Developing and Validating Measurement Instruments to Assess AI Capability in Organizations Based on Resource-Based Theory

The resource-based theory (RBT) is a way to explain how organizations gain an advantage by building special resources and skills. When we use this theory for AI, it shows how things like data, algorithms, AI skills, and technology work together to create an organization’s AI ability.

Patrick Mikalef, a professor in Data Science, says that organizations have certain AI resources. When they use these resources well, they form an AI capability. This is not just about owning AI tools but about using them effectively to improve business work. In healthcare, this means applying AI to front-office work, patient communication, scheduling, billing, and helping decisions.

Manjul Gupta studies how culture affects technology use. He points out that both company culture and national culture influence how AI resources are developed and used. Healthcare groups in the U.S. may face cultural challenges or different readiness levels that impact AI success. For example, a practice that welcomes digital change is more likely to use AI well than one that resists it.

What Are AI-Specific Resources?

AI-specific resources are both physical and non-physical things that help AI work well inside an organization. These include:

• Data: Good data is very important. A healthcare provider with lots of accurate patient data can train AI systems to handle tasks like booking appointments or sorting patient questions.
• Algorithms: These are math models and rules that let AI work. In healthcare, algorithms can find patterns, set priorities, or guide decisions.
• Technological Infrastructure: This means hardware, software, cloud services, and networks that allow AI apps to run smoothly and safely.

Together, these resources make up the base for AI ability and decide how well AI works and what results it gives. Just having AI technology is not enough; these resources must be well managed and connected.

Why Measuring AI Capability Matters to Medical Practice Administrators and IT Managers

In healthcare, especially medical practices in the U.S., spending on new technology needs to show clear benefits. Measuring AI capability gives leaders a way to check how ready their practice is to use AI and if their AI resources are useful.

Mikalef’s research created and tested a tool that measures AI capability by looking at how much AI resources are present and used well in an organization. This tool helps managers find areas that need fixing, such as better IT systems, more staff AI training, or higher data quality.

For medical owners and IT managers, this offers a way to judge risks and the possible benefits from using AI tools like Simbo AI’s phone automation. Without measurement, using AI might be a guess instead of a planned choice.

Impact of AI Capability on Organizational Creativity and Firm Performance

A key finding from Mikalef and Gupta’s study is that higher AI capability helps organizations become more creative and perform better. Creativity means coming up with new ideas, improving processes, or trying new services.

In healthcare, this could mean creating better ways to communicate with patients using AI or changing workflows to make patients happier and lessen staff work. Practices with stronger AI ability often use AI for better appointment scheduling or quick automated call answering.

Firm performance means how well a business does, judged by efficiency, money results, and market strength. The study found that places with higher AI ability tend to do better because they use AI to cut costs, improve services, and make faster decisions.

Medical practice managers in the U.S., who often work with tight budgets and strict rules, see that better AI ability helps their clinics run well while giving good patient care.

AI Integration in Medical Practice Workflow Automation

Good AI ability supports workflow automation, which is key for healthcare groups wanting more efficiency. Front-office phone automation, like Simbo AI provides, changes how patient calls are handled and lets staff focus on harder tasks.

AI answering services can work all day and night, giving patients quick answers about appointments, office hours, and basic health info. This lowers wait times and call loads for human workers. Automation can also do more complex jobs like checking patient details, reminding patients of visits, and doing early symptom checks.

This kind of automation lets medical offices:

• Make patient communication faster and more reliable.
• Cut down missed appointments with better scheduling.
• Lower staff work, reducing burnout risks.
• Save money by automating repeated front-office tasks.

All these points help improve firm results and creativity by letting people focus on important clinical and strategic work.

Applying AI Capability Measurement in the U.S. Healthcare Environment

Healthcare leaders and IT managers in the U.S. work in a setting with special challenges like following HIPAA rules, higher patient demands, and budget limits. These make it necessary to adopt AI carefully and step-by-step.

Mikalef’s AI capability measurement tool helps check if a practice is ready for AI-based front-office automation. It looks at whether data systems meet AI needs, if algorithms are good and flexible enough for healthcare, and if infrastructure can support AI safely.

Cultural readiness is also important, based on Gupta’s research about company culture. Many U.S. healthcare staff worry about technology replacing people. Noticing and dealing with these feelings early can make AI adoption smoother and make sure technology helps care goals instead of hurting them.

Measuring AI ability also fits with value-based care models in U.S. healthcare. As providers try to give better care at lower costs, AI automation becomes a tool to improve efficiency without lowering patient satisfaction.

The Role of Simbo AI in Enhancing Front-Office Operations

Simbo AI works on front-office phone automation using AI. Based in the U.S., Simbo AI’s solutions show how AI capability concepts from Mikalef and Gupta’s research can work in real life.

By focusing on automating phone answering, Simbo AI helps medical offices improve patient communication. Since front-office communication is a big part of patient experience, using AI in these systems matches the research connecting AI ability to firm performance.

For managers, using Simbo AI means using data, algorithms, and infrastructure — the core parts of AI capability — to improve workflows. It also shows how tested AI ability helps organizations be creative by letting them rethink patient interactions without adding many staff.

Summary of Key Points Relevant for Healthcare Leaders in the U.S.

• AI capability comes from linking data, algorithms, and technology into a system that helps the organization succeed.
• Measuring AI capability helps medical practices understand where they stand and how to improve AI resource use.
• Research shows stronger AI capability improves creativity, leading to better patient communication and new process ideas.
• AI capability also boosts overall performance, letting practices work more efficiently and compete better.
• Healthcare groups in the U.S. must consider culture and legal rules when using AI solutions.
• AI workflow automation, like Simbo AI’s phone answering, lowers staff workload and improves patient experience, aiding practice performance.
• The measurement tool developed by Patrick Mikalef’s team helps healthcare organizations measure and build their AI capabilities.

By knowing and using these ideas, medical practice managers, owners, and IT leaders in the U.S. can make better choices about AI investments, improve work processes, and offer better healthcare services to patients with the help of artificial intelligence.