However much your IT operation is using artificial intelligence today, expect to be doing more with it in 2018. Even if you have never dabbled in AI projects, this may be the year talk turns into action, says David Schatsky, managing director at Deloitte. "The number of companies doing something with AI is on track to rise," he says. Check out his AI predictions for the coming year: Many of today's off-the-shelf applications and platforms that companies already routinely use incorporate AI. "But besides that, a growing number of companies are experimenting with machine learning or natural language processing to solve particular problems or help understand their data, or automate internal processes, or improve their own products and services," Schatsky says. See our related article, 8 emerging AI jobs for IT pros.

According to a Deloitte report entitled AI is helping to make better software, the rising demand for custom software is being met with a rise in the use of artificial intelligence (AI) tools during the design, development, and deployment stages. While we are not talking about AI writing lines of codes independently -- though this will inevitably become a reality in the future -- AI is taking on a more prominent role in assisting developer teams to produce cutting-edge software, particularly when faced with a shortage of talented software developers. The Consortium for IT Software Quality (CISQ) reported an estimated loss of US$319 billion in US organizations due to poor software quality in 2018. Moreover, many more are late or over budget, which eventually leads to cancelation and abandonment of projects, adding on to monetary and productivity loss. AI could help turn things around according to Deloitte, with automated development tools able to close the gap between a hike in demand for personalized software and a lack of talent to deliver the programs.

The Joint Artificial Intelligence Center (JAIC), a newly formed AI arm of the Department of Defense, plans to hire an AI ethicist to help address AI ethical concerns and guide the DoD in researching and deploying these technologies. Walmart recently created and filled a digital citizenship role to advise the retail corporation on issues associated with emerging technologies, data governance and privacy. Data and AI ethics are becoming increasingly important to organizations across many industries, as organizations look to get ahead of potential AI ethical concerns. "All organizations aspire to behave ethically and for their employees to behave ethically," said David Schatsky, managing director at Deloitte. "It's not that suddenly ethics is important and it wasn't before."

The global AI market is expected to grow from $21.46 billion in 2018 to $190.61 billion by 2025. On the other hand, however, the public cloud industry stood at $182.4 billion in 2018 and is projected to grow 17.5% in 2019 to total $214.3 Unlike AI, the cloud industry has already trodden the path from hype to broad adoption, and become a different beast altogether. With the cloud industry propelling this sort of growth, could the forecasts about AI adoption ultimately prove to be conservative? Or, perhaps it's time we realised that the AI and cloud computing industries are not mutually exclusive.

When IBM first debuted its Watson system on the game show Jeopardy! in 2011, it was like something out of science fiction. Here was a computer that could not only understand spoken questions, but answer them faster and more accurately than the best human players. Nobody had ever seen anything even remotely like it before. Today, less than a decade later, artificial intelligence has been transformed from the incredible to something approaching the mundane. Not only do we have capabilities that are similar to the original Watson system on our phones, we can access top-notch resources from a number of companies, often for free.

When IBM first debuted its Watson system on the game show Jeopardy! in 2011, it was like something out of science fiction. Here was a computer that could not only understand spoken questions, but answer them faster and more accurately than the best human players. Nobody had ever seen anything even remotely like it before. Today, less than a decade later, artificial intelligence has been transformed from the incredible to something approaching the mundane. Not only do we have capabilities that are similar to the original Watson system on our phones, we can access top-notch resources from a number of companies, often for free.

Your enterprise organization probably has set up data management and data analytics operations to get the most out of the data generated by your organization. Chances are you are also at least exploring or piloting machine learning as part of your data operations. But is your data telling you the whole story? Sometimes incorporating a new data stream from outside the organization can add a whole new level of insight and value to your analytics or machine learning efforts. A Gartner survey of 196 organizations released in February 2018 revealed that 46% of organizations are using external data sources.

If there's one big thing that might thwart companies' headlong rush to adopt artificial intelligence for their businesses, it's data. AI generally requires lots of data. But it needs to be the right kind of data, in very particular kinds of formats. And it often needs it to be "clean," including only the kind of information it needs and none of what it doesn't. "The biggest challenge most organizations face when they start thinking about AI is their data," said Paul Daugherty, the chief technology and innovation officer of consulting firm Accenture, in an interview earlier this month.

Indeed, one of the first synthetic data examples Schatsky encountered was for computer vision, technology that enables machines to recognize faces or identify objects in digital photos. Researchers today are building sophisticated computer vision features where the technology can follow an eye gaze or detect an emotion on someone's face. But gathering the amount of data needed -- and labeling it -- is laborious. "And, so, what researchers did is they took a 3D-digital model of a human face and then manipulated it," Schatsky said. They can generate as many permutations of facial expressions or eye positions as they want -- and they can do so "quickly and cheaply, compared to collecting a comparable number of images," he said.

However much your IT operation is using artificial intelligence today, expect to be doing more with it in 2018. Even if you have never dabbled in AI projects, this may be the year talk turns into action, says David Schatsky, managing director at Deloitte. "The number of companies doing something with AI is on track to rise," he says. Many of today's off-the-shelf applications and platforms that companies already routinely use incorporate AI. "But besides that, a growing number of companies are experimenting with machine learning or natural language processing to solve particular problems or help understand their data, or automate internal processes, or improve their own products and services," Schatsky says. See our related article, 8 emerging AI jobs for IT pros.