A quick answer is not much use if your customer has to contact you again.
This is the basic test for AI in customer support which on the surface provides a significant opportunity to respond faster, improve response accuracy and free up time for more complex customer issues.
But while faster responses and fewer conversations can look like progress, it doesn’t tell you whether customers have solved their actual problems. The more useful questions are practical: is the AI working from a reliable company knowledge base? Is sensitive company data protected? Can a colleague pick up the conversation without starting again? And is its use expanded only once it has proved it can deliver high-quality support?
Here are six things worth getting right now before you scale.
1. Fix the knowledge behind the answers
For the questions your support team is already answering on a daily basis, the answers should already be in your help centres, public product documentation and internal knowledge bases. They are the foundation of any AI support agent.
Then fix the knowledge base behind the answers. If an answer depends on an unwritten workaround or knowing which colleague to ask, there is an immediate gap to close. And when AI gets an answer wrong, review the source material as well as the response. Better instructions will only get you so far if the information underneath them is incomplete.
2. Be clear about what AI can access and answer
An AI support agent needs access to the information required to help the customer. That doesn’t mean it needs access to everything your team can see. Decide which sources it can use, what customer information it needs and which questions should go straight to a person.
Then make sure those limits are enforced through the connected systems. Instructions telling an agent not to share confidential information are only part of the job. You also need to check what it can retrieve and what happens to sensitive information in logs, testing and troubleshooting.
3. Test the questions that are difficult to answer
AI should be evaluated against more than just the straightforward questions. It should include things like routine requests, ambiguous wording, incomplete documentation and situations where the agent needs to refuse or escalate. Ultimately, a strong response accuracy score should never compensate for a serious security or confidentiality failure.
Consider the following approaches:
- Testing historical cases: Compare AI’s responses with answers given in real past cases by a human
- Human review and retrieval checks: Assess accuracy, clarity, source relevance and whether the answer is properly supported by internal documentation
- Adversarial testing: Try to make an agent reveal confidential information, ignore restrictions or answer questions outside the scope of what it’s supposed to do
- Shadow testing and controlled pilots: Compare the AI with the current process before introducing it to a limited number of customers
Include situations where the right response is to ask another question or hand over to a colleague. Agree what would stop you launching, and repeat the relevant checks when your product, documentation or connected tools change.
4. Help customers reach the right answer sooner
AI naturally reduces the time customers and support agents spend searching for information. It can help customers find guidance and work through routine questions before a support engineer even gets involved.
The goal, however, should always be resolution. A quick answer is not useful if it creates confusion or leads to another support contact. So, when a conversation needs a person, make the handover useful. Include what the customer is trying to do, the troubleshooting already completed and anything still unresolved. A customer who has spent ten minutes explaining a problem shouldn’t have to start again.
5. Look beyond response times
Response time and automation rates are important metrics, but they don’t tell us whether customers are receiving quality customer service. You also need to know whether customers are getting the help they need. Look at repeat contacts, reopened cases and customer feedback alongside those numbers.
Read the conversations behind them. Things like repeated questions, abandoned conversations and frequent requests should all be flagged and prevented as they may result to unclear answers, poor escalation paths or unnecessary friction in the customer journey.
6. Know when a human needs to step in
People remain essential for complex investigations, judgement and empathy. Customers who are frustrated, worried or unsure of what to do need someone who understands their situation and takes responsibility for helping them.
A good handover to a human support agent should include the conversation history, relevant customer details and the troubleshooting already completed. Effectively automating support routine work allows support teams to have more time to provide thoughtful, personal service where it matters most.
Building towards more proactive support
Getting AI support right in 2027 starts with reliable documentation, clear safeguards and proper testing. Success comes down to whether customers get their problems resolved and trust the help they receive, not simply by how many conversations were successfully handled.
The next opportunity is to use what those conversations reveal to prevent problems before they surface. That could mean improving the onboarding process, identifying recurring product issues or filling gaps in documentation.
Every support conversation is a chance to make the next one unnecessary.
