# The Role of
Software Engineers in the AI Era
In today's era of AI generating more code than ever before,
what's the role for software engineers? Why not less, software engineers, when
AI seems to be outstripping them in coding?
## AI's Impact on
Software Development
AI has been all the rage in the tech industry. Often
headlines read: “AI now writes the majority of the code of top tech firms”. The
majority of code created by the best tech companies is utilized with AI, many
say. However, the posts are posted on social media and the climax of it is that
software engineering is dying, programming is not required anymore and people
in the future, who still want to be software engineers, should reconsider their
choices.
These are statements that snag the reader's attention, but
don't provide an exhaustive picture.
But things are not as simple as they seem. AI is
revolutionizing the software development industry, but it's not taking the
place of software engineers. Rise of AI is doing a lot to make the value of
professionals with system, architecture, enterprise needs and problem solving
more valuable.
### AI in Code
Generation
It's not like AI is incapable of writing code. It clearly
can.
The bigger concern is that can AI supplant the entire job of
a software engineer.
The answer is not simple though, and would not be
encapsulated in a simple headline.
AI-generated code is anything but code that is just for the
sake of it. AI generated code is NOT code for the sake of code.
If the company says that they are employing a high
percentage of AI-coded code, many folks take a whole software designed using
AI.
Simply put, it will not be like this in real life.
Coding a single line of code isn't the end of the software
development process. All features start with business requirements to customer
needs, architectural decisions, security, compliance requirements, and
integration planning.
There are a number of things an engineer needs to know:
* What is the issue that you are looking to solve?
* The way the feature will be employed in the system
(including by the user)
* Who is consuming who for applications and/or services?
* If they change, what are some potential challenges that
need to be overcome?
* Description of how the solution will affect customers'
operation and business.
Once the requirements have been identified, AI can help in
generating implementation code. The generated code however, needs to be
checked, validated, tested, optimized and deployed by hand.
Using AI can speed up the coding process.
Can't take ownership of the whole ecosystem of an
organization.
It's a significant difference.
### AI vs Human
Engineers
In a complex system software lives (enters the system).
There is a preconception that people have about the software
that is generated by the AI; that it can be an app in itself.
It's not the way an enterprise software typically operates.
## Why Software
Engineers Are Still Essential
Multiple (dozens of) systems are able to be connected to one
application in front and downstream. APIs, databases, messaging, reporting,
analytics pipelines, and customer applications are all used for the flow of
information.
### Beyond Coding:
System Thinking
Sometimes it takes a simple change to have an unforeseen
effect.
Imagine that you have a financial portal from which
customers are also able to get information from various services. A slight
change in one part can cause reporting systems, customer dashboards and
notification services, compliance processes, and data warehouses to be
affected.
AI can develop code for the desired modification.
But it is crucial to apply human common sense to understand
all the dependencies, business rule and operational risk.
Software engineering is NOT about coding!
It is a system's thinking approach.
The value of system thinking in technology is still one of
the most valuable skills.
### Handling Complex Systems
There are still plenty of organizations that are looking for
engineers.There are still many companies that have a demand for engineers.
#### Integration and
Dependencies
All software applications have bugs and/or unexpected
behaviour, as well as evolving over time based on the changing need of the
business.
When a production goes awry, these are the kinds of things
an organization needs professionals to be able to do:
* Diagnose root causes
* Assess business impact
* Coordinate with stakeholders
* Undo changes, if needed
* Implement fixes safely
* Prevent future incidents
#### Operational
Risks and Challenges
Accountability matters.
Delegating responsibility to an AI model is not an easy task
for businesses to do.
Customers and regulators demand reliable systems and
investors and leadership teams want them. In the event of problems, they should
have expert engineers that know how all the aspects work together.
This is a requirement alone, which makes software
engineering necessary.
Implementation help can be provided by AI.
It remains still in the hands of Man.
## Final Thoughts
AI is transforming software development, and it's all quite remarkable.
The performance of code generation is getting quicker. Transitions to the development process are improving. Less repetition of work and more is getting done.
However, software engineering isn't all about coding.
It encompasses an understanding of systems, problem solving, handling complexity, risk management and providing value-enabling solutions that are reliable.
AI tools will be a key factor for the engineers who are willing to adopt them and bolster their technical skills, who will be most successful in the coming decade.
AI is not the future – it's just one part of it.
It's a part of those professionals that know how to work with it.
Instead of the question ‘will AI replace software engineers?’, a more pertinent one should be ‘by how much will AI augment software engineers?'.
In what ways can software engineers become a whole lot more effective by leveraging AI?
How to do that will shape the future of tech careers.
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