This article is originally posted at Forbes.
The widespread excitement around AI—specifically the consumer-facing large language models (LLMs) like ChatGPT—has raised the profile of AI tools in popular consciousness. It has also prompted many decision-makers at companies around the world to ask pointed questions about how they can leverage the power of these game-changing new tools for their own benefit.
AI tools are not new. What is new, however, is the way in which LLM-based AI tools can be leveraged in innovative and impactful ways, perhaps most notably in the application development process. Appreciating the extent to which AI is elevating application development more broadly may provide an important and instructive window into the longer-term potential for this type of technology to have a profound impact across a wide range of industries and use cases.
The widespread excitement around AI—specifically the consumer-facing large language models (LLMs) like ChatGPT—has raised the profile of AI tools in popular consciousness. It has also prompted many decision-makers at companies around the world to ask pointed questions about how they can leverage the power of these game-changing new tools for their own benefit.
AI tools are not new. What is new, however, is the way in which LLM-based AI tools can be leveraged in innovative and impactful ways, perhaps most notably in the application development process. Appreciating the extent to which AI is elevating application development more broadly may provide an important and instructive window into the longer-term potential for this type of technology to have a profound impact across a wide range of industries and use cases.
Two AI Pillars
There are really two different types of uses for integrating AI tools into applications. The first is the one that has been around longer, where more traditional AI models (not LLMs) can be leveraged to identify insights from large volumes of data.
For example, you can run an AI model on a list of reported customer incidents with an application or customer process to sort and categorize those incidents and provide more insight into their causes and prevalence. This may not only identify the biggest problems but also inform an application developer’s approach to fixing them. By training AI on tech tickets generated from bug reports or other issues, it is possible to efficiently address the root causes of those issues and drastically decrease future incidents.
The second use of AI in application development is improving the efficiency of the team. This is a space where the rapid pace of innovation is being driven primarily by LLM-based AI tools that are transforming application development in profound ways.
AI tools are being leveraged at virtually every stage of the development workflow, from gathering customer requirements to analyzing the problems and designing, testing and deploying solutions. LLMs can make things faster and more efficient (sometimes dramatically so) throughout that development lifecycle.
Transformative Efficiency
Regardless of the different lifecycles or operational specifics used when building an application, it all starts with working to understand the client’s business needs. From brainstorming and strategy development to creating a roadmap, building the application, assessing requirements and teambuilding—and throughout the entire app development and digital delivery process—AI can feature prominently.
Early in the development process, AI tools can be used for:
- Brainstorming ideas
- Lean inception and design thinking
- Identifying market benchmarks
- Creating personas and suggesting different user journeys to better understand the customer and how the features will affect them (which helps define the scope of work)
- Running a risk analysis
- Identifying personnel availability and matching roles based on regional and skill-specific availability
On the design side, AI can help with:
- Requirements gathering
- Providing guidance to prototype
- Taking advantage of behavior-driven development (BDD) and starting testing even before the code is done
- Informing the UX/UI approach
Once development begins in earnest, AI becomes a time-saving powerhouse, with LLM-based tools that can deliver:
- Code optimization and code performance (providing coding suggestions or examples, performing coding tasks and generating documentation)
- Debugging, bug fixing and building code fixes for errors
- DevOps
- Enhancing the quality assurance process by automating testing steps like generating and running test scripts, and following a task list
- Continuous improvement
- Enhancing communication by translating to and from virtually any spoken, written or programming languages
The quality assurance functionality alone can significantly reduce the number of hours and professionals required for testing. We’ve found regression testing time can be reduced by more than 90% compared to traditional methods.
Building Proprietary AI Tools
For all the power of AI tools, the public-facing nature of LLM-based AI platforms is problematic from a security standpoint. Because solutions like ChatGPT would expose private and proprietary company and client information, many tech companies are either hosting an LLM model in a private and secure environment or are developing their own versions of these tools to use in specific ways to drive improvement in internal processes.
The key for any application developer is ensuring they train the model to provide the right answers. These LLM tools are only as good as the information they ingest, and pre-training a tool is essential to getting it to function the way you need it to.
With a library of tools and prompts in place, the application developer is like an artist with an endless palette of AI functionality they can call upon to streamline and supercharge the app development process. With the right UI, the game-changing power of LLM-based AI can be utilized in entirely customized ways, all with drag-and-drop and cut-and-paste simplicity.
Artificial Intelligence, Authentic Gains
Given the extent to which increasingly powerful and customized AI tools are already transforming the application development space, it is not unreasonable to think that these assets will continue to significantly increase efficiency throughout the application development lifecycle. But the truly exciting thing about AI in application development is not just the automation and efficiency, but the full scope of ways in which these tools are providing conceptual and procedural support—including creative concepts and idea generation.
The bottom line? AI makes it possible for application development professionals to deliver faster and deliver better, increasing quality, decreasing time-to-market and improving overall user satisfaction in the process.
