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Today, using AI has become much easier than before. You open ChatGPT or Claude, enter a prompt, and get an answer within a few seconds.
From this, many people may think that if AI is so easy to use, then adding AI to a company's business process should also be easy.
But this is where the real story begins.
Using AI is easy, but bringing AI into an old business and making it work properly is not easy at all.
In today's post, we will discuss in simple words why implementing AI in a business is so difficult, what an FDE or Forward Deployed Engineer actually is, and why this role is becoming important.
So, without wasting more time, let's get into the main post.
Running an AI model and properly putting that model inside an old business process are two completely different things.
From the outside, it may look simple:
Build a model → Give it data → Get output → Done.
But when you go into a real business, you will see that the real work has only started.
This is something many companies do not understand at first. Later, they end up spending both more time and more money.
Suppose a company wants to automate its customer support using AI.
From the outside, it looks very simple. An AI model will be created, customer questions will come in, the model will answer them, and the work will be done.
But in reality, many questions will come up.
Where will the customer data come from? Which data can be given to AI? Where is human approval needed? How will AI connect with the old software? What will happen if the database is very old? How will security and privacy be maintained? Who will check if the AI makes a wrong decision?
Many such small questions can later become big problems.
And this is where both domain knowledge and technical knowledge are needed.
A business expert may know very well how the company's entire process works.
They know where the problems are and where automation could save time or reduce costs.
But they may not be able to build an AI system.
On the other hand, an AI Engineer may understand models, APIs, databases, cloud systems, and software architecture very well.
But they may not know why a 10-year-old business process works in a certain way, where approvals are needed, which data is actually important, or where using AI could create problems for the business.
In simple words:
Business people know where the problem is, while engineers know how to build a solution using technology.
The gap between these two worlds is a major challenge.
And this is one reason why the role of a Forward Deployed Engineer, or FDE, is getting more attention.
In simple words, an FDE is an engineer who does not simply sit in an office, build software, give it to the customer, and consider the job finished.
Instead, the FDE works very closely with the customer.
Sometimes they may work directly with the customer's team, understand the business process, see how data is being used, and find where AI can create real business value.
Then they build an AI solution based on that understanding and integrate it with the customer's existing system.
So the job is not simply:
"I built the software, so my work is finished."
The real goal is to make sure the software solves a real problem for the customer and eventually creates a useful business outcome.
Suppose a large company wants to use AI to process thousands of documents.
A normal Software Engineer might build an application where users upload a document and AI reads it and extracts information.
Sounds great, right?
But let's look at the real situation.
First, an FDE would understand where the company's documents come from, who reviews them, where approval is required, what types of documents are most common, where AI can safely make decisions, and where a human must make the final decision.
Then, based on that business process, the FDE would build the AI solution.
This is what makes the FDE role different.
If a separate software system is built from scratch for every customer, things may look good at the beginning.
The customer is happy, and the engineering team has built a solution.
But as the number of customers grows, problems can start.
Suppose 10 different systems are built for 10 different customers.
Now every system has its own codebase, bugs, maintenance requirements, and updates.
After some time, the engineering team may reach a point where fixing a bug in one system creates another problem somewhere else.
Then you may realize that what looked like a very smart solution at the beginning has now become a major problem.
That is why having a reusable base or strong platform is very important for a successful FDE program.
It means you do not have to build everything from zero every time.
Instead, there is a core platform where the necessary tools and components are already available.
Then the FDE can use that core platform to create a solution based on each customer's specific needs.
Simply put, it is not about building a completely new house from zero every time.
There is already a strong structure, and you customize it according to the customer.
Instead of building everything from scratch, you connect existing components to create a custom solution.
When a business grows, this difference can become very important.
AI is no longer limited to simple chatbots.
The use of AI Agents, Automation, and Agentic Software is also growing.
Before, software often needed to be told exactly what task to perform and how to perform it.
Now, in many cases, an AI Agent can follow several steps and complete tasks on its own.
As a result, software is becoming more modular, customizable, and autonomous.
But there is an interesting part here.
As the technology becomes more advanced, implementing it for customers can also become more complex.
It is no longer enough to understand the software.
You also need to understand where an AI Agent will work, what data it will use, which decisions it can make by itself, where human approval is required, and how mistakes should be handled.
This is why roles like FDE could become more important in the future.
Customers do not simply want an AI model.
They want AI to work properly inside their existing business.
If we think about Bangladesh, the situation becomes quite clear.
Large companies, banks, government organizations, and different enterprises have years of domain knowledge built into their systems.
They know how their business works, where problems happen, and which processes are slow.
But sometimes they may not have a technical team that can properly bring AI into those old processes.
Again, even if an outside AI company builds software for them, the job is not necessarily finished.
Building software and making that software work with the real operations of a large organization are two different things.
Suppose an organization has been using an old software system for 15–20 years.
Now you want to add AI to it.
Simply building a new AI model will not solve everything.
You also need to understand how data will move between the old system and the new AI system, whether APIs are available, how security will work, how employees will use it, and many other things.
This is where an FDE can work as a bridge.
On one side, they understand the customer's business.
On the other side, they understand AI and engineering.
Then they bring these two areas together to create a real solution.
Whether it is a large enterprise in Dhaka or an organization somewhere else in Bangladesh, the basic problem can be similar:
The technology exists. The business knowledge exists. But there may not be enough people who can connect the two.
I think the role of an AI Engineer is also moving into a much broader area.
Simply training a model with a dataset or creating an API may not always be enough.
An engineer who can understand business problems, communicate with customers, understand data, build AI solutions, and take those solutions into production can have a much wider role.
Because in the end, a company does not simply want to buy an AI model.
They want AI to solve a real problem, save time, reduce costs, improve quality, or create a new business opportunity.
In simple words, customers do not always care about the model's name or how many parameters it has.
What they really want to know is:
"Brother, how did this thing actually help my business?"
There is a lot of hype around AI right now.
Everyone is learning AI. Everyone is building agents. Everyone is talking about automation.
But the real skill is not just knowing how to use AI tools.
The real skill is understanding where AI should be used, how AI should be placed inside an existing business process, and how to finally create a real business outcome with it.
Simply put:
Building AI is one thing. Using AI to solve a real business problem is another thing.
And somewhere between these two areas, there may be a big opportunity for future FDEs or Forward Deployed Engineers.
AI has now become much easier to access and use.
But taking AI into an enterprise and making it work properly is still a difficult engineering and business challenge.
So, if you want to build a career in AI in the future, do not limit yourself to learning only models or frameworks.
Try to understand business problems.
Learn how to communicate with customers.
Understand how existing systems work.
And learn how AI solutions can be taken into production.
Because in the end, companies will not always judge you only by how many models you can build.
What may matter much more is how well you can use AI to solve their real problems.
In the world of AI, the demand for engineers who can not only write code but also understand business may continue to grow.
That's all for today.
I hope this post gave you a simple idea about FDE or Forward Deployed Engineering.
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With an invitation to everyone to stay with TrickBD, I am ending today's post here.
Goodbye, and thank you everyone.
