This is one of the easiest places to find an automation opportunity.

If your team takes information from one system and manually enters it into another, there may be no reason for a person to be doing the work.

For example, a customer fills out a form on your website. An employee then copies their details into the CRM, sends a notification to the sales team, and creates a follow-up task.

If those systems can communicate with each other, the process could potentially happen automatically.

The same applies to information moving between CRMs, accounting systems, websites, spreadsheets, support platforms and internal applications.

If your employees are acting as the connection between two systems, look at whether those systems can be integrated.

Think about the reports your business produces regularly.

Does someone collect information from several sources, put it into a spreadsheet, clean the data, create charts, and then prepare a presentation?

If the same process happens every week or every month, it is worth looking at automation.

Data can potentially be collected automatically, processed, and presented through a dashboard or reporting system.

This doesn't mean managers have to stop reviewing reports themselves. It simply means they don't need to spend hours preparing the same report from scratch.

Customer enquiries don't always require a person to read every message and decide what happens next.

AI can analyse incoming enquiries and identify what the customer is asking about, classify the request, and determine its priority.

The system can then route the enquiry to the appropriate team or trigger the relevant workflow.

For example, a technical question could go to the support team, while a pricing enquiry could be sent to sales.

A human can still handle the actual conversation when necessary. The automation simply removes some of the sorting and administrative work.

This is particularly useful when a business receives a large volume of enquiries.

Invoices, applications, contracts, forms, and other documents often contain information that employees have to read and enter into another system.
This is a good example of where AI can go beyond traditional rule-based automation.
An AI-powered system can potentially read a document, identify relevant information, extract the required fields, and send the information into another business system.

Instead of having an employee manually enter every piece of information, they can focus on reviewing exceptions or handling cases that require human judgement.

Businesses often need information from websites, documents, databases, research sources, and other systems.

If employees are spending hours searching for information, copying it and organising it into spreadsheets, there may be an opportunity to automate the collection and processing stage.

This becomes particularly valuable when the volume of information is large.
Automation can collect and structure information at a scale that would be difficult to manage manually.

AI can then be used to analyse that information, identify patterns, categorise results, or produce summaries.

This is where automation starts becoming more than simply saving someone a few minutes.

It can change how a business handles large amounts of information.

Look at your internal processes.

After a customer submits a form, does someone have to remember to follow up?

After a proposal is sent, does someone have to create a reminder?

When an employee joins, does HR have to manually send the same set of documents and notifications?

These are examples of processes where the next step is predictable.

When the rules are clear, software can often trigger the next action automatically.

The objective isn't to remove people from the process. It is to make sure predictable steps don't depend on someone's memory.

Data cleaning is one of those tasks that often goes unnoticed.

Someone may regularly remove duplicates, correct formatting, standardise information, check missing fields, or combine data from different sources.

If this happens repeatedly, it may be possible to automate at least part of the process.

For businesses dealing with large datasets, automated data processing can make a significant difference.

Instead of spending time preparing information before it can be used, teams can focus more of their time on interpreting the results and making decisions.

Imagine your sales team has one version of customer information, marketing has another, finance maintains another record, and customer support keeps its own data.
Now someone has to make sure all of these records remain consistent.

This is usually a sign that your systems need to communicate better.

An integration can allow information to move between systems rather than requiring employees to repeatedly update each one.

In some cases, the solution may be a CRM integration. In others, it could require APIs, a central database, or a custom business application.

Spreadsheets are useful, but they can become difficult to manage when they start controlling an entire business workflow.

If a spreadsheet determines which leads need follow-up, which orders need processing, which customers need attention, or which tasks employees need to complete, it may be time to look at the underlying process.

The question isn't whether the spreadsheet is good or bad.

The question is whether the process has become too complex for a spreadsheet.
Sometimes the answer is automation. Sometimes it is a CRM or another existing platform. And sometimes the business needs a custom application designed around its specific workflow.

This is perhaps the most important question on the entire checklist.
Not every task should be automated.

Some work requires creativity, strategic thinking, relationships, judgement, or direct interaction with customers.

The opportunity is to identify the work around those activities that doesn't require the same level of human involvement.

If an employee spends three hours preparing information before a one-hour strategic meeting, perhaps the preparation process can be automated.

If a salesperson spends half their day entering data and managing administrative tasks, perhaps those tasks can be reduced.

Good automation doesn't replace the important work. It gives people more time to do it.