By Deepak Anupalli Co-Founder & CTO at WaveMaker, Inc.
The landscape of application development is set for transformative shifts, particularly those propelled by generative AI, poised to significantly accelerate two critical areas. The first focuses on digital automation, aiming to minimize human intervention, enhance efficiency, and streamline processes. Simultaneously, software automation is set to undergo a paradigm shift as Generative AI not only generates code but also validates security, ensures performance, and automates test case creation and documentation.
Generative AI’s ability to comprehend and respond to natural language will play a pivotal role, accelerating various digital processes and paving the way for a new era of digital automation. There will be a fundamental rethinking of how data is generated, processed, and consumed with the integration of more contextual, conversational elements, directly triggering workflows from a conversation or an image. A lot of existing digital workflows are bound to be replaced by the AI solutions, setting pace to the digital transformation efforts.
AI-powered design tools will become widespread, aiding business teams in expressing their ideas effectively. Generative AI will emerge as a solution to collaboration challenges, bridging the gap between business and IT teams, enabling visualization, prototyping, and improved communication. However, developers using AI models for generating code, will struggle in refining the intent or prompt to get to the appropriate logic or code. These AI models will have to evolve more to support iterative development as it will become an indispensable assistant for professional developers.
More and more open source LLMs & AI models will emerge for specific use cases, large organizations will tend to create their own AI models from the foundational models to protect copyrights, secure and ethical use of AI. These custom AI models bring better visibility to the developers and their ecosystem on how these models are trained and developed, so as to gain more trust in how these AI solutions are put to use.
The enduring impact of the pandemic will continue to shape the way software is created and foster innovation. Despite a decade of existence, the adoption of applications developed by business teams aided by low code platforms, still faces challenges such as IT acceptance, security, scalability, and licensing models. Low-code platforms will embrace LLMs and other AI models in creating apps that are enterprise-deployable, specially crafted by professional developers, eliminating the risks associated with IT deployment.
The future of AI-powered application development holds the promise of heightened automation, seamless contextual integration, and transformative user experiences.