By 2026, the question is no longer whether AI will enter call centers, but how it will coexist with human teams. In most sectors, more than 90% of customer interactions already go through an AI-assisted touchpoint (intelligent routing, dynamic scripts, sentiment analysis, etc.). Forecasts mention up to 95% of customer interactions partially or fully handled by AI by the end of 2025.
For a decision-maker (clinic management, network of practices, radiology group, laboratory, etc.), the dilemma is very concrete: should you keep strengthening a traditional call center floor or invest in an AI Receptionist / AI voice agent to handle part of the incoming calls? The answer is not limited to the per-minute cost: it also concerns service quality, the ability to absorb activity peaks, staff retention, and the overall image of your organization.
In this comparison, we take a 2026 horizon with a strategic view: how to arbitrate between a traditional call center and AI for call centers, and how to build a truly efficient hybrid model for your phone reception.
1. A Legacy Model Under Pressure
Structural costs that no longer decrease
The traditional call center model relies on large teams, organized over extended time slots, with a high share of fixed costs. For a medium-sized medical call center (8 to 15 medical secretaries), payroll usually represents 60 to 75% of the total cost, once you include:
- Salaries and social charges
- Recruitment and initial training
- Management (supervisors, floor managers)
- Premises, workstations, telephony
Over 3 years, the full cost of an internal or outsourced call center can easily reach several hundred thousand euros, even for a modest network of practices. At the same time, pressure on margins and consultation fees leaves little room to absorb recurring cost increases.
Turnover and loss of know-how
Call center jobs are among the most exposed to turnover. In many environments, an annual churn rate of 25 to 40% is not unusual. In practice, this means:
- Regular loss of staff trained in the medical specifics of your organization
- Recurring training costs (2 to 4 weeks to make an agent fully operational on your protocols)
- Instability in the perceived quality of reception by patients
For a network of practices with complex instructions (relative emergencies, exam management, prerequisites before appointments), each departure takes away part of the knowledge capital, which must then be painstakingly rebuilt.
Uneven quality from one call to another
Even with detailed scripts, the reality is that:
- End-of-day fatigue lengthens handling times
- Stress during call peaks increases data entry errors
- Personality differences create major variations in tone and educational approach
On the ground, this results in an uneven experience for patients: some will be handled impeccably, others will hang up after several minutes on hold or after what they perceive as a not very empathetic response. In the medical sector, this variability in quality is a risk both in terms of image and organization (poor triage, inefficient use of slots, missed appointments).
Opening hours disconnected from actual usage
A traditional call center often operates on time slots like 8 a.m.–6 p.m. or 8 a.m.–7 p.m., sometimes with reduced coverage on Saturdays. Yet behaviors have changed:
- A significant share of missed calls occurs early in the morning (7:30–8:00 a.m.) and at the end of the day (6–8 p.m.)
- Many patients want to change or cancel an appointment outside office hours
- Nursing home residents, caregivers, and parents of young children often call at off-peak hours
Result: between 10 and 20% of calls may be lost or postponed after several attempts, with a direct impact on satisfaction, but also on the “no-show” rate (missed appointments), which can exceed 8 to 10% in some practices.
2. What an AI Receptionist Concretely Changes
The arrival of an AI voice agent or AI Receptionist transforms the day-to-day operation of the call center. The goal is not to replace humans across the board, but to delegate repetitive, low value-added tasks to AI, while keeping sensitive cases for human teams.
True 24/7 availability
An AI Receptionist has no working hours. It can:
- Answer any call instantly, day or night
- Handle simple needs outside opening hours (cancellation/rescheduling of appointments, reminders of opening hours, address, instructions)
- Filter emergencies and redirect them according to your protocols (emergency services, medical triage, on-call duty)
In practice, this allows you to capture calls that previously went to voicemail or were simply lost—easily 10 to 30% of total volume depending on the organization.
Near-instant pick-up time
One of the main satisfaction drivers is the waiting time before first contact. In a traditional call center:
- The average time before pick-up often exceeds 30 to 60 seconds during peaks
- The abandonment rate can reach 15 to 25% at peak hours
With an AI voice agent, call handling is almost immediate (under 2 seconds), even in times of high volume. The caller is sure:
- To be heard immediately
- That their request will at least be qualified, even if it must then be passed on to a human
A consistent message aligned with your instructions
An AI Receptionist reads and applies your protocols systematically:
- Always the same safety or triage questions (chest pain, respiratory distress, etc. where relevant)
- Always the same way of verifying identity, date of birth, referring physician
- Always the same reminder of pre-exam instructions (fasting, stopping certain medications, etc.)
The voice AI does not get tired, annoyed, or “simplify” a protocol to save time. Result: more stable information quality, better compliance with your internal procedures, and fewer risks of omission.
Dynamic and adaptive scripts
Unlike a static paper script, an AI voice agent can:
- Adapt its wording according to the detected reason for the call
- Rephrase if the caller does not understand
- Switch scenario branches in real time based on the answers given
- Handle multiple languages if needed
For example, the same AI Receptionist can, for one caller, propose a time slot via your online medical scheduling system and then, if the person expresses particular concern, transfer the call to a human team while providing a structured summary of the request.
3. Full Costs: Call Center vs AI for Call Center
To decide, you need to look at the full cost over several years, not just the face value of a license or per-minute rate.
The visible costs of a traditional call center
For a floor of 10 full-time medical secretaries, you often see:
- Annual payroll (salaries + charges): €280,000 to €350,000
- Management (1 supervisor, 1 manager): €70,000 to €100,000
- Recruitment, training, onboarding: €1,500 to €3,000 per agent hired, i.e. €15,000 to €30,000 per year with 30% turnover
- Infrastructure (premises, telephony, workstations, software): €30,000 to €60,000 per year
Over 3 years, the total cost can easily exceed €1–1.3 million for a moderately sized floor, not counting the opportunity cost of lost calls and missed appointments.
The costs of an AI Receptionist / AI voice agent
An AI Receptionist is generally based on:
- Software licenses (often per channel, number of concurrent calls, or monthly volume)
- Integration fees with your tools (online medical scheduling, patient record, HIS, etc.)
- Initial support (scenario design, testing, fine-tuning)
For a volume of several thousand calls per month, an AI solution for call centers may represent:
- A recurring cost of a few hundred to a few thousand euros per month
- A one-off integration cost (a few thousand to a few tens of thousands of euros depending on complexity)
Even in a high scenario (for example, €3,000 per month + €20,000 integration), the 3‑year cost would remain far below that of a massive reinforcement of the human floor. The point is not to compare “1 AI agent = X positions cut”, but to understand how AI enables you:
- To absorb 20 to 50% growth in call volume without recruiting in the same proportion
- To smooth activity peaks without systematic overtime
- To improve the productivity of the remaining human agents
Hidden costs not to underestimate
On the traditional call center side:
- Cost of errors (misdirected calls, incorrectly scheduled appointments, duplicates)
- Impact of sudden absences (sickness, unplanned leave)
- Quality drop during understaffed periods
On the voice AI side:
- Need for serious initial setup (journey definition, testing)
- Need for continuous monitoring of indicators (resolution rate, dissatisfaction)
- Regular scenario adjustments to keep pace with changes in your organization
However, once these costs are factored in, market projections show that the return on investment of AI in contact centers is generally observed within 12 to 24 months, thanks to reduced average handling time, increased first-contact resolution rates, and fewer repeat calls for the same issue.
4. Impact on the Caller Experience
The key question for a medical decision-maker is not only economic: how is the AI voice agent perceived by patients, and which concrete indicators does it improve?
Waiting time and accessibility
Studies on contact centers show that beyond 2 minutes of waiting:
- The abandonment rate rises quickly (often > 20%)
- Frustration increases sharply, especially in a medical context
With an AI Receptionist:
- Time to first handling drops from 30–90 seconds to a few seconds
- Calls are at least triaged and routed, even if human intervention is later required
- “Simple” calls (schedule change, appointment confirmation, practical information) are handled immediately, with no queue
For patients, the difference is tangible: they feel that they are being taken into account, even during peak periods.
First-contact resolution rate
An AI voice agent well integrated with your online medical scheduling and internal applications can:
- Directly propose available slots, applying your rules (reason for visit, type of procedure, duration, referring physician)
- Update or cancel an existing appointment after identity verification
- Answer recurring questions (exam preparation, opening hours, directions to the practice)
In many deployments, you see:
- Automation of 30 to 60% of standard requests without human intervention
- A 20 to 40% reduction in repeat calls for the same reason
Human agents then handle higher value-added calls, which improves their satisfaction and level of engagement.
Frustration vs satisfaction: what actually happens
The most common fear is a “dehumanized” reception. In practice, perception depends mainly on implementation quality:
- A poorly configured voice AI that does not understand accents, cuts people off, or forces rigid menus generates frustration
- Conversely, an AI voice agent that understands natural language, rephrases, and quickly offers a solution or transfer to a human is often perceived more positively than a long wait in an overloaded queue
Experience shows that with careful implementation:
- Overall satisfaction can increase by 10 to 20%
- Complaints related to waiting times drop significantly
- Switchboard teams feel less pressure, which is reflected in the quality of their interactions
In a medical context, the key is to clearly define what AI is allowed to handle on its own (logistics, administrative matters) and what must be transferred to a human (sensitive clinical cases, distress, complex situations).
5. Building a Winning Hybrid Model for 2026
The question is no longer “AI or human”, but “how to articulate AI and human in a coherent way”. The most efficient models in 2026 are hybrid.
Clear role allocation
An effective approach is to:
- Entrust the AI voice agent with:
- First-level reception, 24/7
- Qualification of the call reason
- Automatic handling of simple requests (appointments, standard information)
- Reserve for human teams:
- Complex or emotionally sensitive cases
- Decisions requiring clinical or organizational judgment
- Personalized relationships for fragile or high-stakes patients
For example, AI can absorb 50 to 70% of total call volume as first line, while transferring 30 to 50% of cases to a human agent, with a structured summary that saves time.
Performance indicators to track
To manage this hybrid model, it is essential to define KPIs shared by AI and human teams, such as:
- Average time before handling (target: a few seconds with AI)
- First-contact resolution rate (overall, AI only, human only)
- Call abandonment rate
- Rate of calls transferred from AI to a human
- Caller satisfaction (post-call surveys, NPS, verbatim comments)
- No-show rate and quality of schedule utilization
AI brings an additional advantage: the ability to finely analyze reasons for calls, activity peaks, and average duration by type of request. Consolidated, this data lets you adjust your operations (human reinforcement on certain slots, adaptation of consultation hours, better patient information).
Governance and change management
The introduction of an AI Receptionist should not be perceived as a threat by teams, but as a tool for offloading and professionalization. A few best practices:
- Involve secretaries and call center managers from the scenario design phase for the AI
- Clearly explain that the goal is to reduce the load of repetitive tasks, not to degrade reception
- Train teams to work “with” AI: how to take over a transferred call, how to use information qualified by the AI voice agent, how to report cases where AI failed
- Plan a pilot phase of a few weeks, with gradual adjustments, rather than a sudden switchover
By 2026, the organizations that succeed in this transition are those that consider AI as a full member of the reception team, with a defined scope, measured objectives, and continuous supervision.
Conclusion
By 2026, the traditional call center model is clearly reaching its limits: costs that are hard to compress, high turnover, uneven quality, and opening hours out of sync with real patient behavior. At the same time, AI for call centers has become an industrial reality, with massive adoption and a fast-growing market driven by the automation of repetitive tasks and improved caller experience.
An AI Receptionist or AI voice agent fundamentally changes the equation: 24/7 availability, near-instant pick-up time, consistent messaging aligned with your protocols, dynamic scripts able to adapt to each situation. Economically, AI does not necessarily replace human teams, but allows you to absorb growth and activity peaks without exploding payroll, while improving overall satisfaction.
The most promising path for practices and medical organizations in 2026 is a well-designed hybrid model: AI handles simple, repetitive requests, qualifies calls, and secures permanent reception, while human teams focus on complex situations, trust-based relationships, and fine-grained priority management. This combination, managed with clear indicators and governance that involves the teams, makes it possible to build phone reception that is more efficient, more stable, and better aligned with patient expectations.