Financial services and technology vendors make for uneasy bedfellows. While tech has formed banking's bedrock since the Big Bang deregulation of the 1980s, in the last decade financial services (FS) organisations have seen the new "masters of the universe" steadily – almost stealthily – encroach on their patch. Established tech vendors and new start-ups have introduced a range of financial services from money transfer apps to mobile payments, crowdfunding to share trading and investments. These new services are perfectly suited to a generation who have grown up with smartphones and expect instant access to digital services, combined simplicity and a great user experience. While over the last few years there has been an exponential increase in the structured data that is collected and used, the inclusion of unstructured data sets, pictures, images and videos along with structured data has been increasingly important in driving both strategic and operational business decisions.

Artificial intelligence is very prevalent in movies and science fiction, from sentient beings that are able to walk, and talk, and live with humans like the characters from Westworld, Star Wars, or Star Trek. In reality, however, we are a far way away from the dream of sentient machines that we see and read about in science fiction. So much of today's AI systems are doing much more mundane things that aren't getting the attention or interest of the press and media. However, interest and investment in AI remains strong, and even if AI is unable to live up to the fantasies of science fiction, vendors are riding the hype wave of AI and promising capabilities that AI systems might not be able to deliver. While vendors are doing their best to deliver these capabilities, the challenge is that adopters and end users sometimes themselves get caught up in the hype as well.

Looking to accelerate the ROI on IT's digital transformation projects, ServiceNow has delivered an offering that combines an AI-based recommendation engine with a collection of support tools and technical support. When ServiceNow started developing its new software, called ServiceNow Impact, two years ago, it interviewed 500 of its customers along with 200 software buyers. What the company learned from those conversations was the majority of organizations lacked the ability to map a strategic vision for transformation and then turn that map into an operating model. IDC estimated in a report released this month that users have spent some $3 trillion on digital transformation projects in the past three years. However, less than half of those companies said those projects delivered the expected results.

Among technology firms that have plans to invest in artificial intelligence (AI), a third expect to invest at least a million dollars over the next two years. And among these companies, almost all (87 percent) expect industry-wide funding for AI to increase at least moderately, if not significantly, throughout 2022. These are the findings of a new report published by analyst firm Gartner, based on a poll of 268 respondents from across the globe, which asserts that AI has the second-highest mean funding allocation compared with other emerging technologies. Among those investing in AI, computer vision is the most popular technology (an average of $679,000 over two years), Gartner further said. And very few organizations reported investing less than $250,000 in AI.

With great power, the saying goes, comes great responsibility. As artificial intelligence (AI) technology becomes more powerful, many groups are taking an interest in ensuring its responsible use. The questions that surround AI ethics can be difficult, and the operational aspects of addressing AI ethics are complex. Fortunately, these questions are already driving debate and action in the public and commercial sectors. Organizations using AI-based applications should take note.

While artificial intelligence (AI) powered technologies are now commonly appearing in many digital services we interact with on a daily basis, an often neglected truth is that few companies are actually building the underlying AI technology. A good example of this is facial recognition technology, which is exceptionally complex to build and requires millions upon millions of facial images to train the machine learning models. Consider all of the facial recognition based authentication and verification components of all the different services you use. Each service did not reinvent the wheel when making facial recognition available in their service; instead, they integrated with an AI technology provider. An obvious case of this is iOS services that have integrated FaceID, for example, to quickly log into your bank account.

Artificial intelligence (AI) is thrown into conversations about the future of business tech with increasing frequency. Many enterprises now have programmers beavering away on bespoke algorithms to automate tasks or services, which they hope will give them a competitive advantage. These algorithms are trained on vast data sets and eventually learn how to correctly identify common patterns without human intervention. They take time to design, and they don't come cheap. But that doesn't mean AI is purely for the big beasts of the business world.

Artificial intelligence (AI) holds tremendous potential for governments, especially machine learning technology, which can help discover patterns and anomalies and make predictions. There are five vectors of progress that can make it easier, faster, and cheaper to deploy machine learning and bring the technology into the mainstream in the public sector. As the barriers continue to fall, chief data officers (CDOs) have increasing opportunities to begin exploring applications of this transformative technology. Machine learning is one of the most powerful and versatile information technologies available today.1 But most organizations, even in the private sector, have not begun to use its potential. One recent survey of 3,100 executives from small, medium, and large companies across 17 countries found that fewer than 10 percent of companies were investing in machine learning.2

No technology concept is going to be more discussed, dissected or debated in healthcare this year than artificial intelligence (AI). In the world of radiology and imaging, AI has the potential to dramatically impact productivity, precision and outcomes. In fact, one of the most prominent use cases for AI applications in healthcare may be in imaging. However, several factors will impact the mainstream adoption of AI in 2018. AI has captured the imagination of the healthcare sector in the transition from a fee-for-service era to a value-based care era.

I'm sure by this point in the year, you've heard these two words more times than you can count, and have started to think about a world of self-driving cars and robot home assistants. But what does this mean for enterprises? That's a question our team at Dell EMC hears from customers on a daily basis. These customers want to understand how they can leverage this technology that promises so much, but do so in a way that doesn't require a steep learning curve that will grind their business activities to a halt. This is where the Ready Solutions from Dell EMC come in.