The fast growth of artificial intelligence (AI) has caused changes in many industries, including healthcare and life sciences. One big AI development is the use of AI agents. These are software programs powered by large language models (LLMs) that can plan, think, and carry out complex tasks by themselves. AI agents are now commonly used to automate customer service, make operations more efficient, and improve business processes. A 2025 Google Cloud study found that over half (52%) of executives said their organizations are actively using AI agents, and 39% have more than ten AI agents in use.
Although many sectors are using AI agents at similar levels, healthcare and life sciences in the United States and worldwide are still slowly adding AI agents compared to sectors like financial services and retail. This article will look at how regional priorities affect AI agent use, especially in the U.S. healthcare sector. It will also review how different industries use AI agents, the benefits they bring, and how AI can change healthcare management and operations by automating workflows.
AI agent applications differ between regions, with each region focusing on certain business tasks. The Google Cloud study shows:
In the United States, there is a clear interest in using AI agents in customer service, marketing, and security. But there is special attention to healthcare needs and privacy rules. The U.S. healthcare system is complex and has strict privacy laws like HIPAA. This means AI platforms must be secure and reliable. They not only automate front-office tasks but also protect patient privacy.
Different industries use AI agents to solve specific problems:
In healthcare and life sciences, AI agents mainly support making operations more efficient, improving patient experiences, and aiding clinical workflows. However, adoption is still growing. Healthcare faces challenges like rising costs, more complex patients, and fewer workers. AI can help by automating tasks and managing data.
Healthcare and life sciences groups are starting to see how AI can automate routine tasks and improve care.
For example, Humana used conversational AI to cut down on pre-service calls, improving the experience for both patients and providers. University Hospitals Coventry and Warwickshire NHS Trust used IBM watsonx.ai technology to see 700 more patients each week. These examples show AI can support efficient operation and good care.
Even though AI agents have many benefits, healthcare is slower than other industries in using them. Some reasons include:
Healthcare groups need modern data management models that keep data accurate, safe, and ready for AI. Cloud platforms that support both local and cloud systems provide scalable, secure areas needed for AI that meets rules and compliance.
Health care in the United States is complex. It includes providers, hospitals, insurance payors, and pharmaceutical companies. This creates a varied setting for AI agent use.
These examples show how U.S. healthcare organizations use AI to lower costs, improve patient interaction, and make operations more flexible.
AI agents do more than talk to customers. They also automate many healthcare administrative tasks that take a lot of effort and are prone to mistakes. Automating routine work frees staff time and makes work more accurate.
These tasks help healthcare centers handle more patients, fewer staff, and financial limits.
The Google Cloud study shows that early AI agent users, meaning those who spend at least half their AI budget on AI agents, get better returns on investment (ROI). Key points include:
For medical practice administrators, owners, and IT managers, these numbers show AI agents are not just for cutting costs but can help grow operations and improve patient connections.
Security and privacy remain very important in healthcare. About 37% of executives say data privacy and security are top concerns when using AI agents. Companies must carefully pick AI vendors who focus on:
Hybrid cloud platforms that support AI work loads offer safe and flexible places to keep and use data. IBM’s watsonx.ai platform is an example. It mixes AI and secure cloud technology to help healthcare groups handle complex data while following rules.
As AI use grows, healthcare in the U.S. will likely see AI agents used more in main operations. For example:
Reaching these goals means investing wisely in AI, training staff well, and building strong data systems.
The U.S. healthcare sector is slowly adding AI agents, shaped by regional goals and industry needs. Healthcare has been slower than financial and retail sectors in using AI agents, but examples from payors, providers, and drug companies show clear benefits. Front-office phone automation, insurance claim processing, patient engagement, and clinical workflow support through AI agents offer ways to improve efficiency and care.
Medical practice administrators and IT managers play a key role in adopting AI by focusing on safe and efficient agent use. By learning from early users who spend large parts of their AI budget on AI agents and using AI responsibly with current systems, healthcare groups in the U.S. can improve services, cut workloads, and get financial returns. With ongoing progress and more focus on security and data management, AI agents will have a bigger role in healthcare administration.
52% of executives report their organizations are actively using AI agents, with 39% having launched more than ten AI agents within their companies.
Agentic AI early adopters represent 13% of executives whose organizations dedicate at least 50% of their future AI budget to AI agents and have deeply embedded agents across operations, achieving higher ROI with 88% seeing returns versus a 74% average.
Top areas include customer service and experience (43% early adopters vs. 36% average), marketing effectiveness (41% vs. 33%), security operations (40% vs. 30%), and software development improvements (37% vs. 27%).
AI agents enable standardized processes and automate complex tasks independently across locations, ensuring consistent execution, decision-making, and service delivery, reducing variability caused by human factors or regional differences.
Data privacy and security rank as the top concern (37%), followed by integration with existing systems and cost considerations, emphasizing the need for strong governance and modern data strategies.
Most industries show consistent adoption, with Healthcare & Life Sciences slightly lagging. Financial services focus on fraud detection (43%), retail on quality control (39%), and telecommunications on network automation (39%).
Europe prioritizes AI-enhanced tech support, JAPAC emphasizes customer service, and Latin America focuses on marketing, reflecting varied regional operational needs and market dynamics.
74% of executives report achieving ROI within the first year from generative AI initiatives, with over half (56%) linking these efforts to actual business growth and revenue increases.
Increased investment in AI, including reallocating budgets to generative AI (48%), correlates with reported business growth (56%) and revenue gains (53% of growth-driven organizations citing 6-10% growth).
Oliver Parker advises treating AI agents as core engines for competitive growth by securing dedicated budgets, redesigning business processes, and adopting modern data strategies with strong governance to overcome integration and security challenges.
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