By: Priyank Mohan

Today, Artificial Intelligence (AI) is more focused on performing a single task very smartly rather than providing a comprehensive solution covering many areas that require intelligence. Research firm Gartner predicts that by 2020, AI will be pervasive in almost every new software product and service and that it will be a top five investment priority for more than 30 percent of CIOs. Today’s AI in the communications service provider (CSP) space is primarily focused on machine learning—a branch of artificial intelligence that focuses on the development of intelligent computer programs with the capacity to predict. These programs can learn autonomously by training themselves using historical data and can improve when exposed to new data.

AI has been around for decades, so why the big push by CSPs into AI today? There are three key reasons. The first is Big Data. We have more data than ever, which itself is allowing machine learning to improve and provide more relevant insights. For telecom and cable service providers, today’s digital world provides a plethora of data that can be put to good use and analyzed using AI technologies

The second reason is reduced processing costs. Until recently, the cost to set up infrastructure and build a specialized team was high. AI also required huge R&D budgets and investment in made-to-order algorithms. Today, AI is going through exponential adoption because of four main factors: the rise of ubiquitous computing, low-cost cloud services, inexpensive storage, and new algorithms. Cloud computing and advances in Graphical Processing Units (GPUs) have provided the necessary computational power, while AI algorithms and architectures have progressed rapidly, often enabled by open source software. In fact, today there are many open source options and cloud solutions from Google, Amazon, IBM and more to address infrastructure costs.

The third reason AI is surging in CSP organizations today has to do with breakthroughs in deep learning technology. A subset of machine learning, deep learning has structures loosely inspired by the neural connections in the human brain. Most of these big deep learning technology breakthroughs happened after 2010, but deep learning (neural nets) have already demonstrated the ability to solve highly complex problems that are well beyond the capabilities of a human programmer using if-then statements and decision trees.

 There are four key requirements that every machine learning project should meet:

1. Large volume of historical data with clear success/fail criteria
2. Well-defined information model, meaning the data can be understood and content can be parsed
3. Clear area of value, such as low levels of process automation, high levels of order fallout, poor customer experience, and/or unpredictable performance of network or processes
4. Ease of developing a proof of concept, so value can be demonstrated quickly

AI Best Practices

Like everything else in business, AI vision and strategy work best when they come from the highest level in the organization—which means every business leader should be aware of what AI makes possible. Simply hiring machine learning engineers or data scientists reactively is not a strategy. At the same time, operational employees should know the possibilities AI offers, too. For best results, AI needs to be democratized and socialized throughout all levels in the organization. Customized AI training should be offered for all levels in the organization so that employees can make more informed business decisions. A lack of training may create uncertainty and a fear of lost jobs due to the advent of AI technology.

There are many categories of AI: computer vision, image recognition, deep learning/machine learning (applications and platforms), natural language processing, gesture control, personalized recommendations, smart robots, speech recognition, video analysis, content recognition, speech-to-speech translation, and virtual assistants, among many more. From a maturity standpoint, AI is still fairly new in many of these areas, and solutions are very specific and tailored to solve one problem. There are many AI solutions and products in the marketplace, but one size does not fit all.