A recent Accenture research on the state of machine learning (ML) in enterprises indicates 84% of C-suite executives believe they must leverage artificial intelligence (AI) to achieve their growth objectives, yet 76% report they struggle with how to scale. The key challenges in scaling ML in enterprises are availability of quality data and ability to explain ML outcomes; here we will discuss how Master Data Management (MDM) can help address these challenges. In 1959, Arthur Samuel defined machine learning as "... gives computers the ability to learn without being explicitly programmed". And how exactly is that achieved? At its core, machine learning is the application of statistical methods to uncover patterns in the training data and make predictions or decisions without being explicitly programmed.

We saw it during the Industrial Revolution, which vastly improved the average living standard, but also led to poor labour conditions and environmental degradation – over a timeline that was difficult to foresee. And here we are, at the dawn of the AI revolution, where the advent of cloud computing and computer processing power, cheap storage, new algorithms, as well as new product and service innovations realises the benefits of a technology – from driverless cars and virtual reality to medical diagnostics and predictive machine maintenance. In tandem, however, we also see some negative, often unintended consequences of these technologies. They go from the rise of fake news and algorithms that favour the incendiary and divisive over the factual, to major privacy breaches and AI models that discriminate against minority groups or even cost human lives. AI is a powerful tool, and it's never been more important for C-suite executives to understand both how to leverage it for growth and innovation, and how to do so responsibly and ethically.

Defining the appropriate organizational design with which to embed AI and data-driven decision-making across an organization is complex. Multiple challenges must be overcome to align AI with all parts of the organization, from engineering to customer-facing units, and to upskill the workforce effectively. Success – even at the level of specific AI project implementations – is not a given: executives need to understand new project execution risk factors (beyond usual ones such as change management challenges) that can lead to costly project failures. These may include very challenging data issues or difficulties from continuous risk management of the AI models deployed. This only covers half of the AI map for executives.

Artificial intelligence (AI) is a constellation of technologies harmoniously enabling machines to act, learn and understand with human-like levels of reasoning. Maybe that's why everyone's definition of AI is different: It's so much more than just one thing. Machine learning and natural language processing are at the heart of AI. When paired with analytics and automation, these evolving innovations help companies improve customer service, optimize supply chains and achieve a seemingly endless number of business goals. And, fun fact, it can even help restore coral reefs.

But, 58% of businesses say that less than 10% of their company's digital budget goes towards AI. So, it's not surprising that only 15% of AI projects succeed. This disconnect comes as no surprise to us. I was an analyst at Morgan Stanley and earned my technology stripes (and scars) at Apple under Steve Jobs, while my partner and wife, Helen, is a former CIO and corporate venture capitalist. We know the hallmarks of innovation window-dressing and transformation-by-outsourcing.

A full 84% of C-suite executives believe they must leverage artificial intelligence (AI) to achieve their growth objectives. Nearly all C-suite executives view AI as an enabler of their strategic priorities. And an overwhelming majority believe achieving a positive return on AI investments requires scaling across the organization. Yet 76% acknowledge they struggle when it comes to scaling it across the business. What's more, three out of four C-suite executives believe that if they don't scale AI in the next five years, they risk going out of business entirely.

AI is helping Royal Dutch Shell locate new oil and gas sources. One of the company's 280 AI projects is aimed at helping the company find new sources of oil and gas by cleaning up data from seismic surveys, which are used to create images of rock formations that in turn help scientists locate oil deposits below the ocean floor. The problem, historically, has been that these surveys don't paint a clear picture of what rock formations look like. Underwater currents and other factors produce noisy data that affects the images. Shell created machine-learning algorithms, based on images the company has cleaned, to filter out that noise.

A full 84% of C-suite executives believe they must leverage artificial intelligence (AI) to achieve their growth objectives. Nearly all C-suite executives view AI as an enabler of their strategic priorities. And an overwhelming majority believe achieving a positive return on AI investments requires scaling across the organization. Yet 76% acknowledge they struggle when it comes to scaling it across the business. What's more, three out of four C-suite executives believe that if they don't scale AI in the next five years, they risk going out of business entirely.

Despite advancing threats from hackers and nation states, human error remains the top cybersecurity concern for both C-suite executives and policymakers, according to a Wednesday report from Oracle. To combat this issue, professionals must invest more in employees--via training and hiring--than in technologies in the coming two years, the report found. Only 38% of C-suite executives said they plan to invest in artificial intelligence (AI) and machine learning to improve security in the next two years, though these technologies can aid in minimizing human error, the report said. In terms of other security investments over that time frame, 44% of C-suite executives said they plan to purchase new software with improved security, and 37% said they plan to invest in new infrastructure solutions, according to the report. In the last five years, C-suite executives said they have upgraded existing software (60%), trained existing staff (57%), purchased new software with enhanced security features (54%), and invested in new infrastructure solutions (40%) to improve security, the report found.

Headlines about machine learning (ML) often accompany stories of futuristic innovations that range from biomedical discoveries to space travel. But some of the most valuable and immediately available applications of ML -- the subset of artificial intelligence whose algorithms rely on historical data to perform tasks with increasing accuracy and without explicit instructions from human programmers -- exist in the continuing quest for greater efficiency in everyday business functions. ML-fueled advancements to natural language processing have been key to professions that have traditionally required employees to read and process long, comprehensive documents mechanically, like finance and law. If you've ever been stuck reading a thousand-page document for work or missed a vital security weakness while doing a network scan, for example, odds are you would have benefited from adopting ML into your workflow. At scale, it can be an industry game-changer.