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A Projects – Intelligent Solution Template

Gainfy Artificial Intelligence

AI Enterprise
Guide.

There are AI-powered applications to enhance customer service, maximize sales, sharpen cybersecurity, optimize supply chains, free up workers from mundane tasks, improve existing products and point the way to new products. It is hard to think of an area in the enterprise where AI — the simulation of human processes by machines, especially computer systems — will not have a deep impact.

Enterprise leaders determined to use AI to improve their businesses and ensure a return on their investment, however, face big challenges on several fronts:

  • The domain of artificial intelligence is changing rapidly because of the tremendous amount of AI research being done. The world’s biggest companies, research institutions and governments around the globe are supporting major research initiatives on AI.
  • There are a multitude of AI use cases: AI can be applied to any problem facing a company or to humankind writ large. In the COVID-19 outbreak, AI is playing an important role in the global effort to contain the spread, detect hotspots, improve patient care, identify therapies and develop vaccines. As we eventually emerge from the pandemic, analyst firm Forrester Research expects investment in AI-enabled hardware and software robotics to surge as companies strive to build resilience against other catastrophes.
  • To reap the value of AI, enterprise leaders must understand how AI works, where AI technologies can be easily used in their businesses and where they cannot — a daunting proposition for the reasons cited above.

This wide-ranging guide to enterprise AI provides the building blocks for becoming an intelligent business consumer of artificial intelligence. It begins with a brief explanation of how AI works and the main types of AI. You will learn about the importance of AI to companies, including a discussion of AI’s principal benefits and the technical and ethical risks it poses; current and potential AI use cases; the challenges of integrating AI applications into existing business processes; and some of the technological breakthroughs driving the field forward. Throughout the guide, we include hyperlinks to TechTarget articles that provide more detail and insights on the topics discussed.

How does AI work?

Many of the tasks done in the enterprise are not automatic but require a certain amount of intelligence. What characterizes intelligence, especially in the context of work, is not simple to pin down. Broadly defined, intelligence is the capacity to acquire knowledge and apply it to achieve an outcome: The action taken is related to the particulars of the situation rather than done by rote.

Getting a machine to perform in this manner is what is generally meant by artificial intelligence. But as AI experts take pains to state, there is no single or simple definition of AI. In a 2016 report by the National Science and Technology Council on preparing for the future of AI, the authors noted that definitions of AI vary from practitioner to practitioner.

“Some define AI loosely as a computerized system that exhibits behavior that is commonly thought of as requiring intelligence. Others define AI as a system capable of rationally solving complex problems or taking appropriate actions to achieve its goals in whatever real world circumstances it encounters,” the report stated.

Moreover, what qualifies as an intelligent machine, the authors explained, is a moving target: A problem that is considered to require AI quickly becomes regarded as “routine data processing” once it is solved.

At a basic level, AI programming focuses on three cognitive skills: Learning, reasoning and self-correction.

  • The learning aspect of AI programming focuses on acquiring data and creating rules for how to turn data into actionable information. The rules, called algorithms, provide computing systems with step-by-step instructions on how to complete a specific task.
  • The reasoning aspect involves AI’s ability to choose the most appropriate algorithm, among a set of algorithms, to use in a particular context.
  • The self-correction aspect focuses on AI’s ability to progressively tune and improve a result until it achieves the desired goal.

Evolution of AI: 4 types of AI

The concept of a machine with human-like intelligence dates to ancient times, represented in the metal automatons referred to in Greek myths, for example, and by the animatronic statues built by Egyptian engineers. Modern AI based on computer systems is generally cited as beginning in the mid-1950s when the term artificial intelligence was coined at a summer conference on the campus of Dartmouth College. Since then, AI’s role in the enterprise has waxed and waned, experiencing two periods known as AI winters when funding and industry interest lagged. Despite these fallow periods, AI continued to evolve. Over the decades, computer scientists, mathematicians and experts in other fields have strived to advance the field either by improving the algorithms or the hardware.

Machine learning enables analysis of massive quantities of data. While it generally delivers faster, more accurate results in order to identify profitable opportunities or dangerous risks, it may also require additional time and resources to train it properly. Combining machine learning with AI and cognitive technologies can make it even more effective in processing large volumes of information.

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