At the end of each year, eWEEK posts observations from IT thought leaders about what they think we should all expect in the coming year--new products, innovative services, trends to look for, and so on. Here are some perspectives from a selection of thought leaders across the IT world. Inclusive engineering will begin to make its way into the mainstream to support diversity. In order to ensure diversity is baked into their AI plans, companies must also commit the time and resources to practice inclusive engineering. This includes, but certainly isn't limited to, doing whatever it takes to collect and use diverse datasets.

Founder and CEO of DotData, Ryohei Fujimaki, explains how automation can help the data science industry become more efficient. Of the many technologies that will shape how we work in the future, automation is one of the most hotly debated. Some look forward to the new avenues it will open up while others fear it will make their skills redundant. Dr Ryohei Fujimaki, founder and CEO of data science company DotData, believes that data scientists are among those that will benefit the most. Fujimaki's team at DotData is helping companies accelerate their data science process.

The notion of using data to predict future outcomes is far from new. Even highly technical products that performed "predictive analytics" analysis have already been available to enterprise organizations for many years. The notion of developing and deploying custom-built predictive solutions, however, have, for the most part, been the exclusive domain of Fortune 500 companies. The rarity of predictive analytics in the enterprise is mostly due to the technical complexity needed to create, train, and deploy the complex AI and Machine Learning (ML) models required to successfully develop predictive solutions. Over the past few years, the world of AI and ML development has seen rapid change.

Over the past few months, the COVID-19 virus has had a huge impact on the globe. As of April 28, according to the World Health Organization, there have been more than 2.8 million confirmed cases worldwide and nearly 198,000 confirmed deaths reported in more than 213 nations across the globe. The COVID-19 Pandemic is forcing governments and businesses into actions that are critical in the effort to minimize the rate at which the virus spreads. On March 19th, all residents in California, 40 million people, were asked to "shelter in place" and leave their homes only for basic necessities. Any bay area citizen who has lived through often nightmarish commutes can now travel corridors with ease that a month ago would have been congested with bumper to bumper traffic.

AI-FastStart was born as a direct response to a rapidly changing BI & Analytics world. AI/ML has become a critical technology investment but most organizations still suffer from scaling AI/ML practices. The program was designed around four core principles: The right platform, education, providing fast time-to-value, and to be easy to deploy and implement. We provide the best software, host it on the best possible platform, bundle the right depth and amount of education for unlimited users, and tailor services to enable an operational first use case in as little time as feasible. Whether your BI team has no experience with AI/ML, or are full experts, dotData AI-FastStart will help them become more proficient, more successful and will ultimately provide an exceptional predictive analytics foundation for your organization for years to come.

AI is having a profound impact on customer experience, revenue, operations, risk management and other business functions across multiple industries. When fully operationalized, AI and Machine Learning (ML) enable organizations to make data-driven decisions with unprecedented levels of speed, transparency, and accountability. This dramatically accelerates digital transformation initiatives delivering greater performance and a competitive edge to organizations. ML projects in data science labs tend to adopt black-box approaches that generate minimal actionable insights and result in a lack of accountability in the data-driven decision-making process. Today with the advent of AutoML 2.0 platforms, a white-box model approach is becoming increasingly important and possible.

The popularity of AI and ML have wide-reaching effects on your enterprise. Here are three important trends driven by AI to look out for next year. As the need for additional AI applications grows, businesses will need to invest in technologies that help them accelerate the data science process. However, implementing and optimizing machine learning models is only part of the data science challenge. In fact, the vast majority of the work that data scientists must perform is often associated with the tasks that preceded the selection and optimization of ML models such as feature engineering -- the heart of data science.