In our daily work with companies eager to adapt AI for their business processes, we see that it’s often difficult for them to imagine how automating a workflow with AI can look in practice. The concepts can seem abstract and far removed from the day-to-day challenges of running a department or a company.
So in this article, we’ll skip the theory and show you a practical, tangible AI automation example. Our goal is to demystify the process and illustrate how an AI-powered workflow can look like to solve a very common, very real business problem.
Keep in mind that this is just an AI automation example. In a real-world engagement, you have many more options to trim a process like this to your individual situation, integrating it deeply with your existing software and business rules. If you’d like to know how that works, feel free to reach out to us anytime. We’re happy to show you.
A Step-by-Step Walkthrough of the Workflow
Now, let’s get practical with our AI automation example: creating an intelligent receptionist to manage and triage all incoming messages from a website contact form.
The thought behind this being, that company’s often receive lots of different messages via their contact forms. They answer them manually and afterwards they’re forgotten. But there’s actually real value in them, as you can learn what’s important to your customers, what they might complain about or which requests they send. Difficult to do by hand, but with AI we are capable of analysing large amounts of unstructured text and find patterns in the chaos.
Do do this, we break down the process into a few easy steps.
Step 1: Identifying the Trigger
Our process begins the moment a potential customer, existing client, or job applicant clicks “submit” on your website’s contact form. We could catch this event directly from your contact form, or, much easier, watch the corresponding email that arrives in your support mailbox.
For example, if the contact form messages always arrive at support@yourcompany.com and have the subject line “New Contact Form Message”, then our trigger will watch for exactly this.
As soon as a new email with this subject line arrives, our AI automation example is triggered.
It would extract all relevant infos from this email – for instance, the senders email, the date and time, and of course the actual message. This information, we’ll pass on to the next step.
Step 2: Designing the AI Brain
This is the most transformative part of the process and the core of this AI automation tutorial. We take the raw text from the email body and send it to a Large Language Model (LLM) to perform the analysis.
Do set this up correctly, we need to decide what we actually want to know. This can be whatever is important to your situation; for this AI automation example, let’s go with the following goals:
- Define the category (sales lead, support request, billing question, job application, others, spam)
- Write a one-sentence summary
- Identify urgency (high, middle, low)
- Analyse sentiment (positive, neutral, negative)
You see what we are trying to do there? We take unstructered text and bring it into a structured format, which we can then use for further analysis.
In order to communicate this to the AI, we create a respective prompt. This is the instruction we want to give the LLM so it knows what to do. For our AI automation example, this could look something like this:
In this prompt, we give the AI a role and very clear instructions what we expect it to do. Also notice the “return ONLY a valid JSON object” part – by defining clearing what result we expect, we get a standardized answer which is much easier to process in subsequent steps and makes this an effective AI automation example.
An answer from the AI would look something like this:
Once we got this json response, it’s time to move on to the next step.
Step 3: Implementing the Business Logic
With the AI’s clean data in hand, we can now apply whatever business logic rules to our workflow. In other words, we can decide what we want to do with the data.
For this AI automation example, let’s say these are our goals:
- Distribute the message to the correct department:
- Sales Leads –> add new lead in Hubspot, send Slack message to Sales channel
- Support Requests –> forward to customer support
- Billing Questions –> forward to accounting
- Job Applications –> add new applicant in HR software
- For each received message, add a new line to an Excel sheet, in which we store the data for all messages, in order to be able to analyse them in the future
Keep in mind, these are examples. You could trigger any actions that make sense to you and your business from this data.
Step 4: Integrating the Actions
The final step is to connect these logical paths to the software your teams already use. This is where the true efficiency gains are realized in our AI automation example.
Looking at the automations we’ve outlined in step 3, we need to set up different paths.
Connect with other software:
In order to send sales leads to Hubspot and Slack, and job applications to the HR software, we need a way to access these platforms. Usually, this is done via APIs, that allow for very easy transfer of data in and out of software tools. We set the connections, decide on the data to be transferred and choose to trigger these automations every time a new sales lead or job applications is processed through our workflow.
Forward emails:
Support requests and billing questions should be send to the respective departments within the company. That’s rather easy, we simply forward the original message per email to the department’s email addresses.
Add data to Excel sheet:
Over time, we want to create a database of all messages that reach the company via the contact form. To do this, we once create an Excel file (for example a Gsheet in Google Drive) and set up column headers that correspond to the data structure we created in step 2. Once a new message comes in and is automatically processed through our AI automation example, we take this data and write it in a new row in this Excel file. Very easy, all in one place, and this way we automatically build a searchable and filterable database that allows for further analysis.
The Business Impact: Why This AI Automation Example Matters
While this is a straightforward workflow, its impact on business operations is relevant. This is one of the most practical AI applications because its value is immediate and measurable. When we implement this type of intelligent automation example for our clients, they see benefits across multiple areas.
- Operational Efficiency: The most obvious benefit is the elimination of manual, repetitive work. This frees up skilled employees to focus on high-value activities like talking to customers and solving complex problems, rather than sorting emails. This is the primary goal of any AI automation example.
- Speed and Responsiveness: In today’s market, the speed of response is a significant competitive advantage. This system ensures that high-urgency sales leads are acted upon in seconds, not hours. This dramatically increases the likelihood of conversion and improves the customer experience from the very first touchpoint.
- Data-Driven Strategy: This workflow creates a powerful new source of structured data. By logging the AI’s analysis over time, you can build dashboards to track key metrics. You can answer strategic questions like: “What percentage of our inquiries are support-related?” or “Is the sentiment of our inbound communication trending positive or negative?” This turns a simple contact form into a business intelligence tool.
This is more than just a theoretical exercise; it is a clear AI automation example that delivers tangible return on investment.
Conclusion: Your First Step into AI Automation
We have walked through a complete, end-to-end AI automation example, from the initial trigger to the final, value-adding action. Our goal was to demystify the process and show you that intelligent automation is not an inaccessible, futuristic concept – it’s a practical tool that can be applied today to solve common business challenges.
As we mentioned at the start, this is a foundational example. The real power comes from customization – tailoring the AI’s instructions, defining complex routing logic, and integrating with your unique set of business tools. This is where simple workflow automation with AI evolves into a deeply embedded strategic asset.
If this AI automation tutorial has sparked some ideas, or if you’re curious about how a similar workflow could be designed to tackle your specific operational bottlenecks, we’re here to help. Reach out to us for a personalized consultation, and we’ll be happy to show you what’s possible.