Did you realize that you, as a software developer, spend about 75% of your time searching through and querying to understand code, fix bugs and make necessary changes? With every change, applications grow increasingly complex, escalating the importance of software development productivity, which is attracting greater attention from the professional community. Whether a startup's leadership is concerned about how much the software development team costs and wants to promote efficiency to get more done with less, or a corporate engineering leader is "shaking out" their teams to improve output, questions about productivity inevitably arise. While some tools can help improve productivity by suggesting what code to write, even as the developer is writing code, software developers still have to use their brains to add new features, fix bugs, implement changes to meet regulatory requirements, address security needs and solve challenging engineering problems. But what if there was a tool that did some of the hardest thinking for you?

All the sessions from Transform 2021 are available on-demand now. As the IBM Watson experience shows, the path to AI success is fraught with challenges. Yet overall, it has been a very good year for AI and the companies developing it. So much so that Alphabet CEO Sundar Pichai, in a recent podcast recorded by BBC, says: "I view [AI] as a very profound enabling technology. If you think about fire or electricity or the internet, it is like that, but I think even more profound."

AI-enabled software development tools enable businesses to perform tasks with efficiency and accuracy. Software application contributes significantly to the routine activities across organizations. From searching a product over the internet to sending emails to clients and colleagues, the utilization of software in business has accelerated. Though the software is a compelling entity, its development is a tough task. It is a complex process that requires ideation, product definition, strategic designing, coding, quality assessment, and coding. Additionally, if any step in the software development goes wrong, the entire process needs to be started again.

Companies involved in software development, either for external customers or for their own internal needs, face a variety of challenges. A shortage of skilled developers is impeding efforts to create quality software. Development projects often go awry. Many are late, go over budget, or are simply cancelled before they come to fruition. And despite the best efforts of programmers and other pros, finished applications can be hampered by bugs.

Artificial intelligence is making the process of designing, developing, and deploying software faster, better, and cheaper. It's not that programmers are being replaced by robots--rather, AI-powered tools are making project managers, business analysts, software coders, and testers more productive and more effective, enabling them to produce higher-quality software faster at lower cost. AI may become a key factor in meeting rising demand for custom software. Developing and deploying custom software is a critical element of how many companies innovate,5 with top-performing organizations developing many of their most important software solutions in-house.6 And the market for custom software development services is large: around US$47 billion in 2018 and climbing.7

As per our Data Science Skills Study 2018, Python is the most used language by data scientists, with 44% of respondents using it for application building and scientific & numeric computing. One of the main reasons for Python's soaring popularity is that it has one of the largest programming communities in the world and offers a number of libraries which a data scientist can use to analyse large amounts of data. In terms of data visualization, Python offers a number of libraries like Pandas or Matplotlib. The study further revealed that 41% data scientists prefer Pandas over other libraries. As Python inches towards supremacy, a lot of emphasis is now being laid on how to improve the platforms that run Python and the use of its machine learning libraries.