The fact that increasingly complex automation is applied to problems that were solved entirely by humans opens the door to great opportunities but also to fallouts and shortcomings. According to consulting firm Accenture, American companies are expected to invest 35 trillion dollars in cognitive technologies before 2035, and that does not take into account other big players, such us Europe, China or Japan. Governments, such the French, are recognizing the importance of AI for the economy and the society. In Spain, the Ministry of Digital Agenda (Minetad) has created a group of experts that is working on a White Paper on AI.

Incremental steps in exploring artificial intelligence now can position organizations to pounce on a powerful array of machine-powered capabilities in the future. In March 2016, the American Association for Artificial Intelligence and I asked 193 AI researchers how long it would be until we achieve artificial superintelligence (ASI), defined as an intellect that is smarter than the best human in practically every field. Of the 80 respondents, 67.5 percent said it could take a quarter century or more, and 25 percent said it would likely never happen. Given the sheer number of "AI is coming to take your job" articles appearing across media, these survey findings may come as a surprise to some. Yet they are grounded in certain realities.

Artificial intelligence has been the domain of science fiction for decades -- think HAL, the computer in "2001: A Space Odyssey" -- but as many people know, it's actually established well-developed and growing roots in modern-day life. Amazon's Echo, Netflix's recommendation engines, Facebook's facial recognition technology, auto-braking on cars, it's all based on the ability to analyze massive amounts of data in near real-time and being able to mimic human behavior based on the results. AI and its various subsegments -- like machine learning and deep learning -- are also reaching deep into the enterprise, helping to automate many of the tasks that now are done manually, creating greater efficiencies, reducing errors and offering valuable new insights into the massive amounts of data being generated. In a dynamic and fast-changing market like manufacturing, systems that can learn and adapt on their own will be crucial in driving the next-generation flexible environments. According to Accenture, 85 percent of executives plan to invest in AI technologies over the next three years.

Voice assistants help people feel organized (50% agree), informed (45%), happy (37%), smart (35%), confident (31%), and free (30%). Yet, cracks begin to show when comparing satisfaction across devices. Voice assistants on smartphones have the lowest consumer satisfaction rate (38% very satisfied). These users expressed frustration with an apparent lack of understanding, reliability, and accuracy--and when compared directly to that of a standalone speaker, the differences are significant. Maturity is a factor here, as smartphones were among the earliest iterations of voice assistants years ago when the technology was still in its infancy.

Last year, a study overseen by management consultancy firm McKinsey revealed that as many as 800 million jobs could be taken by robots by 2030. Our mechanical peers are already outpacing us in certain areas, particularly anything that involves repetition and precision. But there must be some things we humans are better at – like art, for instance. This October, auctioneers at Christie's will be selling a portrait of Edmond De Belamy, a sturdy-looking man from the 18th century pictured from the waist up. He is dressed in a black coat with a white collar and wears a slightly gormless expression. To be honest with you, it is no Rembrandt.

The potential of artificial intelligence to transform the workplace is no longer the stuff of science fiction. It's a today reality that companies--and especially their executive teams and Human Resources (HR) leaders--are meeting head-on. Gone are the days of installing technology systems that served as fixed tools over a fixed lifecycle.iStock Dynamic technology innovations including machine learning and artificial intelligence are already changing the way companies operate, and redefining the very concept of work in the process. And while major technology breakthroughs that fundamentally reshape the workplace have a history of driving net job gains and improved employee satisfaction over the long term, the path is often complicated by short-term concerns or wake-me-when-it's-over inertia.

With IT infrastructure complexity at an all-time high, most enterprises fear that their IT services can't keep pace with their business requirements. Nearly three-fourths of them cite infrastructure limitations as a key hurdle in their digital transformation, according to the consulting and research firm Everest Group. Acknowledging that efforts to align the services provided by IT with business goals are far from novel, the consulting firm points to the marriage of artificial intelligence, analytics and infrastructure management as a potential solution to this perennial challenge. Everest's analysts term the combination of these three technologies "aware automation," noting that by underpinning automated systems with AI and analytics, IT infrastructures will be able to automatically adapt and reconfigure their services in response to user demands. "The trinity of analytics, automation and AI can make the infrastructure run the way business needs it to, without requiring significant oversight or bandwidth," says Ashwin Venkatesan, the Everest Group's practice director.

Artificial intelligence (AI) is no longer just a futuristic notion--it's here now, and more and more businesses are using it to fuel efficiency, growth and innovation. How is that going to impact your career--and are you prepared to partner with the machines? Reimagining business We believe that AI will not replace human workers, but will actually augment their abilities by complementing their skills, collaborating on complex tasks and freeing them from routine or repetitive tasks through automation. When we reviewed what 1,200 businesses around the world are doing with AI, we found that the leaders in this group are, in fact, investing in this kind of human and machine collaboration to grow faster, earn more and hire more people than their competitors. They are moving toward the idea of highly flexible teams that partner humans with AI solutions as they bring voice recognition, machine learning, intelligent robotics and other kinds of AI into the organization.

For some senior-level financial executives, the term Robotic Process Automation (RPA) produces visions of a utopian finance function. For others though, the mere thought of RPA brings on a major case of agita. According to a recent study by HfS Research and KPMG LLP, 55 percent of North American enterprises are looking at new opportunities available with RPA systems. Gartner predicts that by 2020, smart machines will be a top five investment priority for more than 30 percent of CIOs. So, regardless of which camp you find yourself in, the reality is that RPA is here now and will play a significant role in the finance function of the future.

Accenture this week signaled its intention to apply computer vision and product recognition technologies more broadly by investing in Malong Technologies. Based in China, Malong Technologies has been applying computer vision technologies enabled by machine learning technologies to supply chains. But Mike Redding, managing director for Accenture Ventures, says Accenture plans to apply the artificial intelligence (AI) technologies developed by Malong Technologies to a broad range of use cases, including health care and transportation. In a recent trial for an Accenture client, the Malong AI technology achieved the same level of accuracy as human doctors in detecting cases of stroke from brain-scans, says Redding. Redding says Accenture decided to invest in Malong Technologies because the AI pioneer has been recognized by Google, Microsoft and Gartner as being the provider of one of the most advanced implementations of computer vision technologies based on AI.