In this era, Application development and AI are two sides of the same coin. It's not possible to differentiate them, as AI is in demand every day, and developers are continually upgrading themselves to keep up with the pace. However, despite the buzz, it is not possible for AI to replace the job of software developers. Moreover, it will increase dependency and generate a higher level of demand in the market. However, it is evident that this time the pattern of demand will be different from that of the last decade or so.
Artificial Intelligence & Trend
The next phase of software development will involve the empowerment of more advanced tools, which will not only decrease developers reliance but also heighten the demand. This transition encompasses:
- Low-code/no-code platforms
- Automation in software testing
- Automation in code review
- Automation in Infrastructure-as-code
The dependence on testers at every stage of product development will gradually shift towards automation, albeit this transformation will require some time. Concurrently, developer involvement will remain necessary to design such applications because each product or service involves distinct business integration and execution requirements. Platforms like low-code/no-code are experiencing increased demand, with many enterprises having already invested or planning to invest in such solutions. These applications significantly reduce dependency and streamline the customer onboarding process.
A Unified Platform for Industries
Industries operate based on their geographical locations, meaning that as their locations change, be it in manufacturing or services, they must adjust their processes in accordance with the rules and regulations specific to that area. Therefore, it is not feasible to meet diverse requirements with a single solution. In such cases, applications must be integrated with various APIs, and this is where the concept of custom applications becomes essential. These industries include:
- Financial services
- Healthcare/pharmaceuticals
- Manufacturers
- Retail/e-commerce
The adaptation of AI
Enterprises hinges on how effectively they implement it. While everyone recognizes the power and significance of AI, the process of incorporating it remains a formidable challenge. Many enterprises have been operating for decades, resulting in a substantial codebase that has grown in tandem with their business expansion. A critical concern for these businesses is how to transition smoothly and achieve scalability. Applications designed three decades ago with only ten modules may now have expanded to include a thousand modules, yet the programming stack remains the same. Consequently, bridging the gap between this awareness and practical implementation poses a formidable challenge for businesses. Many enterprises are strategizing to establish separate units on top of their existing solutions in order to gain a competitive edge.
Challenges - while expanding AI endeavors
Barriers:- How to manage AI Risks
- Implementing AI
- ROI for Business
- Managing Data
- Establishing a Layer of AI Culture
- Selecting the correct A.I Model
- Identifying Use Cases with Higher Returns
Fostering an AI culture:
Prior to embarking on AI initiatives, enterprises must carefully strategize the integration of AI into their environment. There are numerous myths and apprehensions surrounding AI, and it is essential to address these concerns. Our recommendation is that, before implementation, a suitable culture needs to be established. Enterprises must also develop a plan for change management, as introducing AI-driven transformations is crucial. While there is no doubt that AI can significantly enhance individual productivity and overall performance and satisfaction, effective communication plays a pivotal role in achieving this outcome.
AI and Adaptability
Once developers comprehend AI and acquire proficiency in its effective techniques, their productivity will increase in various aspects:
- Application documentation
- Code generation
- Code refactoring
- Complexity reduction
Developer expertise required at:
- Thoroughly inspecting code to identify bugs and errors.
- Grasping the intricacies of complexity and offering effective solutions.
- Comprehending project requirements and executing implementations accordingly.
Conclusion
Changing a decade-old codebase that still relies on the same technical stack is a challenging endeavor. This challenge becomes more pronounced when a business is heavily dependent on this codebase, and the product it supports generates a substantial ROI. Developers are still required to maintain this legacy codebase, and it's not feasible for enterprises to make an overnight transition to replace it with AI. However, developers must also grasp and learn the effective techniques of AI and incorporate them into their daily work. Transforming the status quo overnight is not a simple task, but it is imperative to be prepared from the outset.