It's no surprise that artificial intelligence is a key ingredient in the modern tech space. From machine learning to wearables to robotics, the AI across industries is a growing necessity for businesses looking to remain competitive in the long term. Yet there are a few common reasons why businesses often fall short in their AI strategy implementation. Information for this eWEEK Data Points article was supplied by Dr. Charla Griffy-Brown, Professor of Information Systems and Technology Management, and Associate Dean of Executive and Part-Time Programs at Pepperdine University's Graziadio School of Business. Here she discusses five key reasons AI strategies fail and what businesses can do to avoid these pitfalls.

Artificial intelligence (AI) and machine learning (ML) are experiencing massive growth as companies increasingly look for fast, cost-efficient and innovative ways to use the big data at their disposal. But in order to effectively deploy these technologies, companies' teams must stay up to date on the latest trends in data science. Today, the term "data science" covers AI, ML, the internet of things, deep learning and others. In simple terms, it's a combination of data inference, algorithm computation, analysis and technology that helps in solving complex business problems. Data science also helps businesses use advanced tools and technologies to automate complicated business processes linked with extracting, analyzing and presenting raw data.

Data science is the current powerhouse for organizations, turning mountains of data into actionable business insights that impact every part of the business, including customer experience, revenue, operations, risk management and other functions. Data science has the potential to dramatically accelerate digital transformation initiatives, delivering greater performance and advantages over the competition. However, not all data science platforms and methodologies are created equal. The ability to use data science to make predictions and take decisions that optimize business outcome requires transparency and accountability. There are several underlying factors such as trust, having confidence in the prediction and understanding how the technology works, but fundamentally it comes down to whether the platform uses a black-box or white-box model approach.

Continuously connected customers with multiple devices and an endless number of interaction touchpoints aren't easy to engage. They're on a multi-dimensional journey and can appear to a brand at any time, on any channel. It's not surprising, then, that consumers give brands low marks for their ability to deliver an exceptional customer experience. According to a recent Harris Poll survey, only 18 percent of consumers rated brands' ability to deliver an exceptional experience as excellent. Even if the data about a customer is well managed, to successfully engage the connected consumer and deliver highly personalized experiences requires advanced analytical tools.

Companies around the world are investing tens of billions of dollars on artificial intelligence (AI) and machine learning (ML), and for good reason. These technologies have real business-altering potential, and that's why Gartner's "Enter the Age of Analytics" report predicts that by 2023, AI and deep-learning techniques will be the two most common approaches for new applications of data science. Effective use of both AI and ML in business production use cases can help enterprises that use them jump far ahead of competitors in their sectors, because the technologies remove friction that gums up processes. But despite this promise, few companies have been able to successfully implement and deploy this technology as part of their overall data and analytics strategy. According to Gartner, 46 percent of CIOs have developed plans to deploy AI, but just 4 percent have made the concept a reality.

Machine learning helps organizations handle the truth by enabling them to create an accurate data model from among huge stores of data and then build processes that are constantly improving through input from the community. This is easier said than done, however, because today's ML tools ecosystem is incredibly complex, and organizations often don't have people with the skills to navigate it. In addition, organizations have a difficult time trusting the so-called black box output of many ML models. Go here to see a listing of eWEEK's Top Predictive Analytics Companies. This eWEEK Data Points article, using industry information from MarkLogic Senior Product Manager Anthony Roach, offers eight things you need to know about the current state of ML and how embedding ML into the database platform enables organizations to innovate quickly and intelligently, based on trusted data and trusted analysis of that data.