Artificial intelligence (AI) has been creeping into our lives as traditional software incorporates predictive elements into existing features and utilities. The full
potential of AI has not yet been unleashed as computing power, especially GPU availability, has constrained the rapid growth of deep learning models. At the same time, generative pre-trained transformers (GPT) are creating a plethora of predictive use cases as they develop the capability to process billions of parameters. One such use case is expected to be autonomous application development.
No-code platforms have made application development a breeze as users communicate with machines through drag and drop symbols. Autonomous application development is expected to take this a step further by first converting what is effectively sign-language based interaction with machines into natural language interactions. This is further expected to graduate towards suggestive improvements from the no-code platform on improving UI and UX elements based on user engagement. Ultimately no-code platforms are likely to build entire apps autonomously by analysing gaps in the digital journey.
Natural language engagement with no- code platforms
No-code platforms are expected to allow users to define application specifications, communicate their imagination, and respond to specific questions in the app development life cycle using natural language. Natural language is expected to replace drag and drop symbols in the next 2 years ushering in an era where conversion of ideas into software will not require business analysts or solution architects. No-code platforms will be able to receive instructions verbally and ask intelligent questions to fill in the gaps in the application development lifecycle. Application development timeframes are likely to come down to a few days once people can communicate with no-code platforms in natural language.
Suggesting improvements in applications based on end-user engagement data
The next step in the progress of autonomous app development is likely to be analysis of data based on user navigation patterns and flows. No- code platforms will be able to infer improvements in software like reducing the number of clicks for navigation or introducing specific features that will allow the user experience to be enhanced dramatically. Currently, UI and UX designers spend a significant amount of time both ideating and then analysing user data to improve applications. No-code platforms will automatically analyse this data and first suggest improvement and then autonomously make changes dynamically to applications. This will also enable a high degree of autonomous and dynamic customisation of software for different users or different types of users.
Building applications to complement existing software
No-code platforms are used extensively to help rapid digital transformation. They still have to work closely with legacy software. This means that automation is likely to have gaps at points of interface or transmission. It also means that every small change in legacy software especially related to data structures is likely to break the digital journey or increase the cost of maintenance. Autonomous no-code platforms will independently change the data structures, interfaces and flows of applications built on them to adapt to changes in legacy applications. Any breakage in the digital journey will be autonomously addressed by no-code platforms in minutes. This is likely to save tons of time that goes into analysing the impact of changes in software data structures across applications
especially in downstream analytical applications.
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Autonomous digital transformation
The end-goal of autonomous no-code development is digital transformation without any human intervention – platforms that analyse existing digital flows against defined or established business models and start creating software without any input from humans. No- code platforms are truly expected to achieve Turing completeness or equivalent where software builds software to achieve a purpose over the next 10 years. This would be the most important use case for artificial intelligence to make today’s human struggle with software development redundant.
Views expressed by Muzammil Patel, Global Head Strategy and Corporate Finance, Acies.
