Developing a Business Value-Based Model for Intelligent Automation: How Technologies Can Transform Knowledge and Service Work

Intelligent automation uses advanced tools like artificial intelligence (AI), machine learning, robotic process automation (RPA), and natural language processing (NLP) to do tasks that usually need human thinking. Simple automation handles routine, rule-based tasks. Intelligent automation can do more complex jobs like making decisions, recognizing speech, understanding data, and predicting outcomes.

Recent research says IA automates knowledge and service work by using new AI technologies. This helps organizations work better and improve their services. It works well where lots of administration, scheduling, and communication happen—like in U.S. medical practices.

Why Intelligent Automation Matters in Healthcare Management

Healthcare groups have a big challenge: they need to improve administrative tasks and keep good patient care. Front-office jobs like scheduling appointments, talking with patients, reminder calls, and billing questions take up a lot of time. These tasks keep staff busy and take them away from patient care. Intelligent automation can help by handling routine calls, messages, and data work.

Using IA means less overhead, faster answers, and fewer mistakes for healthcare managers. This helps patients feel better served and helps healthcare groups follow rules like HIPAA. Automation also helps when patient numbers change, allowing practices to grow without hiring many more staff.

Developing a Business Value-Based Model for Intelligent Automation

A recent study in The Journal of Strategic Information Systems shared a business value model for IA. This helps healthcare groups see how automation can bring clear business benefits.

Key parts of the model include:

• Efficiency Improvement: Cutting down manual work and automating repeated tasks.
• Decision Optimization: Using AI to make faster, better decisions like sorting patients or billing.
• Service Enhancement: Making patients happier by giving quick and accurate answers.
• Cost Reduction: Speeding up work and lowering costs linked to staff time and mistakes.
• Sustainability: Combining automation with company social and environmental goals to keep balance.

The model says IA is not just about technology. It also depends on company culture, employee roles, and the organization’s skills to get full benefits.

Integration Challenges and the Role of Internal Environment

Using intelligent automation well depends a lot on the healthcare group’s internal setup. Important factors include:

• Absorptive Capacity: How well the group can learn and use new knowledge and tech.
• Social Capital Competency: Good communication and teamwork to support automation.
• Employee Socio-Behavioral Concerns: Handling worries among staff about losing jobs or changes to their work.

Research with 207 large firms in 2022 showed that worries among employees are big barriers to using IA. In healthcare, staff might resist automating front desk jobs because they fear losing personal contact or job security. So, managers must work with employees through training and open talks.

Another important part involves company social responsibility (CSR) plans. IA can improve economic and environmental goals but may hurt social goals if CSR is informal or missing. Having clear CSR plans helps protect workers and communities while improving efficiency.

Intelligent Automation in Knowledge and Service Sectors

Healthcare administration is part of knowledge and service work where IA is changing things. Automating phone answering, scheduling, and admin work shows how knowledge processes can be automated. Simbo AI is a company that uses AI for front-office phone work in healthcare.

By using AI virtual agents for phone systems, medical offices can cut wait times, reduce missed calls, and help patients right away. These agents use natural language processing to understand patient questions, direct calls, or even book appointments, lowering the need for many human steps.

This kind of service fits the four parts of IA described by Pascal Bornet—former AI leader at EY and McKinsey:

• Vision: Using computer vision to recognize images, mostly used in health imaging, not phone automation.
• Execution: Robotic process automation and workflows that handle tasks like call routing and scheduling.
• Language: Natural language processing lets chatbots and virtual agents talk clearly with patients.
• Thinking & Learning: Machine learning analyzes call patterns and makes responses better over time.

This shows how intelligent automation acts like human interaction and helps staff by taking care of routine communication tasks.

AI and Workflow Automation in Healthcare Administration

Using AI combined with workflow automation helps healthcare offices work better, reduce delays, and cut admin costs. In the U.S., where resources are tight and patient numbers are high, this is very useful.

AI-driven workflow automation can:

• Automate appointment scheduling, including online and phone bookings, changes, and cancellations.
• Send automated reminder calls and messages to lower no-shows and help patients follow care plans.
• Manage billing and claims by checking insurance info, making invoices, and handling billing questions faster and more accurately.
• Check data for errors and make sure it meets rules and standards.

McKinsey says AI automation will affect more work hours than expected. It was thought to impact 21.5% of work hours by 2030, but with new AI, this may rise to 29.5%. This means tools like Simbo AI will handle more work tasks in healthcare soon.

Pascal Bornet also says people should stay central in automation. Healthcare leaders must give training, keep control systems clear, and protect data quality during automation. Good adoption needs management support and a mix of automation tools matching the organization’s needs.

Addressing Risks and Ensuring Sustainability

IA has many benefits but also new risks. Keeping data correct, private, and secure is very important because healthcare info is sensitive. Professionals need strong controls and ongoing checks on AI systems to stop mistakes or misuse.

The social side of sustainability also needs care. Automation might cause job loss or worker unhappiness if handled poorly. Companies with clear social responsibility plans can better use IA to keep social well-being—like worker morale, fair jobs, and community trust.

Medical practices in the U.S. should find a balanced way where automation helps economic and environmental goals without hurting workplace social health. This balance fits with the triple bottom line idea—focusing on economic, environmental, and social results—in healthcare.

Future Directions in Intelligent Automation Research and Practice

The review of intelligent automation shows there are 12 main research gaps. More studies should bring together knowledge from information systems, AI, organizational research, and business strategy.

For healthcare managers, this means staying updated on new developments and best ways to use IA. Moving from small tests to full and scalable automation needs ongoing checks of technology, workflow changes, and staff readiness.

Working with providers like Simbo AI that offer AI-based front-office services can help healthcare offices handle this change. Using AI phone systems, chatbots, and workflow tools can lessen admin work, improve patient access, and keep healthcare rules.

In summary, intelligent automation can help healthcare practices in the U.S. work better, cut costs, and improve patient care. Success depends on knowing its business value, the organization’s ability, and managing social effects.

By using a business value model, healthcare leaders can plan and use AI well. Keeping employees informed, gaining management support, and linking automation to formal sustainability and social responsibility plans will help IA contribute well to healthcare services.