Artificial intelligence (AI) is an important focus for many businesses, including healthcare. For medical practice administrators, owners, and IT managers in the United States, knowing how to use AI well is key. It can help improve how work is done, lower costs, and make patient services better. The MIT CISR Enterprise AI Maturity Model is one way to understand how organizations adopt AI. It shows four stages of AI maturity. Stage 3, often called the phase where companies “develop AI ways of working,” focuses on growing AI from small tests to wide use that changes how business is run.
This article explains how healthcare groups in the U.S. can move beyond early AI tests to reach the third stage of AI maturity. It shows how industrialized AI can improve work efficiency and spark new ideas, why scaling AI at this stage matters for staying competitive, and how it affects healthcare administration. It also talks about AI and workflow automation in front-office tasks, which are important for medical offices. Companies like Simbo AI provide AI-powered phone automation and answering services in this area.
The MIT CISR Enterprise AI Maturity Model has four stages to help guide companies in using AI well:
Stage 3 is when AI moves from small projects to full use within the organization. For U.S. medical practices and healthcare sites, this means using AI in many parts of patient care, office work, and data handling, instead of just tests or pilots.
Key parts of Stage 3 include building an AI system that can support many AI tools working at once. Companies at this stage focus on:
The result is a more efficient organization that can lower costs and improve patient services.
One example outside healthcare is Ally Bank, the largest all-digital bank in the U.S. It created Ally.ai, a platform that uses different AI tools and cuts customer call time by three minutes on average. This shows how industrialized AI can improve service speed. Healthcare offices can use similar AI tools to improve patient contacts and front-office work.
Medical office workers often face many challenges. They have to handle patient questions, set up appointments, check insurance, and follow up. These front-office duties usually need a lot of human work, causing delays, long wait times, and sometimes mistakes. At Stage 3, AI can help by automating and improving these important tasks.
Research shows companies at Stage 3 or higher often perform better financially than others. According to a survey by MIT CISR of 721 companies, those with advanced AI use get better at working efficiently and improving customer or patient experience. For healthcare, this means quicker patient check-ins, better data gathering, and smoother care coordination.
Healthcare businesses in the U.S. at Stage 3 use AI beyond just small projects in daily work. The AI Maturity Model by Veritis says investing at least $1 million in AI can lead to bigger profits, faster adaptation, and leading in the market.
For medical practice administrators and IT managers, investing in AI tools and staff training helps improve workflows and keeps the practice competitive. It also lets clinical staff spend more time on patient care instead of paperwork.
One way Stage 3 AI is used in healthcare is automating front-office work. This includes handling phone calls, scheduling, patient records, and billing questions. These jobs take a lot of time and can be inefficient if done only by people.
Automating these tasks with AI has benefits:
Simbo AI is a company that shows how AI can improve front-office phone work in healthcare. Their services help offices automate setting appointments, refilling prescriptions, and answering patient questions. This reduces missed calls, shortens waits, and improves patient satisfaction.
At Stage 3, tools like Simbo AI’s phone automation are examples of industrialized AI. They can be scaled up and put into daily work rather than being just tests. This helps medical offices across the U.S., from small clinics to big groups, make workflows more efficient.
Reaching Stage 3 is not only about buying technology. It also means working on company culture and skills. Healthcare providers must create training programs to help office and IT staff understand what AI can and cannot do. They also need strong data systems that keep health information accessible and safe according to HIPAA rules.
The AI Maturity Model says having good data systems and a workforce open to AI are very important in this stage. Medical offices should focus on:
Kaiser Permanente shows how to use AI responsibly. They have seven principles focused on privacy, honesty, and trust. This helps healthcare organizations use AI ethically and with patients in mind as they grow AI use.
Using AI widely lets healthcare groups track important results in new ways. At Stage 3, they try to measure things like:
Data from related industries show these benefits too. For example, Guardian Life Insurance uses generative AI to help underwriters save about five hours a day by automating document summaries and decision help.
In healthcare, industrialized AI can speed up paperwork, insurance handling, and referral processing. Medical office leaders using AI platforms can see better scheduling, fewer errors, and stronger patient loyalty.
Though Stage 3 AI use has clear benefits, medical office managers must watch out for challenges:
Planning for these factors helps medical offices move from small AI projects to full AI use without stopping work.
For healthcare groups in the United States, reaching Stage 3 AI maturity is key to moving from thinking about AI to actually using it well. Industrializing AI lets medical offices streamline work, improve patient contact, and lower admin work. This stage needs not only technology spending but also building AI skills and using AI responsibly.
Companies like Simbo AI show how AI can help with front-office phone tasks. Their solutions can be scaled up to improve patient communication and efficiency in medical offices. Healthcare leaders who learn and use the Stage 3 model can improve finances and service quality at the same time. This helps their organizations continue to grow and stay strong as healthcare changes.
The MIT CISR Enterprise AI Maturity Model describes four stages of AI maturity—Experiment and Prepare, Build Pilots and Capabilities, Develop AI Ways of Working, and Become AI Future Ready—illustrating the capabilities needed at each stage to effectively leverage AI.
The four stages are: 1) Experiment and Prepare, 2) Build Pilots and Capabilities, 3) Develop AI Ways of Working, and 4) Become AI Future Ready.
Key capabilities at Stage 1 include educating the workforce on AI, establishing acceptable use policies, making data accessible, and identifying human roles in AI systems.
In Stage 2, organizations focus on setting up AI pilots, defining metrics, simplifying processes, and consolidating organizational data for effective AI use.
Stage 3 emphasizes industrializing AI, scaling platforms, promoting a test-and-learn culture, and integrating AI into business processes.
Organizations in Stage 4 are termed ‘AI future-ready’ as they embed AI in all decision-making, leveraging proprietary AI for internal enhancement and selling AI capabilities as a service.
The research found that financial performance improves with each stage of AI maturity, with enterprises in stages 3 and 4 performing above industry averages.
Kaiser Permanente exemplifies Stage 1 by defining principles for responsible AI use, which include privacy, reliability, and customer prioritization.
Ally, in Stage 3, has integrated AI that improved efficiency in customer interactions and accelerated marketing efforts, demonstrating significant advancements in AI application.
The interviews revealed that the combination of people and platforms using various AI types—analytical, generative, agentic, and robotic—presents the greatest future value opportunity.
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