AI is continuing to make great strides. Industries as diverse as telecommunications, insurance, financial services and education are embracing new applications – and this growth is only expected to accelerate in 2019 despite challenges such as the shortage of data scientists. AI will not only continue to make inroads across sectors but it will become a pervasive technology because it enables companies to make better decisions and handle processes more effectively and efficiently. The companies that adopt the technology sooner rather than later, will be in the best position to leverage AI for competitive advantage in the coming year and beyond.
With all the benefits that AI offers, it’s no wonder that it’s taking off. From machine learning, which automates routine tasks; to chatbots, which simulate human communication; deep learning, which is fast resembling the thinking of humans; and predictive analytics, which uses historical data to predict future outcomes – the benefits of AI are very attractive.
AI is already having a major impact on the way business is being conducted today. One implication is that BI, as we know it, will soon disappear. The term, Business Intelligence, which has been around since 1958 (long before it could be traced as a search term), will no longer be a viable approach. In 2019 the term will morph instead into Business Insights, marked by a reduced focus on dashboards and reports, and instead an increased focus on outcome-driven, value-based analytics. This will be driven by the emphasis on data, as well as the ability of AI-enabled apps to capture as well as predict outcomes based on this data.
While there are two main thrusts in AI today: pragmatic AI, which has immediate applications today, and pure or open AI, which is focused on simulating humans, in the coming year and foreseeable future, the focus will be on pragmatic AI. Companies are using these smart apps to solve actual business and real-world problems, ranging from automatically detecting parasites in blood smears, helping security personnel at airports improve safety by avoiding false alarms, and enabling financial services firms to provide personalized customer advice through chatbots to increase loyalty and satisfaction.
Challenges to growth
Even in a year of growth, there are several challenges to AI adoption in the year ahead:
- Data is needed to drive AI adoption. In order to automate processes, predict outcomes or communicate with humans, AI requires a huge quantity of data. The more data you can provide, the better – and the more accurate the outcome will be. Also, the data needs to be in good shape for the predictions and results to be valid. With data trapped in different silos, many organizations don’t know what data they have, where it is, or how to clean it up. Getting their data houses in order will be a first step to AI adoption for many companies in the new year.
- The shortage of data scientists will not abate. According to LinkedIn’s Workforce Report for August 2018, there is a shortage of 151,717 people with data science skills, and IBM estimates that by 2020, the number of jobs for U.S. data professionals will increase to around 2.7 million. While there are just not enough qualified data scientists to go around, one thing is certain: the solution does not lie in using citizen app developers to create algorithms with pre-defined, as-a-service apps. Creating algorithms, teaching AI programs – as well as providing the constant refinement and training that is needed – is a complex, specialized process that requires the skills of qualified data scientists.
- Heavy-duty computing power is required. The number of simultaneous calculations needed to create algorithms, as well as the cost and complexity of the advanced GPU servers needed to process them, makes it difficult for most companies to develop AI programs in-house. However, companies will increasingly turn to service providers to provide the specialized servers and skills needed to jumpstart their AI initiatives.
Growth areas ahead
Despite the road bumps that these challenges present, AI will be moving full speed ahead. Here are two new areas of growth we anticipate in 2019
- Data will move to the edge. The challenge of having good, sound data to fuel AI apps will give rise to a new approach to how data is captured, stored, curated and delivered. The focus will be on ensuring data is clean at the edge –where it enters (not in the back-end system). To turn edge data into insight for real-time action, it must be processed close to its source to avoid the latency, bandwidth and cost issues of sending data to a cloud-based data center. There will be huge market opportunities for companies that build tools to help enter and validate data at the edge.
- 2019 will be the year of computer vision. Another area of growth will be in virtual reality (VR), augmented reality, (AR) image detection and facial recognition. The use of AI to find patterns and insights from images and video will become as popular as data analytics. AI will involve not only image detection, but also movement and activity, enabling organizations to predict certain behaviors, for example, if a fight is about to erupt in a crowded group of people.
AI has established strong footholds across industries and applications and proven its value in improving the customer experience, offloading repetitive manual processes, and predicting future outcomes, among other activities. In 2019, organizations will continue to make progress in overcoming challenges and embrace AI at a faster clip as they reach for the brass ring of business insight and agility.