The IT industry has been trying to automate the creation of software for decades, and in several areas has arguably achieved it. But it tends to be done using tools that replicate the deterministic way people create code, for example UI generation from hand-written pages.
AI also offers a different conceptual approach to creating software, using algorithms and training data to determine the most accurate and efficient logic. This transforms the software development lifecycle, and redefines the essence of activities like debugging. For example, to resolve a problem, you may no longer inspect the code, but the training data. This is particularly relevant when developing AI software – especially using an AI platform.
AI is having as big an impact on IT operations, especially with the advent of DevOps – a deceptively simple term often used with ambiguity and vagueness. How you perform and manage IT operations today is as much about managing data as software or hardware.
Finally, project management has not been untouched by AI, but so far the impact lags other parts of the IT function. Tools are smarter, but most project management tasks are still done by people, with AI adding convenience. For example version control reporting, semi-automated schedule creation, team collaboration & task estimation.
However, some changes are appearing, with AI being used for project management tasks only people could do before. One interesting example is extracting and assessing project risks from all the available information about a project, in the context of many past projects.
Some areas of IT Development where AI, ML & Big Data can be applied include:
Some areas of IT Operations where AI, ML & Big Data can be applied include:
Some areas of Project Management where AI, ML & Big Data can be applied include: