As more companies explore the viability of adding artificial intelligence into their business processes, a new resource from CompTIA, the nonprofit association for the information technology (IT) industry and workforce, offers guidance, answers questions and provides information to help in the decision-making process. "Artificial Intelligence in Business: Top Considerations Before Implementing AI" was produced by the CompTIA Artificial Intelligence Advisory Council, a group of thought leaders and innovators committed to accelerating the adoption of AI and machine learning technologies. "AI is already prevalent in many business processes and applications used daily, and there are almost limitless other opportunities where it can be utilized," said Annette Taber, senior vice president for industry outreach and relations at CompTIA. "However, AI processes are complex. The key to a successful deployment is asking the right questions and understanding what's involved before making any investments."

The key to successful deployment is asking the right questions before making any investments. "AI is already prevalent in many business processes and applications used daily, and there are almost limitless other opportunities where it can be utilized," said Annette Taber, senior vice president for industry outreach and relations at CompTIA. "However, AI processes are complex. The key to a successful deployment is asking the right questions and understanding what's involved before making any investments." The guide identifies more than two dozen factors that should be thoroughly considered and addressed by business decision makers and AI practitioners.

AI is finding its way into software of all kinds, from the voice assistants in your home to the algorithms in life-saving healthcare applications. But the broader AI use becomes, the higher the risk of "AI gone wrong" -- unintended decisions made by an algorithm. In this episode of What It Means, Vice President and Principal Analyst Diego Lo Giudice discusses the expansion of AI and the increased need for checks and balances. But testing AI is not as simple as testing traditional software. As Lo Giudice puts it, how do you test something when you don't know the desired or anticipated outcome?

The benefits of artificial intelligence are flourishing across several industries and finding its way to all kinds of technical aspects. From education to manufacturing the technology has served every sector for better while introducing various innovations across its verticals. But, as experts fear, the broader AI use becomes, the higher the risk of "AI gone wrong" which means the algorithms can evolve on their own to make unintended decisions. In a recent blog for Forrester, Vice President and Principal Analyst Diego Lo Giudice discussed the expansion of artificial intelligence and the increased need for checks and balances. However, testing AI is not as simple as testing traditional software and as Lo Giudice puts it, how can one test something when they don't know the desired or anticipated outcome.

In March of 2018, an Uber self-driving car killed for the first time: It did not recognize a pedestrian crossing the road. COMPAS, a machine-learning-based computer software system assisting judges in 12 courts in the US, was found by ProPublica to have harmful bias: It was discriminating between black and white people, suggesting to judges that the former were twice as likely to commit another crime than the latter and recommending longer detention periods for them before trial. I could continue with more examples of how AI can become harmful. Enterprises are infusing their enterprise applications with AI technology and building new AI-based digital experiences to transform business and accelerate their digital transformation programs. But there is a chance that all these positives about AI could end, especially if we continue to see examples like this of delivering poor-quality, untested AI or AI that's not properly tested for businesses and consumers.

Thanks to the rise of artificial intelligence (AI), the nature of DevOps is about to change as developers spend less time writing code in favor of training models based on various types of algorithms to accomplish a task. Dan Scholnick, general partner with Trinity Ventures, an early investor in companies such as New Relic and Docker Inc., says developers in 2018 will absolutely be required to gain mastery over how to implement machine and deep learning algorithms to stay relevant. Once a model based on those algorithms is incorporated into an application, the challenge for the DevOps team will be implementing the processes needed to make sure the AI created based on those models remains tuned and optimized to the business process it's supposed to drive. Now that the price of cloud services based on graphical processor units (GPUs) has dropped and open source tools for building AI models such as TensorFlow are readily available, Scholnick says the cost of infusing AI models into applications has dropped considerably. Before the end the end of the year, most users of business-to-business (B2B) applications will expect that application to exhibit many of same natural language and speech recognition capabilities that already are becoming commonplace in many consumer applications, he says.