AI-based testing has the potential to help solve software quality issues, but it faces significant roadblocks on the way to widespread adoption. Automated testing uses software tools to automate the manual testing process. Testers can use traditional rules- or code-based scripts or AI -- which builds, initiates and runs testing models without human intervention. AI-powered tools such as Selenium IDE-compatible Katalon Studio, mabl and Functionize can free developers from mundane task repetition and monitor complex systems for vulnerabilities. However, a distrust of the inchoate technology hinders adoption rates, according to industry experts.

A headline that many think will dominate in 2022 is regulation and data governance. A shift is underway and will continue next year in how both government agencies and private organizations are approaching AI ethics, according to analyst firm Cognilytica. Until recently, enterprises in large numbers were acquiring and implementing various AI tools and platforms largely without considering the consequences of biased and unexplainable algorithms. But by the end of 2022, vendors will not be able to sell major AI systems to a large business or government agency without following specific guidelines relating to bias, transparency, explainability and data set collection. There's already a framework for ethical AI in the U.S. Department of Defense, noted Ronald Schmelzer, a Cognilytica analyst.

Artificial intelligence continues to gather steam in the enterprise. But it hasn't reached majority status among enterprises. Several roadblocks to more widespread AI adoption remain -- some linked to talent and data and some to perception– says a recent report from Cognilytica, a research firm. By 2025, according to "Global AI Adoption Trends & Forecast 2020," 40% will have deployed AI in some form, compared with 12% in 2020. These numbers indicate that AI in production is still tentative, and enterprises are incrementally making their way toward the technology only as they see immediate return.

Artificial intelligence (AI) has permeated enterprise operations to the point that it now determines an organization's success, including in the area of project management. In a report, Project Management Institute (PMI) examines how six AI technologies are affecting today's project managers and will affect project management operations in the future. PMI's AI Innovators: Cracking the Code on Project Performance (2019) found that in the next three years, project professionals expect overall AI usage to jump from 23% to 37% and the majority of respondents (81%) said their organizations are currently being affected by AI technologies. SEE: The ethical challenges of AI: A leader's guide (free PDF) (TechRepublic) "Project leaders are in the earliest stages of adopting AI to streamline--and improve--project work. AI technologies are already contributing to higher productivity and better quality," said Mark Broome, chief data officer at PMI. "For example, technology is decreasing the amount of time project managers need to spend on activities like monitoring progress and managing documentation--they can rely on AI for these more administrative tasks. The time saved can then be repurposed to more strategic and creative tasks and planning."

Kite, a software development tools startup, has devised a system to boost programmer productivity by using machine learning to predict the next string of code that Python programmers will write. Kite's Line-of-Code Completions feature uses advanced machine learning models to cut some of the mundane tasks that programmers perform to build applications, such as setting up build processes, searching for code snippets on Google, cutting and pasting boilerplate code from Stack Overflow, and repeatedly solving the same error messages. The system analyzes the entire source code base of open source projects on GitHub and applies that data to machine learning models trained to predict the next word or words of code as programmers write in real time. This smarter programming environment makes it possible for developers to focus on what's unique about their application. "From the time I learned coding on a Commodore 64, I always found it nuts that so many talented coders are put off through having to deal with frustrating syntax errors," said Torsten Volk, an analyst at Enterprise Management Associates (EMA) in Boulder, Colo. Kite isn't the only software development tools provider to offer code completion.

IBM is trying to woo and support new developers as the battle for skilled AI talent ramps up. AI is among the hottest technologies on the horizon, and sub-topics like machine learning have emerged as a must-have for many new applications. IBM and others want to empower developers with tools to simplify the creation of AI-powered apps. Oracle released the long-awaited Java 9, but what will this mean for developers? Uncover how Java 9's improvements aim to simplify the development process.