recent adoption figure for each industry covered in this article. Each number comes from a separate survey. The groups surveyed and the questions asked are different.
| Industry | Recent AI adoption indicator |
|---|---|
| All industries | 88% of organizations use AI in at least one function |
| Generative AI overall | 79% of organizations regularly use generative AI in at least one function |
| Software development | 84% of developers use or plan to use AI tools |
| UK financial services | 75% of firms already use AI |
| Healthcare | 66% of surveyed physicians use some form of health AI |
| UK journalism | 56% of journalists use AI professionally at least weekly |
| Legal | Organizational generative AI use rose from 14% to 26% in one year |
| Retail | 64% of surveyed retailers use AI for fraud and cybersecurity |
| Manufacturing | About two-thirds of surveyed large manufacturers are still at the exploration or targeted stage |
The data shows that AI is no longer limited to technology companies. It is now part of work in many parts of the economy.
AI in Finance and Banking Statistics
Financial services used machine learning for years before the generative AI boom. Banks, insurers and payment firms have applied it to fraud detection, credit scoring, anti-money-laundering checks, trading and risk management. Generative AI is now being added to customer service, compliance, research and software work.
A joint survey by the Bank of England and the Financial Conduct Authority found that 75% of responding UK financial firms already used AI. Another 10% planned to adopt it within three years. In 2022, the share using AI was 58%.
Use is higher in some parts of the sector. Insurers and international banks report the highest rates.
| Sector | Share of firms using AI |
|---|---|
| Insurers | 95% |
| International banks | 94% |
| All respondents | 75% |
| Financial market infrastructure firms | 57% |
Where Finance Uses AI
The median firm reported about nine AI use cases. Firms expect that number to rise to 21 within three years. Large international banks already report a median of close to 49.
Operations and IT account for the largest share of reported use cases, at about 22%. Retail banking makes up around 11% and general insurance about 10%. Foundation models, the large systems behind generative AI, account for 17% of use cases.
Finance is one of the most mature industries for AI. Even so, firms are careful with high-impact decisions. Errors in credit, trading, insurance or compliance can carry large financial and legal costs.
AI in Healthcare Statistics
Healthcare has usually been slower to adopt new technology because mistakes can harm patients. AI use is still growing quickly. The American Medical Association found that the share of physicians using some form of health AI rose from 38% in 2023 to 66% in 2024.
That is a rise of 28 percentage points in one year. Common uses include medical notes, billing codes, visit summaries, care plans, discharge instructions, translation and patient messages. Some physicians also use AI for diagnostic support and radiology.
Paperwork is a major driver. Physicians spend many hours documenting care. AI transcription tools can listen to a visit and draft the clinical note, which cuts that workload.
Doctors See Benefits but Want Safeguards
About 68% of physicians surveyed said AI offered at least some advantage to their practice. Their support came with conditions. Most wanted clear protections before using it more widely.
| What physicians want | Share of physicians |
|---|---|
| A way to report AI problems | 88% |
| Privacy assurances | 85% |
| Appropriate AI training | 84% |
| AI built into health record workflows | 84% |
Accuracy standards are higher in medicine than in most business tasks. A wrong marketing slogan is an inconvenience. A wrong diagnosis can be dangerous, so human review is likely to stay central.
AI in Law Statistics
Legal work involves a great deal of text, so it was seen early as a likely target for generative AI. AI tools can search case law, review contracts, summarize documents, draft memos, compare clauses and support discovery. Adoption has grown quickly as a result.
Thomson Reuters found that generative AI use in legal organizations rose from 14% in 2024 to 26% in 2025. Personal use is higher than formal company use. Across legal, tax, risk and government professionals, 41% said they used public tools such as ChatGPT, and 17% used industry-specific generative AI systems.
| Legal use of generative AI | Share of legal users |
|---|---|
| Document review | 77% |
| Legal research | 74% |
| Document summarization | 74% |
| Brief or memo drafting | 59% |
| Contract drafting | 58% |
| Correspondence drafting | 50% |
Law Firms Are Moving Past Testing
Thomson Reuters’ 2026 research points to a shift in the debate. The question is less about whether to use AI and more about how fast to reorganize around it. More than three-quarters of highly rated lawyers surveyed said their firm had an AI strategy.
Pressure is building. About 38% of law-firm professionals reported financial pressure to act faster on AI. Another 32% of in-house lawyers said they were reconsidering ties with firms that failed to show AI-enabled value.
AI may also challenge the billable hour. If a system can finish hours of document review in minutes, clients may be less willing to pay by time. That could push firms toward value-based pricing.
Governance is a concern. Thomson Reuters found that 34% of law-firm professionals used AI tools their firms had not approved.
AI in Software Development Statistics
Software development is the field where generative AI is most deeply part of daily work. The 2025 Stack Overflow Developer Survey found that 84% of developers used or planned to use AI tools, up from 76% a year earlier.
Among professional developers, 51% used AI tools every day.
Developers use AI to write code, explain unfamiliar code, find bugs, write tests, create documentation and convert code between languages. Tools such as GitHub Copilot, ChatGPT and Claude are now common.
High Use, Low Trust
Heavy use does not mean strong trust. Stack Overflow found that 46% of developers distrusted the accuracy of AI output, compared with 33% who trusted it. Only 3% said they highly trusted it.
The top complaint was answers that are almost right, but not quite. Some 66% of respondents named this as a frustration. Another 45% said fixing AI-written code often took longer than expected.
| Measure | Result |
|---|---|
| Use or plan to use AI tools | 84% |
| Professional developers using AI daily | 51% |
| Distrust AI accuracy | 46% |
| Trust AI accuracy | 33% |
| Highly trust AI output | 3% |
| Frustrated by almost-right answers | 66% |
| Do not plan to use AI for deployment and monitoring | 76% |
| Do not plan heavy AI use for project planning | 69% |
Developers are careful about high-responsibility tasks. About 76% did not plan to rely on AI for deployment and monitoring, and 69% did not plan to use it heavily for project planning. AI is acting as a copilot, not a replacement.
AI Agents in Software
AI agents are less common than basic assistants. Stack Overflow found that 52% of developers either did not use agents or stuck with simpler AI tools, and 38% had no plans to adopt agents.
Among developers who did use agents, about 70% said agents reduced the time spent on tasks. Another 69% reported better productivity. Code is easier to test and check than many real-world tasks, which helps explain the early interest.
AI in Media and Journalism Statistics
Media offers both a large opportunity and a trust problem. AI can handle transcription, translation, headlines, summaries, data analysis, fact checking and personalization. It can also produce text and images.
A Reuters Institute study of 1,004 UK journalists found that 56% used AI professionally at least once a week. Another 27% used it less often. Only 16% said they had never used AI for journalism tasks, and about 27% used it daily for at least one task.
Publishers Focus on Back-End Tasks
Media executives are most interested in efficiency. Among publisher respondents, 96% said back-end automation would be important. Personalization and recommendations came next at 80%, followed by content creation at 77% and newsgathering at 73%.
Coding was cited by 67% and commercial uses by 63%. Some publishers already use AI to rewrite or distribute stories at scale. Audience views limit how far that can go.
The Reuters Institute found that only 12% of consumers were comfortable with news made entirely by AI. Comfort rose when a person stayed in charge. Entirely human-made news drew the most support at 62%.
| How the news is made | Consumers comfortable |
|---|---|
| Entirely by humans | 62% |
| Human journalist leads, AI assists | 43% |
| Mainly AI, with human oversight | 21% |
| Entirely by AI | 12% |
These results suggest that mainstream journalism is likely to be AI-assisted rather than AI-written.
AI in Retail Statistics
Retail AI has grown beyond product recommendations. It now touches fraud detection, pricing, inventory, demand forecasting, customer support, marketing, supply chains and store operations.
Deloitte’s 2026 Global Retail Outlook found wide use across core functions. Fraud detection and cybersecurity led, with 64% of surveyed retailers already using AI there.
| AI use case | Retailers already using it |
|---|---|
| Fraud detection and cybersecurity | 64% |
| Pricing and promotion optimization | 48% |
| Customer-service chatbots | 42% |
| Demand forecasting | 38% |
| Product recommendations and search | 33% |
| Social-media monitoring | 33% |
| Supply-chain visibility | 30% |
Use is expected to grow over the next 12 months. Another 41% of retailers expect to add AI-based supply-chain visibility, and 38% plan to add pricing and promotion optimization.
Agentic Shopping May Be Next
Retail could be one of the first industries to feel the effect of AI agents. A shopper might ask an agent to find running shoes under €150, compare reviews and prices, and buy the best pair. That would shift influence from search engines and retailer websites to AI intermediaries.
Deloitte found that about 68% of retail respondents expected to use agentic AI for important operational or enterprise activities within 12 to 24 months. AI referral traffic is already material for some retailers. Over time, it could change product discovery and advertising.
AI in Manufacturing Statistics
Manufacturing differs from software and law because AI has to work with physical equipment. Common uses include predictive maintenance, computer vision, quality inspection, production scheduling, robotics, digital twins and energy savings.
Deployment is harder. A software firm can give an AI tool to thousands of staff quickly. A manufacturer may need to connect AI to machines that are 10, 20 or 30 years old.
Most Manufacturers Have Not Reached Full Scale
McKinsey surveyed more than 100 chief operating officers at manufacturers with yearly revenue above $1 billion. About two-thirds said their AI work was still at the exploration or targeted-implementation stage. Only 2% said AI was fully embedded across operations.
Spending plans are large. Some 93% planned to spend more than 1% of cost of goods sold on digital and AI over the next five years. Almost one-third expected to spend at least 5%.
Advanced Factories Show a Different Picture
The World Economic Forum’s Global Lighthouse Network tracks leading factories. By 2026 it included more than 220 sites. AI and machine learning appeared in about 62% of the top use cases at these sites.
Generative AI’s share of leading solutions rose from 9% in 2024 to 23% in 2025. Traditional machine learning handles prediction and optimization. Generative AI helps workers talk to machines, create instructions and find company knowledge.
Physical AI and Robotics
Deloitte’s 2026 Manufacturing Outlook found that 9% of manufacturers used physical AI, and 22% expected to use it within two years. Physical AI covers robots that act more flexibly instead of repeating a fixed motion.
These robots can move goods, inspect factories, sort parts and work in changing settings. Manufacturing may be where AI has the biggest effect on physical output.
Which Industries Lead AI Adoption?
There is no perfect ranking because the surveys measure different things. Some broad points can still be drawn from the data.
Software has the deepest individual use. With 84% of developers using or planning to use AI and half of professionals using it daily, AI coding tools are close to standard equipment.
Finance has the most mature institutional use. About three-quarters of UK financial firms use AI, and rates among insurers and international banks are above 90%.
Healthcare has grown fast for a high-stakes field. Physician use rose from 38% to 66% in one year, despite strict safety and privacy rules.
Law is changing quickly as specialized tools improve. Media shows high professional use but low consumer comfort with automation. Retail uses AI across many operations, while manufacturing is slower because AI must connect to machines and supply chains.
From AI Assistants to AI Agents
The next shift may be from AI that answers questions to AI that takes actions. Today a person writes a prompt, reads the AI response and makes a decision. In an agent workflow, a person sets a goal, the AI plans and uses software to finish the task, and a person reviews the result.
Examples could include AI that checks transactions for fraud in finance, or AI that summarizes records and books tests in healthcare. In law, it could review thousands of documents and draft a case analysis. In software, it could find a bug, write a fix and submit it for review.
Stanford found that agent deployment stayed in the single digits across most business functions in 2025. Early use is already visible in software and other technology-heavy fields.
Barriers to AI Adoption
Several problems appear across industries. Accuracy is one, because AI models can state wrong information with confidence. That matters most in medicine, finance and law.
Data privacy is another. Firms cannot put confidential customer, patient or legal data into open public tools. Regulation adds limits, since AI decisions in regulated fields must follow existing rules.
Integration is often the hardest part. The challenge is less about getting a model and more about linking it to databases, software and workflows. Staff also need to know when not to trust AI.
Governance gaps are common. Thomson Reuters found that 34% of law-firm professionals used unapproved AI tools. Its 2026 professional-services research found that only 18% of professionals said their organizations tracked AI return on investment, and 40% did not know if their organization did.
AI Investment and the Job Market
The Stanford AI Index shows how much money is involved. Global corporate AI investment reached about $581.7 billion in 2025. Private AI investment alone reached about $344.7 billion, and generative AI investment grew by more than 200% during the year.
Current data points to AI changing parts of jobs more than removing whole occupations. A lawyer may spend less time searching documents. A doctor may spend less time writing notes, and a journalist may automate transcription.
There are early signs of strain at the entry level. Stanford reports that employment among software developers aged 22 to 25 fell almost 20% from 2024. If fewer junior workers are hired, fields such as law, accounting and software will need new ways to train future experts.
The Bottom Line
AI adoption is no longer only a technology-sector story. By 2025, 88% of surveyed organizations used AI somewhere in their business, and 79% regularly used generative AI.
The depth of use differs by industry. Software leads on daily personal use at 84%. Finance has the most mature setup, with 75% of UK firms using AI.
Healthcare use rose from 38% to 66% in a year. Legal generative AI use nearly doubled between 2024 and 2025.
More than half of UK journalists use AI each week, but consumers prefer AI that assists reporters over AI that replaces them. Retailers use AI in security, pricing and forecasting. Manufacturers face the hardest path to scale, although advanced factories show what is possible.
The question for most firms is changing. It is shifting from whether they can use AI to whether they can redesign work around it while keeping accuracy, security and human accountability.
Sources
- Stanford AI Index 2026 Report. Global data on enterprise AI adoption, investment, employment and AI agents.
- Stanford AI Index 2026: Economy. Organizational adoption and investment statistics.
- Bank of England: Artificial Intelligence in UK Financial Services. Survey of AI use across banking, insurance and financial markets.
- American Medical Association: Two in three physicians are using health AI. Physician AI use rising from 38% to 66%.
- Thomson Reuters 2026 AI in Professional Services Report. AI use in legal, tax, accounting, risk and government work.
- Thomson Reuters 2025 Generative AI in Professional Services Report. Adoption data for law firms and professional services.
- Thomson Reuters Institute: Turning law-firm AI strategies into practice. AI strategy, client pressure and workflow change at law firms.
- Stack Overflow 2025 Developer Survey: AI. Developer AI use, trust and agents.
- Reuters Institute: AI adoption by UK journalists and their newsrooms. Survey of 1,004 UK journalists on professional AI use.
- Reuters Institute: Generative AI and News Report 2025. Public attitudes toward AI in journalism.
- Deloitte 2026 Retail Industry Global Outlook. AI use across retail operations, pricing, forecasting and supply chains.
- National Retail Federation: Retail AI Trends 2025. Survey of AI leaders at US retailers.
- McKinsey: From Pilots to Performance, How COOs Can Scale AI in Manufacturing. Survey of large manufacturers on AI deployment and spending.
- Deloitte 2026 Manufacturing Industry Outlook. Smart manufacturing, agentic AI and physical AI.
- World Economic Forum: Global Lighthouse Network. AI and generative AI use in advanced factories.
