Organizations are under growing pressure to transform the volumes of data captured by their systems into valuable insights that drive impact across all levels and lines of business. Investing in AI/ML is no longer optional but critical for organizations to remain competitive. Yet, this growing investment also brings challenges. AI remains complex and out of reach for many. Outcomes that drive real business change can be elusive.

After many fits and starts, the era of enterprise machine learning has finally arrived. According to 451 Research's Voice of the Enterprise, AI and Machine Learning survey, 20% of enterprises have already deployed the technology and a further 33% plan to do so within one year. These figures should come as no surprise: AI has the potential to benefit almost any company by automating and improving a variety of business processes. These encouraging numbers, however, do not indicate that adoption is easy; on the contrary, as with any emerging technology, there are numerous obstacles to success. The lack of skilled resources is the chief obstacle for enterprises, cited by 40% of respondents.

Our friends over a H2O.ai have sponsored a new Business Impact Brief from 451 Research – "Overcoming Obstacles to Machine Learning Adoption." The brief highlights the organizational barriers to machine learning adoption from 451 Research's Voice of the Enterprise: AI and Machine Learning 2H 2018 survey, asking the question: "What are your organization's most significant barriers to using machine learning?" After many fits and starts, the era of enterprise machine learning has finally arrived. According to the 451 Research survey, 20% of enterprises have already deployed the technology and a further 33% plan to do so within one year. These figures should come as no surprise: AI has the potential to benefit almost any company by automating and improving a variety of business processes.

Organizations in every industry are rapidly embracing artificial intelligence (AI) to enable and accelerate their business transformation -- with machine learning proving to be foundational to gaining insights - fueled by data. With the advent of GPU-accelerated data science businesses are realizing faster time-to-insight, making organizations more productive and cost-efficient, gaining competitive advantage. Capital One is one such business that has integrated AI and machine learning at scale, even developing its own Machine Learning Center of Excellence, an in-house consolidation of expertise and technology that enables the financial services giant to expand innovation across many businesses. Senior director Zach Hanif shares his insights on what every business should know as they seek to tap into the power of AI and machine learning. Data is the lifeblood of machine learning efforts.

The darling of artificial intelligence -- the technology referred to most by vendors and media outlets -- is machine learning. In fact, the technology is so popular today that some companies use the terms machine learning and AI interchangeably. What exactly is digital transformation? You may hear the term often, but everyone seems to have a different definition. See how our experts define digitization, and how you can get started in this free guide.

Machine learning enables computers to learn from large amounts of data without being explicitly programmed to do so. We can already see how machine learning gives rise to new intelligent applications, from self-driving cars to intelligent assistants on our smartphones. Increasingly, businesses recognize the importance of using machine learning to transform their data assets into business value. However, many companies are unsure how machine learning can be applied to solve problems in an enterprise context. As the world's most relevant enterprise data is part of SAP's system and business network, SAP aspires to make all its enterprise solutions intelligent and help customers to leverage their data.

For a topic that generates so much interest, it is surprisingly difficult to find a concise definition of machine learning that satisfies everyone. Complicating things further is the fact that much of machine learning, at least in terms of its enterprise value, looks somewhat like existing analytics and business intelligence tools. To set the course for this three-part series that puts the scope of machine learning into enterprise context, we define machine learning as software that extracts high-value knowledge from data with little or no human supervision. Academics who work in formal machine learning theory may object to a definition that limits machine learning to software. In the enterprise, however, machine learning is software.

For a topic that generates so much interest, it is surprisingly difficult to find a concise definition of machine learning that satisfies everyone. Complicating things further is the fact that much of machine learning, at least in terms of its enterprise value, looks somewhat like existing analytics and business intelligence tools. To set the course for this three-part series that puts the scope of machine learning into enterprise context, we define machine learning as software that extracts high-value knowledge from data with little or no human supervision. Academics who work in formal machine learning theory may object to a definition that limits machine learning to software. In the enterprise, however, machine learning is software.

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Machine learning enables computers to learn from large amounts of data without being explicitly programmed to do so. We can already see how machine learning gives rise to new intelligent applications, from self-driving cars to intelligent assistants on our smartphones. Increasingly, businesses recognize the importance of using machine learning to transform their data assets into business value. However, many companies are unsure how machine learning can be applied to solve problems in an enterprise context. As the world's most relevant enterprise data is part of SAP's system and business network, SAP aspires to make all its enterprise solutions intelligent and help customers to leverage their data.