Artificial intelligence (AI) has moved quickly in the last few years. For many practice owners, it can feel like a full-time job trying to keep up with what is changing, what is real, and what is actually useful in a practice setting.
The goal of this article is not to cover every development in AI. It is to give practical, real-world examples of how AI can fit into an eyecare practice today in order to increase productivity, improve efficiency, and enhance the patient experience.
With any new tool in a practice setting, it should be evaluated through two simple questions: Does it enhance the patient experience? And does it increase the efficiency of a practice?
So far, the highest return on investment for AI has been in administrative work. That includes patient intake, front desk operations, scheduling, documentation, billing support, and patient engagement.
These areas may not be the flashiest use cases, but they are often where the most immediate value can be created. In a recent American Medical Association survey, physicians identified reducing administrative burden through automation as the biggest opportunity for AI in healthcare.1
A brief definition of AI
In simple terms, artificial intelligence, or AI, refers to software that can use information to make predictions, recommendations, or decisions toward a defined goal.2 In a practice setting, that can mean tools that summarize information, recognize patterns, answer questions, or help complete repetitive workflows. The key point is that AI should support the practice and the patient experience, not replace clinical judgment.
Why AI is gaining traction in practice operations
AI is most useful when it reduces repetitive work and helps teams process information more efficiently. That is why administrative workflows have become such a natural starting point.
Front desk and admin teams deal with repetitive questions and repeated processes all day. They collect information, route calls, answer common questions, document conversations, and guide patients through similar workflows again and again.
In simple terms, AI tends to work best when the job involves:
- Repeated patterns
- Natural language
- Decision trees
- Summarization and documentation
- High-volume, rules-based tasks
This is also why healthcare AI is often discussed around documentation and information processing. The US Agency for Healthcare Research and Quality notes that natural language processing can help summarize lengthy clinical notes, extract key findings, and convert unstructured documentation into more usable data.3
The important point is not that AI replaces staff. It is that it can take pressure off staff by handling parts of the workflow that are repetitive and operational in nature, allowing people to focus on the tasks that require judgment, empathy, and human interaction.
Areas where AI boosts efficiency
Artificial intelligence can be used effectively to streamline several tasks, including:
- Patient intake and documentation
- Scheduling and front desk operations
- Patient engagement and retention
Patient intake and documentation
One of the clearest use cases for AI is reducing documentation burden and improving intake efficiency.
This matters because documentation already takes up a large portion of the clinical day. In a time-motion study of ambulatory physicians, physicians spent 27.0% of their office day on direct clinical face time with patients and 49.2% on EHR and desk work.4
Many practices are already seeing the rise of AI scribes that help generate draft notes during or after exams. Early studies of ambient AI scribes have found improvements in documentation efficiency and lower perceived documentation burden.5 There are also tools, such as Eluve, that collect patient history before the visit through digital forms, automated outreach, or conversational systems that gather relevant information in advance.
The challenge: Staff and doctors spend a large amount of time collecting, organizing, and documenting information.4 This is necessary work, but it can also create friction in the patient experience and take time away from the visit itself.
The solution: AI can assist in several ways, such as:
- Generating draft notes
- Collecting intake information before the patient arrives
- Organizing responses into a cleaner format
- Reducing repetitive data entry
These tools may handle sensitive patient information, which can raise privacy and security concerns. A best practice when evaluating any AI provider is to ask whether they have signed business associate agreements (BAAs) with the technology providers across their stack that may handle protected health information. Practices should also ask for a security and compliance letter outlining how patient data is accessed, transferred, stored, and protected.
The implications: The goal is not to remove the doctor from the process. The goal is to reduce administrative drag so doctors can spend more time focused on the patient and less time on repetitive documentation tasks.
Scheduling and front desk operations
For many practices, this is where the opportunity is most immediate.
The challenge: Phones do not stop ringing. Staff answer the same questions repeatedly. Patients get put on hold. Calls are missed or dropped during busy periods. Even strong front desk teams can get overwhelmed when the volume is high.
The solution: An AI receptionist can help alleviate this burden.
An AI receptionist is a voice agent that can:
- Answer common FAQs
- Schedule appointments in the EMR/PM
- Route or transfer more complex calls to staff
- Provide after-hours coverage
- Reduce the volume of repetitive front desk calls
- Give clear concise information while taking the “emotion” out of the equation
Examples of AI receptionist products being introduced into eye care and other medical practices include RingCentral, Zo by Zocdoc, and Vocca.
The implications: The value is not just convenience. It is operational protection. When AI handles the predictable part of phone volume, staff can focus on in-office patients, escalations, and cases that truly need human attention.
Patient engagement and retention
The challenge: Many offices already use reminders and follow-up communication, but these workflows are often inconsistent when teams are busy. Missed appointments can also create measurable revenue loss, with one study estimating each missed appointment represented about $292.70 in lost billing charges.6 AI can help make patient communication more timely and scalable without adding more manual work.
The solution: AI can also support the practice after the visit.
Examples include:
- Appointment reminders
- Follow-up communication after visits
- Recall campaigns for annual exams
- Reactivation outreach for inactive patients
Platforms supporting these types of patient engagement workflows include Phreesia and Weave.
There is evidence that reminders can improve appointment attendance. A Cochrane review found that mobile phone messaging reminders increased attendance compared with no reminders and postal reminders.7
The implication: In many practices, follow-up falls behind not because it is unimportant, but because the team is stretched thin. AI can help close that gap and make communication more consistent. That consistency can improve retention, reduce no-shows, and keep patients more engaged with the practice over time.7
AI through the lens of ROI
It is easy to get distracted by buzzwords of AI. A better way to evaluate it is as a workflow and business tool.
Before adopting any AI system, practices should ask:
- Is this enhancing my patient’s experience?
- Is this increasing revenue, reducing cost, or both?
Those two questions create a much more practical framework than simply asking whether a tool is “innovative.” Let’s walk through one example.
Example: The business case for an AI receptionist
An AI receptionist is a voice agent that can answer common questions, book appointments into the EMR/PM, and transfer callers to staff when needed.
The ROI comes from three places:
- Increased patient experience
- Lowered cost
- Revenue captured
Do the math: simple workflow calculations
Imagine a practice receives 300 calls per week and the average call lasts 3 minutes.
That equals:
- 900 minutes per week
- 15 hours of phone time per week
If AI handles even 40% of routine call volume, that would save:
- 360 minutes per week
- 6 staff hours per week
- Roughly 24 staff hours per month
That is meaningful operational relief for a busy office.
Revenue impact
Now consider the revenue side. If the office is missing calls during lunch, busy periods, or after hours, some of those missed calls are likely missed scheduling opportunities.
Even recovering a small percentage of those missed calls can have a meaningful impact over time. One Medical Group Management Association (MGMA) case study found that some clinics had more than 50% of incoming calls going to voicemail and that reducing missed calls was associated with higher schedule utilization and a 17% increase in work relative value units (RVUs), a measure of physician services and productivity.8 That is why AI should not just be viewed as a cost-saving tool. In many cases, it also helps capture demand that would otherwise slip through the cracks.
The value of an AI receptionist is not just that it sounds modern. It is that it can improve access, protect staff time, and reduce missed opportunities in a measurable way.
Key takeaways
- Eyecare providers should think of AI as a set of tools that can be applied to specific workflow bottlenecks (e.g., missed calls).
- The best place to start is usually not the most advanced use case. It is the one where friction is highest and ROI is easiest to measure.
- For many practices, that starting point is administrative work. Administrative workflows are repetitive, language-heavy, and full of small inefficiencies that add up across the day. They are also some of the easiest areas to evaluate in terms of time saved, calls handled, appointments booked, and workload reduced.
Conclusion
AI adoption does not need to be all-or-nothing. Practices do not need to overhaul everything at once.
The better approach is to start with one real problem, implement carefully, and measure the result. For many optometry practices, that means starting with intake, scheduling, phones, documentation, or patient communication.
The highest-value use cases in optometry today are often not the most futuristic ones. They are the practical tools that reduce administrative burden, protect staff time, and help the practice run more efficiently.
The practices that start small and solve real workflow problems will be in the strongest position to benefit from AI, both operationally and financially.
