Voice shopping accounted for $2bn in spend last year but this is expected to jump to $40bn in 2022, as the number of smart speakers in homes more than quadruples within the same timeframe. The number of homes using smart speakers will rise from 13 per cent today to 55 per cent by 2022, according to management consulting firm OC&C Strategy Consultants. Despite this, only 39 per cent of consumers trust the'personalised' product selection of smart speakers. The three most commonly shopped categories are groceries, entertainment and electronics – representing 20 per cent, 19 per cent, and 17 per cent of voice purchases respectively. This is followed by clothing at eight per cent.

A survey of early adopters helps correct some common misconceptions about artificial intelligence. Artificial intelligence (AI) is one of the most frequently discussed topics in business today, but even more than most new technologies, its promise is sometimes obscured by a set of lingering myths--particularly among those whose exposure to the technology has been limited. Professionals with first-hand experience have a different perspective, according to the 2017 Deloitte State of Cognitive Survey, which is based on interviews with 250 business executives who have already begun adopting and using AI and cognitive technologies. The responses of these early adopters shed considerable light on the current state of cognitive technology in organizations. Along the way, they help dispel five of the most persistent myths.

Bottom line: Machine learning algorithms, applications, and platforms are helping manufacturers find new business models, fine-tune product quality, and optimize manufacturing operations to the shop floor level. Manufacturers care most about finding new ways to grow, excel at product quality while still being able to take on short lead-time production runs from customers. New business models often bring the paradox of new product lines that strain existing ERP, CRM and PLM systems by the need always to improve time-to-customer performance. New products are proliferating in manufacturing today, and delivery windows are tightening. Manufacturers are turning to machine learning to improve the end-to-end performance of their operations and find a performance-based solution to this paradox.

Rapid advances in artificial intelligence (AI) and other technologies are accelerating the creation of intelligent enterprises and enabling companies to integrate themselves into people's lives, according to Accenture Technology Vision 2018, the annual technology report from Accenture that predicts key technology trends likely to disrupt business over the next three years. However, capitalizing on growth opportunities while also having a positive impact on society requires a new era of leadership that prioritizes trust and greater responsibility. This year's report, "Intelligent Enterprise Unleashed: Redefine Your Company Based on the Company You Keep," highlights how rapid advancements in technologies -- including artificial intelligence (AI), advanced analytics and the cloud -- are enabling companies to not just create innovative products and services, but change the way people work and live. This, in turn, is changing companies' relationships with their customers, employeesand business partners. As part of the Technology Vision, Accenture surveyed more than 6,300 business and IT executives worldwide.

Machine learning (ML) is in for a big year in 2018, if new research from consulting firm Deloitte is correct. One of the major predictions of the firm's 2018 Technology, Media & Telecommunications (TMT) report released this week is that enterprises will likely double their use of ML technology by the end of 2018. "We have reached the tipping point where adoption of machine learning in the enterprise is poised to accelerate, and will drive improved business operations, better decision making and provide enhanced or entirely new products and services," said Paul Sallomi, vice chairman of Deloitte. ML, a core element of artificial intelligence, will progress "at a phenomenal pace," according to the study. "As impressive as it is today, in 50 years' time the ML abilities of 2018 will be considered baby steps in the history of this technology," the report said.

So far we've explored how AI can be applied to improve business outcomes and to increase strategic agility and growth, now let's examine a third feature. A recent Accenture Research report on AI notes a distinctive value of these technologies: Their role in helping propel innovation as companies apply AI and spread it into the economy. In a recent interview, Accenture Chief Technology & Innovation Officer Paul Daugherty described how the research study looked at different countries' "national absorptive capacity," or how well a country is prepared to absorb and spread innovation. Said Daugherty, "The kinds of things you need to look at are--what's the structure of their economy, what kinds of industry do they have, what's their capacity of research and development, what kind of ecosystems do they have around innovation…you look at the underlying economic data, dig a little deeper and say...how can AI technology change productivity and how will that drive underlying economic improvements." Using this approach, we found, for example, that Japan is expected to benefit dramatically from AI in part because of its capacity for innovation.

Rapid advances in artificial intelligence (AI) and other technological innovations will likely disrupt conventional business activities in Indonesia over the next three years, according to the latest report from business consulting firm Accenture.

There is no doubt that the need for companies to get serious about digitally transforming their business and production processes is critical to maintaining their competitive edge. The customer is driving the need to be able to get to market faster with products that are competitively priced with the features and capabilities they demand. Artificial intelligence (AI) is at the core of being able to realise success. The results of a Gartner global survey of over 3,000 CIO's confirms the importance of AI focused initiatives. Compared to a baseline established in 2015, this decision to implement AI is up 10%.

The majority of CFOs are increasing investment in their companies' efforts to spur change through new technology, according to a new survey. The survey of more than 300 CFOs and financial leaders by Grant Thornton and CFO Research found that 69 percent plan to increase their investment in technologies that speed business change. In addition, four out of 10 of the financial leaders polled plan an increase of more than 10 percent in the next 12 months. Competition is the main factor behind the spending, with 41 percent of the survey respondents saying their companies' digital-transformation investments are intended to help them overtake their competition through differentiation. "While investment strategies for digital transformation have traditionally been influenced by an organization's desire to improve operational performance and reduce costs, respondents have shown that future investment strategies will shift to more strategic opportunities – chief among these being improving the customer experience," said Srikant Sastry, national managing principal of advisory services at Grant Thornton, in a statement.

McKinsey & Company, a global management consulting firm that helps organizations make significant and lasting improvements to their organizational performance, estimates that AI can automate as much as 45 percent or more of any particular job, allowing workers to focus on higher level, mission-critical activities. Preez provides two examples of this already in use: 1. AI-driven natural language interfaces make interacting with applications much easier and speeds up many individual steps within a process. "For example, natural language processing can allow doctors to dictate clinical notes into a device, which then automatically populates appropriate forms, lab orders and prescriptions. Or it can summarize long blocks of text from medical journal articles or studies by identifying key concepts and phrases," Preez explains. The second example he mentions is the utilization of machine learning, which is a component of AI.