When a company reaches the point of thinking, “We need to implement AI,” the next question usually sounds like this:
“Okay. Where do we start?”
And this is where it is very easy to take the wrong path.
Buy a few licenses.
Connect a fashionable service.
Launch one AI pilot in every department.
Six months later, end up with a pile of different systems, subscriptions, several nice presentations, and almost the same processes as before — only now with an AI note attached somewhere on the side.
That is why proper AI implementation does not start with choosing a model.
It starts with a complete analysis of how the company actually works.
First, You Need to See the Whole Picture
A company may have dozens or hundreds of processes, and almost every one of them can be automated to some extent.
But that does not mean everything should be automated at once.
First, you need to run an audit and understand:
- where the largest amount of routine work happens;
- where people constantly repeat the same actions;
- where delays and errors occur;
- where data is transferred manually between systems;
- where the process depends on a specific employee;
- and finally, where automation will create the biggest business impact.
This is an important point.
AI should not be implemented where it looks fashionable or technically interesting.
It should be implemented where the company will actually feel the result.
The First AI Project Should Remove a Real Pain Point
Imagine a sales department.
Every day, managers:
- answer the same customer questions;
- look for prices and product specifications;
- check availability;
- prepare similar commercial proposals;
- copy information into CRM, out of CRM, through CRM, around CRM…
- pass the same data further through the process.
Each individual action does not take much time, but together the company spends a huge amount of resources on routine work.
That is already a good candidate for automation.
The system can take over request handling, data collection, initial proposal preparation, checks, data transfer between systems, and even communication with the customer, for example by email.
And this creates a double effect.
The company gets a faster and more stable process.
And people stop spending a large part of their day on work that no longer really requires a person.
A Good Result Is When the Team Says “Thank You”
For us, this is one of the best indicators of a successful implementation.
Not only an ROI chart.
Not only the number of automated operations.
But a simple reaction from an employee:
“Finally, we do not have to do this manually anymore.”
That is why the client’s team needs to be involved already at the audit stage.
Management sees the process from above.
The people who work inside it every day see it from the inside.
They know very precisely:
- what is most frustrating;
- where time is being lost;
- which actions are repeated dozens of times;
- where errors happen most often;
- what could have been automated a long time ago.
And there is another important effect.
When the team itself helps identify the problem and design the future solution, resistance to change becomes much lower.
It is no longer “management has brought us another new system.”
It is a solution that removes a specific pain point the team itself helped identify.
After the Audit, You Need an Opportunity Map — Not a List of Fashionable Ideas
The next step is to collect potential scenarios and evaluate them properly.
For example, by criteria such as:
- expected business impact;
- amount of manual work;
- process frequency;
- number of people involved;
- cost of error;
- data readiness;
- integration complexity;
- cost and speed of implementation;
- and so on.
After that, it becomes much clearer where the company should really start.
In one area, a few months of work may remove hundreds of hours of manual effort.
In another, the company may spend a year building a beautiful AI feature that will be used twice a month.
We prefer the first option.
Do Not Be Afraid of Strange Processes and Old Systems
Real business almost never looks like this:
CRM → AI → result.
More often, it looks something like this:
ERP → Excel → email → old database → manager → PDF → another system → back to ERP.
And somewhere in the middle there is a person who “knows what to do when something goes wrong.”
For us, this is not a problem.
If the process is already digital, it can be automated and enhanced with AI.
Through APIs.
Through databases.
Through workflow platforms.
Through a dedicated integration layer.
Through local, cloud, or hybrid architecture.
And if the process has not been digitized yet, it can be digitized first.
This is not a wall.
It is simply another important stage of the work.
Sometimes the Right Solution Is Not to Use AI at All
We are convinced that not every automation should be AI-powered.
If a task can be handled reliably with a standard algorithm, rule, or API, that is usually exactly what should be used.
AI makes sense where it is genuinely needed:
- working with unstructured text;
- document analysis;
- request classification;
- searching for and matching context;
- working with large volumes of information;
- natural language interaction;
- supporting complex decisions.
That is why in a good AI solution, “pure AI” may account for only around 20%.
The rest is data, logic, integrations, algorithms, validation, security, and solid engineering work.
Start Gradually
A proper AI transformation should not look like “let’s implement AI everywhere by the end of the quarter.”
A better approach is:
1. Audit.
Understand the processes, systems, data, and problem areas.
2. Opportunity map.
Identify where automation or AI can create the greatest impact.
3. Prioritization.
Choose the first scenario with the best balance between value and complexity.
4. Pilot.
Build a working solution around a real process.
5. Validate it with the team.
Check whether the process has actually become faster, simpler, and better.
6. Scale.
Move on to the next processes based on real experience.
This way, the budget is not spent on ticking the box “we also have AI.”
It is spent on gradually improving the company’s operating model.
Conclusion
There is a simple answer to the question “Where should we start with AI implementation in the company?”
Not with AI.
Start with the company.
Look at its processes, systems, data, and people.
Find the routine.
Find the losses.
Find the process where automation can create the greatest impact.
Make it better.
Let the system take over the mechanical work.
And let the team say:
“Thank you.”
Then move on to the next process.
That is how AI transformation turns from a collection of experiments into a controlled development program for the company.
Learn more about the SOLARA approach: AI Implementation in Business Processes.
FAQ: Where to Start with AI Implementation in a Company
What Is the Best Place to Start an AI Transformation?
Start with an audit of business processes, systems, and data. First, you need to see the company as a whole and identify the processes where automation can create the greatest business impact.
Should AI Be Implemented in Every Department at Once?
No. It is more practical to start with one or several priority scenarios, measure the result, and only then gradually scale the solution.
How Do You Choose the First Process to Automate?
A strong candidate usually involves a large amount of repetitive manual work, happens frequently, involves many people, has a clear outcome, and allows the company to measure time savings, cost savings, or error reduction.
What If the Process Has Not Been Digitized Yet?
First, the process should be formalized and moved into a digital environment. After that, repetitive stages can be automated and AI can be added where it genuinely provides value.
Why Should Employees Be Involved in the Audit?
The people who work with the process every day understand its real routine, exceptions, and bottlenecks better than anyone else. Their involvement helps identify more useful scenarios and increases motivation to use the new solution.
Does Every Automation Need AI?
No. Some tasks are better handled with standard algorithms, APIs, and business rules. AI is most useful when the process requires work with text, documents, context, large volumes of information, or natural language interaction.