Historically, the fintech industry has been among the earliest Artificial Intelligence adopters. As of today, AI is becoming the main driver of digital transformation in traditional finance and the golden standard for fintech services. In fact, according to a recently published report authorized by the World Economic Forum and conducted in partnership with the Cambridge Centre for Alternative Finance, by 2022, we can expect mass adoption of AI in the financial industry on a global scale. In other ways, legacy financial services will become obsolete in as little as two years. AI and Data analytics go hand in hand, and nascent technologies like Machine Learning, Neural Networks, and Natural Language Processing, continue to improve data-crunching capabilities for financial industry players.

At the end of 2019, it would have been a fairly common occurrence to stumble upon a tech article like this ZDNet one, that used statements such as, "AI jobs are on the upswing, as are the capabilities of AI systems." Fast forward about 6 months and this statement is still accurate in some ways, but highly conditional in others. As COVID-19 continues to act as a threat to the US and global economy, businesses are being forced to reevaluate not only their current and prospective AI projects, but also who they hire and who they retain to help execute these projects. Many business leaders will likely feel pressure to quickly finalize AI projects and bring them to market, but in the process they may succumb to one or more of the major pitfalls outlined below, rendering their results invalid and even potentially harmful to the public. Data should often be the starting point for your AI project, as it will come to represent the underlying fuel for your market offering (platform, solution, etc.) to thrive.

Widespread deployment of 5G brings with it the promise of a network of connectivity with greatly improved latency and downtime. To reach full 5G capabilities, telecom service providers would be required to reach enough density to monetize the network by installing 5,000 to 20,000 5G small cells in every major city within the next five to ten years. This brings the network much closer to mobile phones and every type of IoT sensor or device that needs connectivity. The explosive increase in bandwidth will open new possibilities, from remote surgeries guided from New York City to a small African village, to enabling new ways for humans and robots to work together on a factory floor. Full 5G coverage is an ambitious goal, requiring the biggest telecom players to invest $20 billion annually in the US.

It's an undeniable fact: the world now runs on data. The haves will succeed, and the have-nots are destined for failure. Specific to modern business, winners and losers are now being determined by the way organizations collect, analyze, and act on data – increasingly through automation. And much of the value of data today is being unleashed through models: predictive analytics, machine learning (ML) and the emerging world of artificial intelligence (AI). We've spent the last 20 years relentlessly pursuing digital transformation and building millions of applications based on programmed logic.

Manually processing mounds of documents is tedious work for employees and impedes organizational efficiency, scalability, customer responsiveness, and the ability of a business to compete in a digital world. To avoid being lost in the shuffle, a growing number of businesses, across all industries, are adopting automation solutions. With the right automation technologies in place, organizations – and thus their employees and customers – can benefit in many ways. They enjoy greater accuracy in processing data and new insights to improve and expand their offerings to customers. Organisations benefit from greater productivity so they can do more things more effectively – for example, a global insurance company realized higher business productivity, lower turnaround times and a more than 65% increase in accuracy for handwritten cursive recognition.