Implementing AI yields a variety of benefits for enterprises of all sizes, such as improving efficiency by taking on repetitive tasks and saving time by reviewing and analyzing large amounts of data. It can be used to streamline supply chains, simplify demand forecasting, and make inventory control more accurate. Still, to become useful, it needs to be adopted by all internal and external stakeholders.
According to McKinsey & Company’s State of AI in 2025 report, the surveyed companies were in the following phases of AI usage:
88% report using AI in at least one business function
7% have fully deployed AI across the company
31% were scaling AI across the organization
30% were piloting AI for a first use case in business
32% were in early testing of AI
For leadership, it entails building teams that are comfortable working with data, open to new tools, and skilled in reviewing and validating outputs. It also means pacing the transition so that everyone learns and progresses equally.
Employees need a practical understanding of when and when not to use AI. They need to see AI as an assistant that enhances their judgment rather than replacing it. Training, iteration, and ongoing engagement all play a role in the successful use of AI.
Healthcare
Historically, the healthcare industry pursued adoption of advanced technology in a gradual fashion. However, the industry’s AI transformation evolved quickly. The technology is capable of analyzing records from EHRs and other data and identifying patients at risk of chronic conditions or those likely to experience complications. It can detect diseases faster and determine treatments with more effective care pathways.
Healthcare personnel currently use AI technology and data for patient diagnosis. Clinicians use AI algorithms to analyze medical imaging data, such as X-rays, MRIs, and CT scans, to assist in making swift and accurate diagnoses. For example, a UK study found that an AI tool can successfully detect 64% of epilepsy brain lesions previously missed by radiologists. Trained on the MRI scans of over 1,100 adults and children globally, the AI tool was able to spot lesions more quickly than a doctor and discover tiny or obscured ones that humans overlooked.
Government
AI transformation is already being deployed across government sectors for a wide range of uses, largely to improve how organizations deliver public services. Some processes include drafting policy documents, analyzing and summarizing global legislation and policies, simulating urban planning scenarios, and engaging with the public. According to the 2026 State of Digital Government Trends in Websites and Customer Service report:
55% of government organizations now use AI.
64% of government call centers use AI to operate as consolidated hubs, streamlining operations.
78% of local governments plan to use AI to improve service delivery in 2026.
The United States Department of Agriculture (USDA) Forest Service needed to make faster, more informed decisions in combating wildfires. Yet the available data was fragmented, incomplete, and constantly changing. It was difficult to determine how to optimize crews and equipment. Eventually, the Forest Service teams leveraged a variety of AI-based tools and applications in their fire management efforts, including the Risk Management Assistance Dashboard, which is a set of science-based tools for wildfire incident managers. For example they use one of the tools to predict where downed or weakened trees might injure or trap firefighters, daily fire weather, and resource deployments that enable responders to adapt to changing conditions.
Manufacturing
Manufacturers are proactively exploring strategies to incorporate AI into their operations to streamline processes and be more cost-effective. AI introduces a fundamentally different architecture across this value chain. By embedding real-time sensing and predictive intelligence into execution, it enables operations to shift from reactive, scheduled execution to adaptive, predictive and learning-based systems that respond dynamically to changing conditions.
According to a survey conducted by the National Association of Manufacturers (NAM), 72% of the companies deploying AI have reduced costs and improved operational efficiency.
Artificial intelligence (AI) tools are key to effective data collection and data management solutions in manufacturing. They offer robust data acquisition techniques from a wide range of data sources, including sensors, equipment, automation, and control systems.
While automation and data analysis are significant contributions of AI, manufacturers are implementing the technology in ways that keep workers as the central drivers and decision-makers in AI processes. AI systems can quickly process large amounts of data, identify patterns, and automate repetitive tasks. However, the systems lack humans’ intuition and context-specific problem-solving skills.
Some of the ways manufacturers are putting AI to use include:
Supply chain. AI enhances supply chain efficiency and resilience by analyzing real-time data and trends and recommending improvements or contingency plans to mitigate risks.
Predictive maintenance. Generative AI proactively addresses equipment issues by analyzing sensor data and usage patterns to predict potential malfunctions and schedule maintenance before the issue causes downtime.
Quality control and defect detection. Companies are using machine learning (ML) to inspect and maintain product quality.
Education
In the academic arena, the global pandemic prompted educators and policymakers to adopt digital technologies to support online instruction and other off-campus educational options. AI technology has now made its way into classrooms. A survey of more than 1,100 U.S. students across all levels of the academic spectrum revealed that 90% have used AI academically.
Many of the respondents cited using the technology to brainstorm, refine ideas, draft outlines, and improve their writing.
Given the high levels of usage among students, colleges and universities are recognizing the need for AI fluency, which includes guidance on using AI responsibly, critically, and effectively.
The demand for skilled AI workers in industries is growing, so many universities are incorporating AI fluency programs into their learning curricula.
