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AI recently gave me an incomplete answer to a tax question.
And I still use AI almost every day.
A client came to me with a question that looked simple on the surface.
But underneath it were several different tax issues.
I used an AI research tool to help me work through it.
The initial answer looked good.
It was also only half the answer.
Because I understood the tax concepts involved, I knew there were more questions we needed to explore.
So I kept researching, verified the underlying guidance, and ultimately reached a different conclusion for the client’s specific situation.
That experience didn’t make me trust AI less.
It reminded me how we should be using it.
AI can help us research faster.
It can help us identify issues we might otherwise overlook.
It can help us organize information and get to a useful starting point much quicker.
But it doesn’t replace professional judgment.
And that’s why I don’t think AI makes accountants less valuable.
I think it raises the bar.
Getting an answer is becoming easier.
Knowing whether that answer is correct, what might be missing, and how it applies to the person sitting across from you?
That’s where our value is.
AI can help us get to the answer.
But we still need to know when the answer isn’t enough.
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AI-Powered Client Enquiry Automation
Project Overview:
I built an AI-powered client enquiry automation workflow using Zapier, Google Forms, Google Sheets, AI by Zapier, Gmail, and Google Calendar.
The goal was to automate the initial handling of client enquiries from receiving the enquiry to understanding the client's needs, sending an acknowledgement, and creating a follow-up task.
The Problem:
Managing client enquiries manually can be repetitive and time-consuming. Each new enquiry needs to be reviewed, categorized, summarized, responded to, and followed up.
Without a structured process, it's easy for enquiries to be delayed or follow-ups to be missed.
The Solution:
I designed a workflow that automates these repetitive administrative tasks while using AI to analyze the information provided by the client.
The automation follows this process:
Client submits enquiry
↓
Google Sheets captures the enquiry
↓
AI analyzes the enquiry
↓
Gmail sends an acknowledgement
↓
Google Calendar creates a follow-up
How It Works:
1. Enquiry Collection.
The client submits their details and enquiry through a Google Form. The information is automatically recorded in Google Sheets.
2. AI Analysis.
AI by Zapier reviews the enquiry and extracts key information such as the service category, priority, concise summary of the client's needs, and recommended next action.
This eliminates the need to manually review every enquiry just to determine what it is about and how it should be handled.
3. Automated Client Response.
After the enquiry is processed, Gmail automatically sends the client an acknowledgement, ensuring they receive a timely response.
4. Follow-Up Management.
The workflow creates a Google Calendar follow-up, helping ensure that the enquiry doesn't get lost after the initial response.
Result:
The completed workflow turns a manual enquiry process into a structured automated system:
Capture → Analyze → Respond → Follow Up
The key improvement is the use of AI as a decision-making layer, rather than simply moving information from one application to another.
This demonstrates how AI automation can reduce repetitive administrative work, improve response consistency, and help businesses stay organized with client enquiries.
Tools Used:
Zapier | AI by Zapier | Google Forms | Google Sheets | Gmail | Google Calendar
Skills Demonstrated:
AI Automation • Workflow Design • Administrative Automation • AI Prompting • Client Enquiry Management • Email Automation • Calendar Automation • Process Optimization
Really clean workflow, Ann. The Capture → Analyze → Respond → Follow Up structure makes the business value immediately clear. One useful next layer could be an exception route for urgent or low-confidence enquiries, so AI handles the routine cases while anything uncertain...
AI is becoming useful for a simple reason: it’s starting to finish the work, not just suggest what to do.
For a small business, the difference is clear.
“How should I follow up with this lead?” is advice.
Researching the lead, preparing a personalized message and updating the client list is execution.
The opportunity isn’t replacing your team. It’s removing the repetitive steps that keep people away from customers and creative work.
What’s one task you would happily stop doing every week?
Are developers becoming too dependent on AI?
Few days ago, Claude was unavailable for around 3 hours.
For some devs, IT work almost stopped.
That made me think:
What would happen if AI tools stopped working for a full day?
I’m seeing more developers, especially those who started coding with AI, using AI for almost everything.
The problem is not using AI.
The problem is not knowing what to do without it.
Developers who started before AI became mainstream had to learn the fundamentals, read documentation, debug problems, search for solutions, and figure things out themselves.
That experience builds real problem-solving skills.
AI is a powerful tool, and developers should absolutely use it.
But there is a big difference between:
👉 Using AI to become faster
and
👉 Depending on AI to do your job
A good developer should be able to work without AI when necessary and use AI to improve productivity when it’s available.
AI should be your copilot, not your brain.
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