This has led financial services companies to make investments in artificial intelligence (AI) and machine learning (ML) to improve the omnichannel experience. Despite these efforts, there is still significant room for improvement. Disparate technologies and intelligence layers that don't talk to each other lead to a disconnected customer experience. Companies can take a different approach by building a customer experience data hub that unifies information about customers and then applies AI and ML to support an array of customer-facing applications. This central hub enables companies to anticipate the motivations for incoming customer contacts across all channels and proactively deliver the help they need.

Organizations are working relentlessly to make the customer experience at every touchpoint of a transaction as seamless as possible. And the conversation around AI has intrinsically correlated itself with intuitive, human-based customer experiences. The need for identifying and solving customer challenges intuitively and objectively while having access to all possible information pertaining to said needs is of utmost importance in the present economic climate. Pankaj Kulshreshtha, CEO at Scienaptic AI believes that financial institutions today may be ahead in incorporating the latest technology but there is a gap in how this technology translates into customer experience. Their systems of records (Bureau, databases, core, alt data) need to connect with systems of engagement (CRM, LOS, digital apps) and all of this needs to be powered by systems of intelligence i.e. decisioning systems for every touch point powered by AI. "We hear of instances where an individual with $700,000 of petty cash sitting in his bank account is being offered a $500 credit line. These are instances where despite the seeming intelligence of financial systems, they don't seem intuitive enough."

People have always grouped themselves based on the mutual enjoyment of certain activities and hobbies. Look hard enough and you'll find a community centered around just about anything. Specific games or sports, music genres, automobiles, travel -- the list is endless. As technology advances, we find a similar dynamic unfolding online. Online communities across social media have now become prevalent.

The time is now to build a more gender inclusive Artificial Intelligence (AI) ecosystem in Europe – but how can that be best actualised in practice and at scale? I am delighted to be participating in EU AI Week and especially #breakthebias: Women in AI event Hear tomorrow from Nicole Foster Director of Amazon Web Services (AWS) Global AI ML together with policymakers, business leaders, the European AI #research community and #startups to discuss the policies and actions needed to build a more diverse and inclusive AI sector. I am delighted to provide closing comments too. This is a fantastic'dialogue for action' opportunity – with speakers from OECD - OCDE European Commission European Innovation Council and SMEs Executive Agency (EISMEA) French Government Belgian Federal Government and Women in AI. Above all be inspired by the women leading AI start-ups in Europe in the driving seat to develop the #future of this technology, making everything about the way we live and work better!

How conversational AI has evolved, and why it is rapidly redefining how customers interact with businesses. Imagine if customers could talk to a business whenever they need something, not just during working hours, not just when a human is available, and not just through protracted processes. People have conversations in person or via text, phone, video chats--the obstacles have never been lower. But when it comes to interacting with a business, points of friction are more numerous, with customers often forced to peruse bespoke websites and apps, deal with phone trees, wait for email responses, or accommodate the limited availability of human staff during business hours. That needn't be the case: it's time for organizations to reevaluate the role of conversational AI in customer experiences.

John Halamka, M.D., president, Mayo Clinic Platform, and Paul Cerrato, senior research analyst and communications specialist, Mayo Clinic Platform, wrote this article. The FDA's approach to software as a medical device (SaMD) has been evolving. In 2018, IDx-DR, a software system used to improve screening for retinopathy, a common complication of diabetes that affects the eye, became the first AI-based medical device to receive US Food and Drug Administration clearance to "detect greater than a mild level of … diabetic retinopathy in adults who have diabetes." To arrive at that decision, the agency not only reviewed data to establish its safety, it also took into account prospective studies, an essential form of evidence that clinicians look for when trying to decide if a device or product is worth using. The software was the first medical device approved by the FDA that does not require the services of a specialist to interpret the results, making it a useful tool for health care providers who may not normally be involved in eye care. The FDA clearance emphasized the fact that IDx-DR is a screening tool not a diagnostic tool, stating that patients with positive results should be referred to an eye care professional.

The global life insurance industry has seen significant changes over the past decade. Developing economies--predominantly emerging markets in Asia that were formerly small contributors--have become global growth drivers and now account for more than half of global premium growth (Exhibit 1) and 84 percent of individual annuities growth (Exhibit 2). The availability of data has skyrocketed, and insurers have made progress in advanced analytics and artificial intelligence. Digital and mobile advances have raised the bar on transparency and service quality: customers can now file claims and access agents, insurance quotes, and policy information with a few taps on a screen. The past decade has also introduced new challenges. Life insurers have not benefitted from the bull market (Exhibit 3). Global penetration fell to 3 percent, and premium growth within most developed markets, hovering just below 2 percent per year, struggled to match GDP.

Over the years, the influence of artificial intelligence (AI) has spread to almost every aspect of the travel and the hospitality industry. Thirty percent of hospitality businesses use AI to augment at least one of their primary sales processes, and most customer personalisation is done using AI. The proliferation of AI in the travel and hospitality industry can be credited to the humongous amount of data being generated today. AI helps analyse data from obvious sources, brings value in assimilating patterns in image, voice, video, and text, and turns it into meaningful and actionable insights for decision making. Trends, outliers, and patterns are figured out using machine learning-based algorithms that help in guiding a travel or hospitality company to make informed decisions.

Now, let me qualify that a bit better. Humans are rubbish at the majority of work we are asked to do. I for one, am absolutely terrible at typing (I've already hit backspace more than any other key). I'm not saying anything we don't already know, in that most humans are not good at manual, repetitive, rule-based tasks. We also need breaks, and sleep… in my case a lot of sleep.

Stardog really means it when they say you can act as if the walls of the silos are not there. Debuting in version 7 is what they call Virtual Transparency, which appropriately abstracts away from the user, whether that's an application developer, a business analyst, data scientist, or anyone else who needs data, the unnecessary details of where the data is or how it's stored. Data consumers simple write a logical query, i.e. "get all purchases made in the last 30 days in the mid-atlantic, within the Electronics department for more than $100" and Stardog's platform handles the details of accessing the data where it resides and returning a single, coherent, and most important, complete result.