Chapter 5. GenAI and the future of app development
Table of Contents
Software development is currently one of the top use cases of generative AI. A Github survey of 500 U.S. developers working in large companies in July 2023 revealed that 92% are using GenAI for coding at work. They say these tools remove the drudgery of everyday tasks, provide up skilling opportunities, and improve performance, coding, and collaboration.19 The global estimate is that 5% of enterprise software engineers were using it at the end of 2023, but that number could rise.20
These numbers have led to all kinds of speculation about what the future holds for app development in the GenAI era. This section will explore the most likely scenarios, so that you can be prepared as you think about how you will develop applications in the future—and who will develop them.
“The future is going to elevate the profession. It’s going to become more strategic for sure. But, in some areas you’ll need more precision in the code and you’ll need a human to code.”
—Paulo Rosado, CEO and Co-Founder, OutSystems
How GenAI could change how IT leaders approach application development
Generative AI is already accelerating software development and deployment by automating coding, testing, and optimisation. As even more generative AI tools come online, the impact on IT will be even greater. Application development in your IT organisation will definitely not be the same. Here are just a few of the things that will be different.
Skillset shifts
Because generative AI automates many coding tasks, developers will begin engaging in more high-level, strategic work. This automation is likely to elevate the developer to the point where they become architects and designers of very large systems. It also accelerates the time it takes them to deliver business value, and allows them to decide things at a much higher level.
At the same time, IT professionals will also need to develop skills in AI and ML to use these tools effectively and oversee their implementation. Even though a lot of work is done by the machine, it’s important to be able to read the output, understand it, adapt it, and change it.
“To use GenAI tools for traditional code, you still need to be an expert.”
—Rodrigo Coutinho, Cofounder and AI Product Manager, OutSystems
Organizational structure changes
The increased efficiency and speed of application development will lead to changes in the structure of your IT team. Greater emphasis will be placed on cross-functional collaboration, with AI specialists working closely with domain experts and business stakeholders. This will lead to a more collaborative and business-focused approach to IT, with a greater emphasis on delivering user value. At the same time, a centralised governance structure will need to be in place to oversee the use of GenAI tools and ensure that they are being used properly—especially by the business.
Greater focus on agility and iteration
IT teams will need to be even more agile and iterative than they are now in this new GenAI world. The ability to quickly prototype and test ideas will also mean adopting a more experimental approach to development, with an even greater focus on continuous improvement and innovation.
Automation management
GenAI will take over many repetitive and time-consuming tasks, automating IT organisations in ways that are unexpected. Although automation frees up resources for more strategic initiatives and innovation, it is likely that managing it all will require re-skilling and up skilling members of your team.
Continuous learning and improvement
Your IT organisation will need to keep learning, adapting, and improving to stay up-to-date on GenAI. IT leaders will need to be informed regularly about the latest advancements in generative AI and ensure their teams have access to ongoing training and development opportunities. They should also be encouraged to continuously improve their GenAI output, whether that’s code, content, or both.
Tips for continuous improvement
- Providing source material creates trust
- Asking for a thumbs up or thumbs down for feedback is helpful in improving the app
- Implementing human spot checks
Ethics in IT
No longer just the purview of legal, your IT organisations will grapple with ethical considerations around the use of GenAI. Prepare to develop guidelines and principles for responsible AI development and deployment and how to ensure transparency and accountability in the use of these tools.
All this adds up to an important fact: There’s a lot for IT leaders to consider when preparing for the use of GenAI for application development and the SDLC. For starters, here’s what it means for the fate of traditional code.
GenAI and traditional code
GenAI offers people who are not writers, designers, videographers, animators, or artists the chance to create impressive work with a simple prompt. But since GenAI models have also been trained on massive amounts of code, it’s a no-brainer that developers are going to use it to build applications. These changes aren’t going to happen in some misty future—they are already in the works.
Using natural language to build applications
It’s only a matter of time before natural language replaces coding complex syntax with plain English and other languages. A combination of GenAI prompts and AI automation will take over SDLC management with real-time feedback loops, intuitive assistance, and self-healing capabilities to enhance reliability and operational efficiency.
So, where does that put coding in general? In a precarious state. Consider this prediction from a recent InfoWorld article: IDEs will become assembly platforms where developers focus on integrating pre-built components rather than writing custom code from scratch.21
The rise of full visual development
With the even greater potential for GenAI to generate full applications or a massive amount of applications, there will be a need to reverse engineer to make sure a human understands it. As a result, there will soon be tools to explain the code that was generated. At that point, the code will become something that is no longer part of the conversation; it will be buried inside interfaces.
“Written code is disappearing.”
—Paulo Rosado, CEO and Co-founder, OutSystems
Useful resources
Gartner® Emerging
Tech Impact Radar
Explore Gartner's in-depth analysis on Generative AI.
AI Adoption in Software
Development: Report Insights
How enterprises are navigating AI adoption and implementation.
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19 Survey reveals AI’s impact on the developer experience, 2023. Github.
20 Lucas Mearian, Here’s why half of developers will soon use AI-augmented software, 2023. Computerworld, 6 Dec.
21 Isaac Sacolick, “10 ways generative AI will transform software development,” 2024. Infoworld, 12 Feb.
Originally published on OutSystems.com
