Automation is often discussed in terms of tools.
Which platform should we use? Can this system integrate with that one? Do we need an API? Could AI handle it?
Those are useful questions, but they are rarely the best place to start.
Before thinking about the technology, there is a more important question:
Can you clearly describe the decision you want to automate?
If the answer is yes, there is a good chance you have found a process worth automating.
If the answer is “it depends”, the next question is: what does it depend on?
And if nobody can quite explain that, the process may need some work before automation should enter the conversation.
Look for the decisions you are already making repeatedly
Some of the best automation opportunities are hiding inside very ordinary statements:
- “Every Monday, I need to do this.”
- “Whenever a course is rolled out, I send these emails.”
- “When a sale closes, I enter the same information into these systems.”
- “If someone submits this request, I notify their manager.”
- “When X happens, I always do that.”
If Action A consistently results in Action B, you potentially have something that can be automated.
It might be a relatively simple trigger and action, or it might become a more sophisticated workflow using conditional logic, integrations, APIs or webhooks. The technology will vary. The principle does not.
You are taking a standardised decision that someone is making manually and translating it into logic a system can follow.
For example:
A lead meets a defined set of criteria, so it is automatically assigned to the appropriate salesperson.
A contract is sent, so Operations is notified that a dedicated manager needs to be assigned.
The contract is signed, so the company records are updated with the services the client has subscribed to.
A new client is added, so rollout milestones are created and a renewal date is calculated from their contract details.
None of those decisions necessarily require somebody to sit at a desk and manually move information from one place to another.
And that is the opportunity!
But first, make sure the decision is actually standardised
Automation works well when the rules can be described clearly.
For example:
If X, Y and Z are true, perform Action A.
If T, Y and Z are true instead, perform Action B.
That gives you something concrete to build.
The problems start when two identical scenarios regularly result in completely different decisions.
If the same criteria sometimes lead to Outcome A, sometimes Outcome B and sometimes Outcome C, with no tangible property explaining why, the process probably is not ready to be automated.
At that point, you risk spending more time maintaining the automation than you would have spent completing the task manually. It can be a frustrating realisation, especially when the whole point was to make life easier, but sometimes the right automation decision is deciding not to automate at all.
This is why improving a process and automating a process are not the same thing.
Sometimes the first step is not building the workflow. It is agreeing on how the process should work.
Automating an action is easy. Automating the decision takes more thought.
Sending an automatic email is relatively straightforward.
The more interesting questions are:
Who should receive it?
When should they receive it?
What needs to be true before it is sent?
What happens if a particular piece of information is missing?
What happens if the expected action does not take place?
What happens when something falls outside the normal process?
This is where triggers, conditional logic, workflows, integrations, APIs and webhooks become useful.
At a basic level:
- A trigger tells the automation when to start.
- Conditional logic decides which route the workflow should take.
- An integration allows different systems to exchange information.
- An API gives systems a structured way to communicate with each other.
- A webhook can notify another system immediately when a particular event occurs.
You do not necessarily need to become the technical expert who configures all of these yourself. Your technology team, implementation partner – or increasingly, a well-directed AI assistant – can help with the technical side.
The more valuable skill is often being able to define the business logic first.
What should happen? Under which conditions? What information is needed to make the decision? And what should happen when something goes wrong?
Build automations as broad as possible, but as specific as necessary
A good automation should cover the normal process without becoming so rigid that every unusual situation causes it to fail.
That means thinking about edge cases from the beginning.
If a workflow normally expects A, B and C to happen, what should it do if B is missing?
Should the process stop?
Should somebody be notified?
Should the record be flagged for manual review?
A robust automation does not just automate the successful outcome. It also tells you when the expected outcome did not happen.
Error notifications, fallback routes and exception handling are just as important as the main workflow itself.
The aim should be to make the automation broad enough to accommodate legitimate variations, while keeping the decision criteria specific enough that the system still knows what to do.
Avoid building workflows around things that are likely to change
One practical lesson is to be careful about using individual people as criteria.
For example:
“Assign this training to everybody whose manager is Jane.”
That might work today.
But what happens when Jane changes role or leaves the business?
The automation still exists, the original reason for the rule may be forgotten, and six months later somebody is trying to work out why employees are no longer being assigned training correctly.
Where possible, use stable organisational properties instead:
Department. Location. Seniority. Employment type. Business unit.
Those are much easier to maintain than rules tied to individual people.
The same applies more broadly: good automation depends on good data.
Training is a perfect example
This becomes particularly relevant in HR, L&D and Compliance because so much training administration involves repeatable decisions.
Take a new employee joining the business.
Someone may need to:
Create their account on the learning platform.
Work out which courses they require.
Assign those courses.
Monitor completion.
Send reminders.
Escalate overdue training to the employee’s manager.
Record the outcome.
Repeat.
This is where a well-designed training matrix can become the foundation for automation.
Rather than deciding manually what every employee needs, you define the logic in advance.
An employee in Department X, Role Y and Location Z requires Courses A, B and C.
An employee with a different role may require A, B, D and E.
A manager may need an additional course.
An employee in a regulated jurisdiction may require another set altogether.
The training matrix effectively becomes your decision-making model.
Your HR system should provide the source data
The strongest setup is one where the attributes used by the training matrix are already officially recorded in the organisation’s HR system.
Job title.
Department.
Location.
Seniority.
Employment type.
Business unit.
That gives you consistency in how those properties are named and maintained.
You can then use an API or HR feed to connect the HRIS with the learning platform.
When somebody joins, changes role, moves department or leaves, that information can flow into the training environment without relying on somebody to manually recreate it.
The HR system provides the employee data.
The training matrix provides the decision logic.
The learning platform carries out the resulting action.
That is a much more scalable model than asking someone in HR, Compliance or L&D to repeatedly interpret the same information by hand.
Then automate the administration around the training
Provisioning and course assignment are only part of the picture.
Once training has been assigned, the same principles can be applied to the administration that follows.
If a learner has not completed a course seven days before the deadline, send a reminder.
If the deadline passes, send another notification.
If the course remains incomplete after an agreed period, escalate it to the learner’s manager.
The exact workflow will depend on your organisation. The important thing is that the decision does not need to be remade every time.
You decide the rule once, build it into the process and let the system apply it consistently.
Where iGaming Academy can support
Within iGaming Academy, we can help organisations apply these same principles to the way they manage internal training.
That may mean using integrations or HR feeds to support learner provisioning, creating a structured training matrix based on employee attributes, automating course assignments or reducing the manual effort involved in reminders and escalation workflows.
The technology is only one part of the equation.
The real starting point is understanding who needs what training, when they need it and what should happen next.
Once those decisions are clearly defined, there is far more opportunity to automate the administration around them.
And that is ultimately what good automation should do.
It should not automate something simply because it can.
It should remove the repetitive work that comes from making the same predictable decision, based on the same information, over and over again.
So the next time you find yourself repeating a process, ask:
Am I actually making a new decision here – or am I applying a rule I have already decided?
If it is the latter, you may have just found your next automation.
Author: , Head of Client Success