AI and software development mix well together
With the rise of ML, cloud based advanced data services and lately LLMs, AI solutions are rapidly offering flexible, powerful and affordable solutions and much wider solution space than ever before.
Many of us are thinking: Will AI* be just hype that fades away, or will everything be based on and replaced by AI?
From a perspective of professionals who have implemented multiple ML and GenAI solutions, and have longer background in software and data development, we see that software and AI will be intertwined in a very symbiotic way. Sustainable and maintainable production level AI solutions require modern software development, and how software is made, will be changed by AI.
*Although inspired by the capabilities, many of us engineers have a bit mixed feelings towards the term “AI”, as we don’t see that the actual level of artificial intelligence would have been reached by the current “prediction algorithms” that we call AI. We try to get over it.
AI capabilities are available for all companies like never before
AI solutions provide increasingly flexible solutions to more complex problems every day. They allow automation of tasks and processes that were previously very difficult to automate with sufficient results. Customer service chatbots, information summarisation, content production assistance and product description automation are just few examples of pragmatic use cases for AI. The amount of opportunities is vast and feels like a new renaissance. Moreover, many of these capabilities are out of lab environments, affordable and available on as open source or as common cloud/SaaS services.
Software is needed to implement process level AI solutions
Until humankind is a able to produce a true artificial general intelligence, software is needed to implement production-level automated AI solutions that integrate to processes. AI solutions are not magic. They require software engineering skills and practices to gain access to data, ensure their quality, reliability, security, and scalability. Software developers need to design, develop, test, deploy, and maintain AI solutions that can run on various platforms and environments with feasible cost. They also need to integrate AI solutions with existing software systems and processes, such as databases, APIs, web services, and cloud services.
AI changes how software is made
As one of the first processes, AI will change the process of software development. AI is not only a tool for software development, but also a paradigm shift. AI will help software developers increase automation level of some of the tasks that are tedious, repetitive, or error-prone, such as debugging, testing, documentation, and refactoring. AI can also help software developers learn from data, feedback, and best practices, and improve their skills and productivity. Moreover, AI can enable new ways of software development, such as collaborative coding, code synthesis, code reuse, and code evolution.
Balanced solutions require both AI and software
AI solutions are often computationally very intensive and may consume much more energy than traditional code, sometimes to a prohibitive extent. Thus, solutions in near future will likely be a mix of both to be environmentally and economically sustainable. Software developers need to be aware of the trade-offs between performance and efficiency when choosing AI solutions.
As a conclusion:
Software is not going to disappear. It’s going to be integral element of AI solutions. Solutions and architectures will be a mix of code and AI.
How software is made will be changed by AI - majority of modern developers already use some AI based tools, and the tools will develop rapidly
We can keep energy consumption and costs in control with optimal use of AI capabilities
Partner, Lead Architect & Developer
Managing Partner, Technology Strategist