Artificial intelligence and Machine Learning are two of the hottest trends in enterprise tech, with both IT vendors and their customers desperate to reap their benefits. TechTarget defines AI as “the simulation of human intelligence processes by machines, especially computer systems.” Machine Learning is a type of AI in which systems learn automatically without ongoing programming from humans. The two have become possible as the result of massive volumes of data being generated and collected during the past two decades: AI/ML draws on Big Data to learn.
According to Gartner, AI will produce worldwide business value of $3.9 trillion by 2022. Most people are familiar with the value AI/ML provides at a consumer level – for example, online bots, self-driving cars, Netflix’s recommendation engine. AI/ML also provides major value at the enterprise level, allowing businesses to automate and improve all sorts of processes, such as manufacturing, accounting, IT operations and security. To support these enterprise use cases, vendors provide a wide variety of software and hardware.
Coverage overview and analysis
With AI/ML being hot-button topics that are of interest and concern to consumers as well as business it is not surprising that top tier business publications are giving space to the topic. Forbes, Bloomberg, Business Insider, and other heavily consumer-facing business publications are covering AI and Machine Learning at a high rate. AI isn’t just for techies.
In fact, Forbes is very favorable towards AI and Machine Learning. It’s important to note that much of Forbes coverage relates to consumer-related applications and the impact of AI on automation and jobs. With articles like “Are Robots About To Enter The Healthcare Workforce?” and “Why Design Thinking Is The Future of Sales,” AI and Machine Learning are colloquial topics. It’s less about the technical applications that trade publications, say TechRadar, offer. For trade, it’s about the nuts and bolts of technology – the products, the partnerships, the verticals served, etc.
Imagine your average reader. It’s likely they don’t care much for the RTX 2070 GPU or TensorFlow. You can bet that more of them interested in a Tesla road trip or robot etiquette. So in that regard, none of this is particularly surprising.
With all that said, the far more interesting insight comes from the graph above. The top five reporters, each covering AI and Machine Learning ten times or more, are all from trade publications.
Angela Guess of Dataversity focuses on more technical and industry-driven news, from product specifications to partnerships between companies. If you’ve recently collaborated with a start-up to launch an AI program, Angela is a good target.
On the flip side, take another of our top reporters: Kyle Wiggers at VentureBeat. VentureBeat focuses on tech, but at a business level. Kyle favors AI’s consumer applications and focuses heavily on major brands like Google and Facebook. Rather than learning about a company’s AI-focused server chip, he’s more likely to be interested in learning how a new sensor will improve the performance of self-driving cars.
From an enterprise perspective, trade publications’ coverage focuses on the various vendor technologies (hardware and software) that are required to make AI/ML work. Some of the vendor technologies you’ll see discussed with AI/ML include Nvidia GPU processors, HPE servers, Cloudian object storage appliances, BlueData BDaaS software, Cisco Tetration Analytics and Unravel Data’s Application Performance Management platform. Trade reporters cover new AI/ML products from such vendors and are often write about new POVs on best deploying AI/ML from thought leaders at these companies
There are a number of hot open source software offerings that are aimed at deploying AI/ML, such as Spark, Kafka and TensorFlow. Trade media closely track the development of these offerings and discuss general trends, such as rising popularity of one or confusion about how another is supposed to be deployed.
When we look at the overall landscape of who is covering AI and Machine Learning, it’s clear there is a wide appeal AND precision-focusing is key to coverage in a very noisy and increasingly over-hyped space . But you probably knew that. AI has its own very busy hashtag on social media. There were twenty-two new tweets tagged to #AI in the time it took to write this one sentence. And that doesn’t include #ML or #MachineLearning.
But let’s circle back. AI/ML are hot topics, sexy technology that brings cool new ideas to the real world for consumers and businesses and techies. The media landscape evaluation tells us that there’s a widespread interest in what AI and Machine Learning do and mean. To get noticed with all the buzz, organizations need the right positioning and to reach the right people with the right angle. There is no one right message. The best thing you can do is be compelling, tell a story, and never forget to say why a reporter or their audience needs to know this information.
Conclusion
The media covering Artificial Intelligence and Machine Learning are interested in any new angles on the technology that they haven’t previously considered. Last year, 10Fold helped secure a great piece of coverage on Machine Learning for Barefoot Networks over at The Next Platform. The agency explained to the reporter that AI/ML have extremely advanced and unique network requirements that traditional networking infrastructure cannot support. In order for AI/ML to see wide deployments, organizations will need to embrace a new style of networking that allows them to customize their networks to meet such unique needs. Barefoot Networks provides that programmable networking technology.
AI/ML will continue to generate more and more headlines in tech, business and news publications. It’s frankly hard to find many publications that are not currently covering the space regularly. AI/ML is no longer a topic of science fiction, it’s a technology that enterprises feel like they need to wield and vendors realize they need to support. However, AI/ML is complex, and the technology that it’s built on (i.e. Spark, Hadoop, TensorFlow) is equally complicated to deploy and manage. As these supporting technologies evolve and new use cases emerge, it’s critical to understand what’s enabling AI/ML at a deeper technical level in order to cut through the noise and earn coverage in key publications.
Good luck out there.
By Jordan Tewell and Morgan Eisenstot