Every workplace has tasks that take more effort than they should. Someone copies the same details between systems. A team member searches through old emails for an answer. A project moves forward, but the next person does not receive the information they need.
AI and automation can help with this everyday friction. A useful starting point is one familiar task: understand what makes it slow, decide what a better result looks like, and test a small improvement with the people who do the work.
AI and automation do different jobs
Automation follows a defined process. When a customer completes a form, it can create a record, assign a task and notify the right person. If the rules are clear and the information is structured, conventional automation may be all you need.
AI can help interpret less structured information: an email, a document, meeting notes or a question written in everyday language. It can suggest a category, extract relevant details or prepare a first draft. Its output still needs checking, particularly when missing context could change the answer.
The two can work together. AI might identify the main request in an email; automation can place a draft task in the appropriate queue. A person checks the details before anything is promised to the customer.
Four places to look for useful improvements
1. Repetitive administration
Consider the information that gets entered more than once: contact details, job references, booking requirements or delivery updates. Connecting the systems involved can reduce copying and give everyone a more consistent record.
Where the source is an email or attachment, AI could help prepare the fields for review. The workflow should flag missing information and possible duplicates. An incomplete request should stay visibly incomplete, rather than have the system guess an answer.
2. Finding information
Teams often have useful knowledge spread across procedures, manuals and shared folders. An AI assistant connected to approved documents can help people find relevant material and draft an answer with links to its sources.
The quality of that experience depends on the underlying documents. Outdated instructions, unclear ownership and conflicting versions need attention. Access should also follow existing permissions: an assistant should only retrieve information the person asking is allowed to see.
3. Meetings and handovers
AI can turn meeting notes into a draft summary, organise decisions and suggest action items. Automation can then move confirmed actions into a task system or remind an owner when something is due.
The review step matters. A suggestion discussed in a meeting is not necessarily an agreed decision. Someone should confirm the wording, owner and deadline before tasks are assigned. If meetings are recorded or transcribed, agree the approach with participants and follow your organisation's policies.
4. Customer enquiries
Incoming enquiries are a useful place to combine the two approaches. AI can suggest the topic and summarise the request. Automation can route it to the appropriate team, show how long it has been waiting and keep its status up to date.
A draft reply can save preparation time, while the person handling the enquiry remains responsible for checking facts, prices and commitments. Unusual or sensitive requests should reach someone who can make the decision.
What could this look like in practice?
Imagine a team receiving requests for event equipment. Each enquiry arrives in a different format, and someone has to find the event date, location, equipment list and delivery requirements before a quote can begin.
A small workflow could:
- Capture the enquiry and retain a link to the original message.
- Use AI to suggest the relevant details, leaving unknown fields blank.
- Ask a team member to check the details and request anything missing.
- Create the job record and assign the next task once approved.
Stock availability, pricing and delivery commitments would still come from the relevant systems and authorised people. This is an illustrative workflow, not a claim about a completed MyEvent Labs project.
Measure the whole task
There is evidence that AI assistance can improve particular kinds of work. The 2023 working paper Generative AI at Work reported an average productivity increase of about 14% in a study of customer support agents, measured by issues resolved per hour. The effects varied across workers. That result is useful evidence for one setting, not a forecast for every business.
For your own trial, measure the complete process. A draft produced in seconds is less useful if it takes ten minutes to correct. Record the time spent reviewing, fixing errors and handling exceptions, as well as the time saved on the initial task.
Ask the team whether the change makes work easier. Fewer interruptions, clearer handovers and less searching can matter alongside speed.
Start with a small, supported trial
Choose a frequent task with a clear owner and an outcome you can check. Write down how it works today, including the awkward cases. Use representative examples with sensitive information removed where possible, and agree which tools and data are approved for the trial.
Set a review point and decide what success means before you start. Keep a straightforward manual route available when the system cannot complete a task. Someone should own the workflow after launch, including updating instructions, investigating failures and reviewing ongoing costs.
Most importantly, involve the people doing the work. They know which exceptions matter and which steps exist for a reason. Their feedback should shape both the first version and the decision to continue.
The best place to begin is a recurring frustration your team can describe clearly. What do people keep doing by hand, and what would a reliable improvement look like?
If you have a workflow in mind, discuss it with MyEvent Labs. We can help define a focused trial, identify the checks it needs and decide how to measure whether it is worth taking further.