Artificial Intelligence Terms

AI has become a catchall term for applications that perform complex tasks that once required human input such as communicating with customers online or playing chess. The term is often used interchangeably with its subfields, which include machine learning and deep learning. There are differences, however. For example, machine learning is focused on building systems that learn or improve their performance based on the data they consume. It’s important to note that although all machine learning is AI, not all AI is machine learning.

To get the full value from AI, many companies are making significant investments in data science teams. Data science, an interdisciplinary field that uses scientific and other methods to extract value from data, combines skills from fields such as statistics and computer science with business knowledge to analyze data collected from multiple sources.

How AI Can Help Organizations

The central tenet of AI is to replicate—and then exceed—the way humans perceive and react to the world. It’s fast becoming the cornerstone of innovation. Powered by various forms of machine learning that recognize patterns in data to enable predictions, AI can add value to your business by:

• Providing a more comprehensive understanding of the abundance of data available
• Relying on predictions to automate excessively complex or mundane tasks

AI in the Enterprise

AI technology is improving enterprise performance and productivity by automating processes or tasks that once required human power. AI can also make sense of data on a scale that no human ever could. That capability can return substantial business benefits. For example, Netflix uses machine learning to provide a level of personalization that helped the company grow its customer base by more than 25 percent in 2017.

Most companies have made data science a priority and are investing in it heavily. In Gartner's recent survey of more than 3,000 CIOs, respondents ranked analytics and business intelligence as the top differentiating technology for their organizations. The CIOs surveyed see these technologies as the most strategic for their companies; therefore, they are attracting the most new investment.

AI has value for most every function, business, and industry. It includes general and industry-specific applications such as:

• Using transactional and demographic data to predict how much certain customers will spend over the course of their relationship with a business (or customer lifetime value)
• Optimizing pricing based on customer behavior and preferences
• Using image recognition to analyze X-ray images for signs of cancer

How Enterprises Use AI

According to the Harvard Business Review, enterprises are primarily using AI to:

• Detect and deter security intrusions (44 percent)
• Resolve users’ technology issues (41 percent)
• Reduce production management work (34 percent)
• Gauge internal compliance in using approved vendors (34 percent)

What’s Driving AI Adoption?

Three factors are driving the development of AI across industries:

• Affordable, high-performance computing capability is readily available. The abundance of commodity compute power in the cloud enables easy access to affordable, high-performance computing power. Before this development, the only computing environments available for AI were non-cloud-based and cost prohibitive.
• Large volumes of data are available for training. AI needs to be trained on lots of data to make the right predictions. The emergence of different tools for labeling data, plus the ease and affordability with which organizations can store and process both structured and unstructured data, is enabling more organizations to build and train AI algorithms.
• Applied AI delivers a competitive advantage. Enterprises are increasingly recognizing the competitive advantage of applying AI insights to business objectives and are making it a businesswide priority. For example, targeted recommendations provided by AI can help businesses make better decisions faster. Many of the features and capabilities of AI can lead to lower costs, reduced risks, faster time to market, and much more.

The Benefits and Challenges of Operationalizing AI

There are numerous success stories that prove AI’s value. Organizations that add machine learning and cognitive interactions to traditional business processes and applications can greatly improve user experience and boost productivity.

However, there are some stumbling blocks. Few companies have deployed AI at scale, for several reasons. For example, if they don’t use cloud computing, AI projects are often computationally expensive. They are also complex to build and require expertise that’s in high demand but short supply. Knowing when and where to incorporate AI, as well as when to turn to a third party, will help minimize these difficulties.

AI Success Stories

AI is the driving factor behind some significant success stories:

• According to the Harvard Business Review, the Associated Press produced 12 times more stories by training AI software to automatically write short earnings news stories. This effort freed its journalists to write more in-depth pieces.
• Deep Patient, an AI-powered tool built by the Icahn School of Medicine at Mount Sinai, allows doctors to identify high-risk patients before diseases are even diagnosed. The tool analyzes a patient’s medical history to predict almost 80 diseases up to one year prior to onset, according to insideBIGDATA.

Ready-to-Use AI Is Making Operationalizing AI Easier

The emergence of AI-powered solutions and tools means that more companies can take advantage of AI at a lower cost and in less time. Ready-to-use AI refers to the solutions, tools, and software that either have built-in AI capabilities or automate the process of algorithmic decision-making.

Ready-to-use AI can be anything from autonomous databases, which self-heal using machine learning, to prebuilt models that can be applied to a variety of datasets to solve challenges such as image recognition and text analysis. It can help companies achieve a faster time to value, increase productivity, reduce costs, and improve relationships with customers.

How to Get Started with AI

Communicate with customers through chatbots. Chatbots use natural language processing to understand customers and allow them to ask questions and get information. These chatbots learn over time so they can add greater value to customer interactions.

Monitor your data center. IT operations teams can save huge amounts of time and energy on system monitoring by putting all web, application, database performance, user experience, and log data into one cloud-based data platform that automatically monitors thresholds and detects anomalies.

Perform business analysis without an expert. Analytic tools with a visual user interface allow nontechnical people to easily query a system and get an understandable answer.

Roadblocks to Realizing AI’s Full Potential

Despite AI’s promise, many companies are not realizing the full potential of machine learning and other AI functions. Why? Ironically, it turns out that the issue is, in large part...people. Inefficient workflows can hold companies back from getting the full value of their AI implementations.

For example, data scientists can face challenges getting the resources and data they need to build machine learning models. They may have trouble collaborating with their teammates. And they have many different open source tools to manage, while application developers sometimes need to entirely recode models that data scientists develop before they can embed them into their applications.

With a growing list of open source AI tools, IT ends up spending more time supporting the data science teams by continuously updating their work environments. This issue is compounded by limited standardization across how data science teams like to work.

Finally, senior executives might not be able to visualize the full potential of their company’s AI investments. Consequently, they don’t lend enough sponsorship and resources to creating the collaborative and integrated ecosystem required for AI to be successful.

Creating the Right Culture

Making the most of AI—and avoiding the issues that are holding successful implementations back—means implementing a team culture that fully supports the AI ecosystem. In this type of environment:

• Business analysts work with data scientists to define the problems and objectives
• Data engineers manage the data and the underlying data platform so it’s fully operational for analysis
• Data scientists prepare, explore, visualize, and model data on a data science platform
• IT architects manage the underlying infrastructure required for supporting data science at scale, whether on premises or in the cloud
• Application developers deploy models into applications to build data-driven products

Find out how your data science team can work together more efficiently.

From Artificial Intelligence to Adaptive Intelligence

As AI capabilities have made their way into mainstream enterprise operations, a new term is evolving: adaptive intelligence. Adaptive intelligence applications help enterprises make better business decisions by combining the power of real-time internal and external data with decision science and highly scalable computing infrastructure.

These applications essentially make your business smarter. This empowers you to provide your customers with better products, recommendations, and services—all of which bring better business outcomes.

Learn more about the transformational power of Oracle’s SaaS applications with embedded AI.

AI as a Strategic Imperative and Competitive Advantage

AI is a strategic imperative for any business that wants to gain greater efficiency, new revenue opportunities, and boost customer loyalty. It’s fast becoming a competitive advantage for many organizations. With AI, enterprises can accomplish more in less time, create personalized and compelling customer experiences, and predict business outcomes to drive greater profitability.

But AI is still a new and complex technology. To get the most out of it, you need expertise in how to build and manage your AI solutions at scale. A successful AI project requires more than simply hiring a data scientist. Enterprises must implement the right tools, processes, and management strategies to ensure success with AI.

Best Practices for Getting the Most from AI

The Harvard Business Review makes the following recommendations for getting started with AI:

• Apply AI capabilities to those activities that have the greatest and most immediate impact on revenue and cost.
• Use AI to boost productivity with the same number of people, rather than eliminating or adding headcount.
• Begin your AI implementation in the back office, not the front office (IT and accounting will benefit the most).

Getting Help with Your AI Journey

There is no opting out of AI transformation. To stay competitive, every enterprise must eventually embrace AI and build out an AI ecosystem. Companies that fail to adopt AI in some capacity over the next 10 years will be left behind.

Though your company could be the exception, most companies don’t have the in-house talent and expertise to develop the type of ecosystem and solutions that can maximize AI capabilities.

If you need help developing the right strategy and accessing the right tools to succeed in your AI transformation journey, you should look for an innovative partner with deep industry expertise and a comprehensive AI portfolio.