.webp)
Artificial intelligence is the hottest topic in the business world right now. In a matter of months, generative text programs like ChatGPT have gained mainstream visibility and rearranged companies' priorities. Investment is flowing into the technology, and decision-makers across fields are asking how AI can impact their specific workflows.
Software engineering is no exception. Spurred by the widespread visibility of AI, stakeholders are considering ways to bring the technology into the engineering pipeline, while the engineers themselves are wondering how their roles will change as a result.
It's important to note that some of the media attention around AI is out of step with its present capabilities. The generative text takeover is not imminent in software engineering. However, there are some places where AI algorithms and machine learning can make development less laborious and more efficient.
Knowing how to make intelligent, objective-driven use of AI systems is a valuable skill to have in both the immediate and long-term future. Software engineers who take the time to understand what the solutions can and can't do — and where they're going next — can set themselves up to ride the currents within their industry.
While AI has never been more hyped or popular, it's worth clarifying how these systems work. Namely, AI is evolving along a pathway that was set down for it decades ago. The recent explosion of popularity around the technology is partly a matter of visibility rather than a sea change in the direction of AI development.
Another piece of the puzzle regarding AI's new status quo involves the democratization of powerful infrastructure. In an era of widespread cloud computing and distributed infrastructure, businesses are experimenting with new capabilities for AI algorithms. Generative programs today are working with horsepower and data sets that weren't available in past decades.
The current state of the art in generative AI involves producing workable answers to queries, and doing it quickly. This has an immediate relevance for software engineers. Programmers who may once have polled their peers on online forums or reached out directly for insights can now use AI to answer their questions.
Answering questions with AI is a powerful process, but it takes effort. The algorithms have a vast repository of online knowledge to pull from when giving their automated recommendations and replies, but to get useful and accurate responses, engineers must make sure they're asking the right questions.
While AI isn't the cure-all it's sometimes portrayed as, it is a versatile technology with many applications in a wide variety of fields. This includes software engineering, where the algorithms can assist with the completion of work that's largely basic, repetitive or programmatic.
This assistance with simple tasks may come as a relief to software engineers, who can rebalance their schedules to spend more time on tougher, more strategic tasks. As for specific programmatic functions that can benefit from an infusion of AI, these include:
As for next steps, AI can potentially transform areas such as user interface and user experience design. Companies could algorithmically generate UI and UX capabilities that meet industry standards quickly.
However, future capabilities for AI-aided development are held back by a few important considerations. For example, organizational leaders hesitate to allow AI into the development pipeline because intellectual property and ownership questions around AI-created code are still untested in court.
The current state of AI in development, at least from a legal oversight perspective, is close to the early years of open-source code. Due to a lack of clarity and precedent around the technology's IP implications, organizational policies may still dictate that developers can't use these methods.
Considering the fact that changes are likely coming to software development — and some are already here — engineers are asking which skills are most important in an AI-infused industry. Being a top member of a software development team will still take largely the same skill with AI as without it, with some key focus areas.
Today, most software engineering is more concerned with the strategic work of solving problems than the physical act of writing code. This will remain the same as AI's presence grows more prominent, but the divide is set to become starker than ever.
Engineers' primary role in the development process is to think and strategize, identifying the issues to be solved with software and creating a program that will reach that objective. Since AI is far better at accomplishing discrete tasks than developing novel solutions, there will be a clear split between people making plans and AI contributing to brute-force code generation.
In addition to creativity and strategy, software engineers in an AI-enabled pipeline should specialize in reasoning and quality assurance. This will prove especially useful in the future, as AI algorithms become responsible for larger sections of standard code.
Engineers looking at the code will have to test it, using a problem-solving mindset to ensure the software adequately addresses the problem it’s meant to solve. This same process holds true with code generated entirely by human engineers. Still, it is more pressing when the code was developed by an unthinking algorithm that may not have spotted issues earlier.
The next few years will likely see a change in the means used to accomplish software development objectives, but those objectives themselves will stay the same. The goal of a development process is to identify a problem and solve it, and that will remain true.
The most successful software engineering teams in a more AI-enabled era will likely be those with rich domain knowledge and the ability to make informed strategic decisions to keep their projects on the right track.
If there is a change from the present, it will likely involve the emphasis of those strategic-thinking and domain-expertise abilities. Engineers whose primary selling point is the ability to turn out large amounts of relatively standard code may have to reskill to ensure their ongoing value to their teams.
As for new skills rising to the fore, every member of development teams should work on the ability to ask the types of questions that AI algorithms respond to. Getting quick answers to development questions is a valuable workflow enhancer, but only if engineers grasp how AI tools process information. Otherwise, they may waste time searching for usable answers.
Amid ever-changing development conditions and rapidly evolving technology, there is an ongoing need for companies to work with knowledgeable engineers who are thoughtful and intentional. These experts can use their domain knowledge to identify problems accurately and deliver solutions, whether their preferred methods incorporate AI or not.
Even as AI takes over some of the work of code generation — a process that will, itself, require the resolution of ongoing IP issues — human insights from knowledgeable thinkers will remain valuable. To receive this guidance, development teams must ensure they're working with the right people.
External consultants and experts can take a fresh look at a project and deliver a tailored solution. This process could take the form of either a one-off engagement or an ongoing partnership, adding effectiveness to a development pipeline without the organization having to find permanent employees.
This is where Transcenda's experts can make their mark. Thoughtful engineering from these professionals can lead to ideal results for a development project. Since Transcenda's team comes with cutting-edge domain expertise, internal teams can learn whether AI is applicable to their present situation and, if so, use it in a suitable and effective manner.
To learn more about software engineering with insights from Transcenda, contact us today.
