Key takeaways
- Scripted chatbots follow fixed flows; AI agents understand open questions and can take actions in your systems.
- The best service AI resolves routine requests end to end and hands complex ones to people with full context.
- Knowledge quality and system integrations matter more than the AI model.
- Measure resolution, not deflection: a customer pushed away unhappy is not a success.
Customer service is where most businesses first try AI, and results vary widely. Some deployments resolve a large share of requests and earn good satisfaction scores; others frustrate customers and get switched off. The difference is mostly in approach.
Scripted chatbots
Traditional chatbots follow decision trees: buttons and keywords lead the customer through predefined paths. They are predictable and cheap, and they work for simple, structured tasks. They struggle with anything outside the script, which customers notice quickly.
AI agents
AI agents use language models to understand open-ended questions, find answers in knowledge bases and take actions through integrations, such as checking an order, changing a booking or updating an address. They handle variety far better, but need guardrails, good data and careful testing.
Comparison
| Scripted chatbot | AI agent | |
|---|---|---|
| Understanding | Keywords and buttons | Natural language, including follow-up questions |
| Actions | Limited, predefined | Calls tools and APIs within permissions |
| Setup | Design every path | Define knowledge, actions and guardrails |
| Risk | Low, but rigid | Higher without guardrails; more capable with them |
| Best for | Simple menus and forms | Varied questions and routine transactions |
Where AI works well in service
- Order and delivery status, returns and exchanges.
- Appointment booking, rescheduling and cancellations.
- Account updates such as address or contact details.
- Answering policy and product questions from approved knowledge.
- Drafting replies and summaries for human agents.
Where people should stay in charge
- Complaints, emotional situations and vulnerable customers.
- Refunds, credits and exceptions beyond clear rules.
- Complex technical troubleshooting.
- Anything with legal, medical or financial consequences.
Designing the handoff
A good handoff passes the full conversation, the customer’s identity and what the AI already tried to a human agent, so customers never repeat themselves. Make it easy to reach a person, and route by topic and urgency.
Metrics that matter
| Metric | Why |
|---|---|
| Resolution rate | Requests fully resolved without a human |
| Customer satisfaction after AI interactions | Whether customers are happy, not just deflected |
| Handoff rate and reasons | Where the AI falls short, and what to fix |
| Repeat contacts | Whether “resolved” issues come back |
| Agent time saved | Value delivered to the team |
How to start
- Analyze recent contacts to find the most common, rule-based requests.
- Fix and complete the knowledge articles behind those requests.
- Connect the systems needed to resolve them, such as orders or bookings.
- Launch on one channel with clear handoff, review conversations weekly and expand gradually.
Our AI solutions team builds service agents on platforms such as Agentforce and on custom stacks, always starting with the knowledge and integrations.
A worked example: order status
A retailer receives thousands of “Where is my order?” messages a month. An AI agent on web chat identifies the customer, looks up their recent orders, checks the carrier tracking and explains where the package is. If the order is late beyond a threshold, the agent offers the options policy allows, such as a replacement request, and creates a case for the team with everything gathered so far. Customers get answers instantly, and agents only see the cases that need judgment.
Readiness checklist
- Top contact reasons identified from recent data.
- Knowledge articles for those reasons accurate and current.
- Systems needed to resolve requests connected through secure integrations.
- Clear rules for what the AI may and may not do.
- Handoff to people designed and tested.
- Weekly review process for conversations in the first months.
Frequently asked questions
Will AI replace our support team?
It typically takes over routine requests and assists agents with the rest, letting the team focus on complex and high-value conversations.
Should customers be told they are talking to AI?
Being transparent builds trust, and in some places disclosure may be required. Make it clear and offer an easy route to a person.
Which channel should we start with?
Start where volume is high and requests are routine, often web chat or messaging, then extend to email and voice.
