Automation in customer service isn’t new. Basic IVRs and AI chatbots have handled simple inquiries for years, but the technology has been limited, making the choice between humans vs AI for customer service relatively easy.
But with the growing use of large language models, (LLMs) that line is blurring. AI can now understand, respond to, and even predict customer needs – requiring call center managers to rethink how calls should be handled. The choice between AI and human agents is no longer simple, and it directly impacts efficiency, satisfaction, costs, and compliance.
Factors in the Choice of Human or AI Customer Service
Reliability & Uptime – AI can offer 24/7 availability, but systems must be stable and dependable.
User Experience – Automation should improve, not frustrate, the customer experience.
Microsoft Teams Integration – Many of the best contact centers integrate with Teams, requiring that AI and human workflows fit well into that environment.
Security & Compliance – Data privacy and regulatory requirements (ISO 27001, PCI, FERPA) vary, but often must be upheld.
Cost Control – AI reduces labor costs, but implementation and oversight still require investment.
Scalability – AI can handle high volumes instantly, while human support teams must scale carefully.
Benefits of AI in Customer Service
Using AI for customer support – specifically in contact centers – presents benefits and drawbacks. Here, let’s look at both as well as the cases in which AI can be the best tool for customer service.
1. Faster call handling – If the request is straightforward, AI can process it very quickly, eliminating the need for customers to wait on hold.
2. 24/7 availability – Unlike human agents, AI never clocks out. A university’s financial aid office, for example, can use AI to answer FAQs about deadlines and eligibility—without needing to hire late-night staff.
3. Cost savings – AI reduces labor costs by automating common interactions with AI chat and voice assistants. This is especially valuable for organizations handling thousands of daily inquiries.
4. Scalability – AI can handle unlimited simultaneous interactions, making it ideal for seasonal spikes or crisis situations. For example, during university enrollment periods, AI can manage the predictable increase in routine admissions and housing questions, freeing up human agents for more complex cases.
5. Data-driven improvements – AI learns from every interaction, continuously refining its responses. A retail contact center could use AI insights to optimize return policies based on frequent customer feedback.
Disadvantages of AI in Customer Service
1. Potential caller frustration – If AI misinterprets caller intent or forces users through rigid menus, this can be frustrating. Consumers generally prefer human agents over chatbots for complex inquiries, particularly those requiring negotiation or exception handling.
2. Less personalization – AI lacks empathy. While it can recognize keywords, it may struggle with nuanced concerns, such as a student worried about losing financial aid due to unexpected life circumstances.
3. Compliance risks – AI must be carefully managed to comply with regulations like ISO 27001, PCI, and FERPA. Mishandling sensitive data—such as credit card details or student records—can bring security breaches and legal penalties.
4. Technical limitations – AI systems depend on structured data and predefined rules. If a customer asks a question outside of its training data, the AI may produce incorrect or vague responses. For instance, an AI-powered help desk might struggle with an unusual software bug that requires a detailed, human-led troubleshooting process.
Use of AI in Customer Service: When it Works Best
Simple, repetitive calls – AI is ideal for handling common, predictable questions such as “What’s my account balance?”, “What are your business hours?”, or “How do I reset my password?”
Need for 24/7 handling – This is especially valuable for industries like education, finance, and healthcare, where important information is often needed at all hours of the day.
Existing successful IVR system – Organizations with effective Interactive Voice Response (IVR) systems can add additional capabilities with AI. Instead of pressing buttons, customers can simply say, “I need to check my payment status.”
Humans for Customer Service
Humans are still irreplaceable in contact centers. Let’s look again at benefits and disadvantages of live agents, and the times when you will need them.
Benefits of Human Agents
Personalized experience – Humans can adapt responses based on tone, context, and emotions. A financial aid officer, for example, can reassure a stressed student struggling with tuition payments.
Higher first-call resolution – Unlike AI, which may struggle with ambiguous or multi-step problems, humans can handle a broader range of inquiries in a single interaction.
Better VIP/escalated call handling – High-value customers expect concierge-level experience. Whether it’s an enterprise client needing immediate IT support or a donor calling a university’s development office, human agents can provide this top-tier service.
Reliability for complex cases – Humans excel in unpredictable scenarios where AI might fail. If a caller has a billing issue that requires exception handling, a live agent can make judgment calls, offer goodwill credits, or escalate problems appropriately.
Downsides of Human Agents
Higher labor costs – Salaries, benefits, and training for customer service staff are significant expenses. Unlike AI, which can scale instantly, human teams require more investment to grow.
Slower handling – Even the best-trained agents take longer than AI to process simple, repetitive requests. While AI can instantly pull up account details, a human agent must manually search, verify, and respond.
Limited operating hours – Human teams require shifts, breaks, and time off, making 24/7 coverage expensive. Businesses relying solely on human agents may struggle to provide round-the-clock support.
Potential human error – While AI operates with programmed consistency, human agents are prone to mistakes – incorrectly hearing details, providing inaccurate information, or misunderstanding policies.
Best Use Cases for Human Agents
Customer service priority – If customer satisfaction is the main goal, investing in human agents helps provide a higher-quality experience. Industries like luxury retail, private banking, and higher education rely on personal service to maintain strong relationships.
High-value/sensitive interactions – Conversations involving legal, financial, or medical topics often require human judgment and discretion. AI should never be the sole handler of complex or emotionally charged situations.
Limited IT resources for AI management – AI systems may require monitoring and adjusting to stay effective. Small IT teams or those with limited budgets may find that maintaining a human-centric contact center is simpler and more effective.
Human agents are still irreplaceable in customer service, especially for complex, high-touch interactions. The key is knowing when to rely on people—and when AI can lighten the load.
Hybrid Scenarios: AI + Human Collaboration
For many organizations, the best strategy in terms of human or AI customer service isn’t choosing between them – it’s combining them. A hybrid approach allows AI to assist in various ways. Here we look at several options.
AI-Assisted Human Operators
Live Speech-to-Text Transcription – AI can transcribe conversations as they unfold, giving agents a searchable text record of ongoing calls. This helps with note taking and reference for the agent during long calls.
AI Call Summaries – Instead of requiring agents to type out post-call notes, AI can automatically generate summaries, reducing wrap-up time.
Callback Reminders with Context – AI can streamline follow-ups by generating detailed callback notes, giving agents full context when returning a customer’s call.
Database Lookups – AI can detect keywords in conversations and automatically fetch relevant details, such as product specs, pricing, or inventory levels.
Enhanced Agent Tools
Live Coaching – AI can provide real-time prompts during calls, reminding agents to cover important topics like warranties, compliance statements, or upselling opportunities.
Knowledge Base Integration – AI can connect directly to internal resources, suggesting solutions based on customer inquiries. Instead of searching through documentation, agents receive instant recommendations.
Sentiment Analysis – AI can monitor customer tone and emotions, alerting supervisors when a conversation requires intervention.
Action Item Extraction – AI can identify commitments made during a call, such as follow-up tasks, service adjustments, or scheduling needs, which reduces the risk of oversight.
Smart Call Handling
Teams Presence Awareness – AI can route calls based on an employee’s availability in Microsoft Teams.
AI-Based Initial Triage – AI can handle basic verification, call routing, and FAQ responses before escalating complex issues to a live agent.
Customer Self-Service with Human Backup
AI for Routine Inquiries – AI handles straightforward customer requests, such as order status, billing questions, and appointment scheduling.
Intelligent IVR – Instead of frustrating, button-based menus, AI-powered IVRs use natural language processing to understand customer intent, allowing for more fluid and accurate call routing.
Context Preservation – When an interaction started by AI escalates to a human agent, the full conversation history transfers with it.
Opt-In AI Model – Some customers prefer self-service, while others want human support. Offering a choice between AI assistance and live agents improves satisfaction and allows customers to feel in control of their experience.
Making the Choice: AI, Humans, or Both?
Deciding when to use humans and when to rely on AI for customer service may be easier if we use a framework to categorize callers’ inquiries. Here we present a three-dimensional framework as one way of thinking about it.
A Framework for Customer Interactions
Customer inquiries can be analyzed across three axes:
- X-axis: Simple ↔ Complex – How many steps or details are required to resolve the inquiry?
- Y-axis: Specific ↔ General – Does the question apply to one person/situation or many?
- Z-axis: Subjective ↔ Objective – Is the answer purely factual, or does it require opinion, interpretation, or guidance?
This yields eight possible combinations, and by plotting inquiries here, we can see which cases for which AI, humans, or hybrid is likely to be the best approach. You’ll find here the eight possible combinations, AI vs human capabilities, and examples from a university contact center.
1. Simple + Specific + Objective
AI capability: Handles this fully
Examples:
- “What is my student ID number?”
- “How much is my current tuition balance?”
- “What time does my class start tomorrow?”
Given access to the necessary information, AI can process these requests instantly, with no need for human involvement.
2. Simple + Specific + Subjective
AI capability: Assists, but a human may be needed
Examples:
- “Should I take this professor’s class?”
- “Is my GPA good enough for an internship?”
- “Is this elective easier than that one?”
AI can provide general insights based on past student reviews or academic policies but cannot make a final recommendation based on things such as personal preferences or career goals.
3. Simple + General + Objective
AI capability: Handles this fully
Examples:
- “Where is the financial aid office located?”
- “When does the semester start?”
- “What are the library’s operating hours?”
These standardized questions have fixed answers that AI can instantly provide if it has access to the right information.
4. Simple + General + Subjective
AI capability: Assists, but human input is helpful
Examples:
- “Is the food in the cafeteria good?”
- “Which dorm is the best to live in?”
- “Is the workload manageable for first-year students?”
AI can summarize online reviews or historical data, but customer experiences vary, making human advice more valuable.
5. Complex + Specific + Objective
AI capability: Assists, but requires human verification
Examples:
- “Can I drop this class without affecting my financial aid?”
- “How do I appeal a grade in this specific course?”
- “Am I eligible for early graduation based on my credits?”
AI can guide customers through policy explanations, but human oversight is needed to confirm details and make decisions.
6. Complex + Specific + Subjective
AI capability: Not suitable – human required
Examples:
- “Should I switch my major based on my grades and interests?”
- “What’s the best strategy for me to improve my GPA?”
- “Do you think I should take a gap year before applying to grad school?”
These inquiries require personalized guidance, career advice, and emotional intelligence—areas where AI falls short.
7. Complex + General + Objective
AI capability: Assists, but may need human clarification
Examples:
- “How does the university’s financial aid process work?”
- “What are the requirements for graduation in my program?”
- “How does the credit transfer process work for international students?”
AI can provide broad, structured answers, but edge cases and exceptions may require human intervention.
8. Complex + General + Subjective
AI capability: Not suitable – human required
Examples:
- “What’s the university’s culture like for international students?”
- “Is this university better for research or hands-on experience?”
- “How does this school compare to others in my field?”
AI can compile general trends, but subjective questions need human insight, personal experience, or expert opinion.
| Inquiry Type | AI Capability | Needs Human Input? |
|---|---|---|
| Simple + Specific + Objective | 10/10 | No |
| Simple + Specific + Subjective | 6/10 | Yes |
| Simple + General + Objective | 10/10 | No |
| Simple + General + Subjective | 4/10 | Yes |
| Complex + Specific + Objective | 6/10 | Yes |
| Complex + Specific + Subjective | 2/10 | Yes |
| Complex + General + Objective | 5/10 | Yes |
| Complex + General + Subjective | 1/10 | Yes |
Landis Contact Center Solutions
Landis delivers a Microsoft Teams Certified solution that supports AI, human agents, or a blend of both – so contact center managers have all the necessary options at their fingertips. Built to integrate natively with Microsoft Teams, Landis Contact Center and Attendant Console offer powerful features for any contact strategy.
For AI Handling
- IVR & Call Queues – Landis uses Microsoft Teams Calling APIs to provide deeply integrated IVR and call queue features. These support intelligent call routing, automated handling, and advanced voice input with natural language recognition.
- Sentiment Analysis – Real-time sentiment tracking helps identify when a call may need escalation. Supervisors can monitor tone and intervene proactively when AI detects frustration or urgency.
- Data-Driven Insights – The system continuously captures analytics across queues, agents, and IVRs. Metrics like average wait time, abandon rate, and service level help optimize AI workflows and inform operational decisions.
For Human Handling
- Landis Attendant Console – Built for receptionists and high-volume call handlers, the Attendant Console allows fast, efficient transfers, quick access to contacts, and visibility into Teams presence—all within the familiar Teams interface.
- Real-Time Presence & Directory Search – Agents can search, sort, and transfer based on live Teams presence, custom status messages, and shared notes. This ensures calls are routed to the right person without delays.
- Failover Options – In the rare event of an outage, calls can be set up to automatically route to native Teams call queues or auto attendants. Failover scripts allow IT teams to shift resources instantly, with the option to fail back to Landis once service is restored.
For a Hybrid Approach
- AI with Human Backup – AI handles routine inquiries and routes complex cases to human agents when needed. Escalation includes full conversation history, so customers never have to repeat themselves.
- Teams Presence-Based Handling – Landis uses real-time Teams presence to route calls intelligently. If a user is busy or offline, calls can be held, transferred, or escalated according to pre-set logic.
- Customizable AI Prompts – With real-time transcription enabled, Landis AI can generate call summaries, action items, and coaching tips. These prompts can be customized to support chat consults, callback reminders, or documentation workflows—helping agents work smarter, not harder.
Want to see more about how Landis can help with human, AI, or hybrid customer service? Talk to a contact center expert to ask questions or book a free demo.
