AI & Machine Learning in the enterprise

AI & Machine Learning in the enterprise

AI & Machine Learning in the enterprise

Interest in Artificial Intelligence and Machine Learning has seen a significant boom in recent times as the techniques and technologies behind them have quickly emerged from the research labs to the mainstream and into our everyday lives.

AI is helping organizations to automate routine operational tasks that would otherwise need to be performed by employees, often at a steep time and financial cost. By automating high-volume tasks, the need for human input in many areas is being reduced, creating more efficient and cost-effective processes.

Today we’re going to take a look at why we are seeing this rapid increase in interest in the areas of AI and Machine Learning, the key trends emerging, how various industries are leveraging them, and the challenges that lie ahead in a fascinating area with seemingly unlimited potential.

The mathematical approaches underlying Machine Learning are not new. In fact, many date back as far as the early 1800’s, which begs the question, why are we only now seeing this boom in Machine Learning and AI? The techniques behind these advancements generally require a considerable amount of both data and computational power, both of which continue to become more and more accessible and affordable to even the smallest of organizations. Significant recent improvements in computational capacities and an ever-expanding glut of accessible data are helping to bring AI and Machine Learning from futuristic fiction to the everyday norm. So much of what we do and touch on daily basis, whether in work, at home, or at play, contains some form of ML or AI element, even if we are not always aware of it.

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We’re seeing this boom now because technological advancements have made it possible. Not only that, organizations are seeing clear and quantifiable evidence that these advancements can help them overcome a variety of operational problems, streamline their processes and enable better decision-making.

Analyzing the sheer volume of data that is being generated on a daily basis creates a unique challenge that requires sophisticated and cutting-edge research to help solve. As the volume and variety of data sources continues to expand so too does the need to develop new methods of analysis, with research focussing on the development of new algorithms and ‘tricks’ to improve performance and enable greater levels of analysis.

As the level of accessible data continues to grow, and the cost of storing and maintaining it continues to drop, more and more Machine Learning solutions hosting pre-trained models-as-a-service are making it easier and more affordable for organizations to take advantage. Without necessarily needing to hire Machine Learning experts, even the smallest of companies are now just an API call away from retrieving powerful and actionable insights from their data. From a development point of view, this is enabling the quick movement of application prototypes into production, which is spurring the growth of new apps and startups that are now entering and disrupting most markets and industries out there.

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Regardless of what an organization does or what industry they belong to, data is helping to drive value. Some will be using it to spot trends in performance or markets to help predict and prepare for future outcomes, while others will be using it to personalize their inventory, creating a better user experience and promoting an increased level of engagement with their customers.

Traditionally, organizational decisions have been made based on numerical and/or structured data, as access to relevant unstructured data was either unavailable, or simply unattainable. With the explosion of big data in recent time and the improvement in Machine Learning capabilities, huge amounts of unstructured data can now be aggregated and analyzed, enabling a deeper level of insight and analysis which leads to more informed decision-making.

 



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