Consider a SaaS company that sells project management software.

A customer sends:

“I can’t connect your application to our company’s SSO. We followed the documentation but we’re still getting an authentication error.”

An agentic support workflow could look like this:

Step 1: Understand the request

The AI identifies the issue as an SSO configuration problem.

Step 2: Retrieve relevant information

It searches the approved technical documentation and troubleshooting knowledge base.

Step 3: Ask targeted questions

It identifies information that is missing, such as the authentication method or specific error message.

Step 4: Provide documented troubleshooting steps

The AI provides the relevant steps from the company’s approved documentation.

Step 5: Determine whether the issue is resolved

If the customer confirms that the problem is fixed, the conversation can end.

Step 6: Escalate when necessary

If the problem doesn’t match documented troubleshooting scenarios, the agent can collect the relevant details and escalate the case.

Step 7: Create a summary

The human technical support specialist receives a concise summary of the issue and troubleshooting already performed.

This is where agentic AI becomes more interesting than a simple question-and-answer chatbot: the system is participating in the support process rather than merely generating text.

How to Start Using Agentic AI in Customer Support

Companies don’t necessarily need to automate their entire support operation on day one.

A practical starting point is to identify repetitive, well-documented processes.

Step 1: Analyze support conversations

Look at your recent tickets and identify the questions that appear repeatedly.

Step 2: Identify trusted knowledge sources

Collect:

• Product documentation
• FAQs
• Knowledge-base articles
• Troubleshooting guides
• Policies
• Internal support documentation

Step 3: Choose one use case

Start with something relatively contained, such as answering product documentation questions.

Step 4: Define escalation rules

Determine when the AI should stop and transfer the conversation to a human.

Step 5: Keep humans in the loop

For important customer interactions, allow support executives to review AI-generated responses before they are sent.

Step 6: Measure the results

Track metrics such as:

• Response time
• Resolution time
• Escalation rate
• Repetitive tickets handled
• Customer satisfaction
• Human review requirements

These measurements can help determine where AI is genuinely improving the support process.

What Customer Support Teams Should Not Automate Blindly

Agentic AI can be powerful, but not every support task should be fully automated.

Human review may be particularly important for:

• Security incidents
• Account ownership disputes
• Refunds and financial decisions
• Contractual issues
• Sensitive customer information
• Complex technical problems
• Regulatory or compliance matters
• High-value enterprise customers
• Situations where the AI cannot establish a reliable answer

A good implementation therefore focuses on controlled automation, rather than maximum automation.

The question should not be:

“How much of customer support can we replace with AI?”

A more practical question is:

“Which parts of our customer support process can AI handle reliably, and where should humans remain involved?”

Where Gemini in Google Workspace Can Fit

Many customer support teams already rely on email and collaboration tools as part of their daily workflow. For organizations using Google Workspace, Gemini in Google Workspace can provide AI assistance within familiar productivity applications.

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For example, support professionals working in Gmail can use Gemini-related capabilities to help with tasks such as summarizing email threads, drafting responses, and working with information in their Workspace environment, depending on the organization’s plan and enabled features.

This is particularly useful for teams where a large proportion of customer support happens through email.

Imagine a support executive receiving a long customer email.

Instead of manually reading the entire thread and starting a response from scratch, AI assistance can help summarize the conversation and create a draft that the executive can review and modify. The human remains responsible for checking the response, especially when the email involves technical instructions, billing, security, contractual matters, or other sensitive information.

For organizations already using Gmail, Docs, Drive, and other Google Workspace applications, this can make AI adoption feel more like an extension of an existing workflow rather than introducing an entirely separate tool.

The Future of Customer Support Is Likely to Be Human + AI

Agentic AI changes the role of customer support automation. Instead of using AI only as a chatbot that answers basic questions, organizations can build AI-assisted workflows around the entire support journey—from understanding the customer’s problem and retrieving relevant information to drafting responses, collecting missing details, summarizing conversations, and escalating complex cases.

Learn more: Write for Us + AI

For a software company, one of the most practical starting points is its existing technical documentation and knowledge base. When that information is well-organized and kept current, it can provide a strong foundation for AI-assisted support.

At the same time, tools such as Gemini in Google Workspace can help teams bring AI assistance into everyday communication and productivity workflows, particularly for organizations already using Gmail, Docs, Drive, and other Workspace applications.

The most effective approach is unlikely to be AI instead of customer support professionals. It is more likely to be AI handling repetitive information work while people handle judgment, relationships, exceptions, and complex problems.

That distinction is important.

The objective of agentic AI should not simply be to automate customer support. It should be to build a support process where customers can get faster answers to routine questions while human support professionals have more time to solve the problems that genuinely require human expertise.