B2B software sales and marketing teams love hearing the term "artificial intelligence" (AI). AI has a smoke and mirrors effect. But, when we say "AI is doing this," our buyers often know so little about AI that they don't ask the hard questions. In industries like the DevTools space, it is crucial that buyers understand both what products do and what their limitations are to ensure that these products meet their needs. If the purpose of AI is to make good decisions for humans, to accept that "AI is doing this" is to accept that we don't really know how the product works or if it is making good decisions for us.When we're in the buyer role, we often don't hold ourselves responsible for understanding AI and machine learning (ML) products because these technologies are intimidating.
Artificial Intelligence (AI) is the new buzz word. We all have heard and read that it will change the world. However, most articles fall short on explaining how exactly AI algorithms can be used to solve real-world problems. This series is my attempt at bridging the gap between technical AI and applications of AI. For this series, I will restrict to Machine Learning (ML) algorithms which is a section of AI where we let machines learn from data.
AI refers to machine intelligence or a machine's ability to replicate the cognitive functions of a human being. It has the ability to learn and solve problems. In computer science, these machines are aptly called "intelligent agents" or bots. Not all AI are alike. In fact, what is considered artificial intelligence has shifted as the technology develops.
AI, or Artificial Intelligence, is often demonised and portrayed as some cyborg entity just about ready to take our jobs and eventually kill us all, but more and more businesses, martech and adtech providers are using different AI subsystems each day to advance their services. The term AI is contentiously used to describe a broad spectrum of systems and software's, the controversy arises from where we can begin to describe a machine as being'intelligent' opposed to simply following complex but nonetheless human-reliant algorithms. Regardless of strict definition, there are helpful systems within the subsets of AI which already exist that B2B marketers need to utilise. Machine learning is a subset of AI that can help marketers to improve productivity by taking over mundane tasks, particularly work involving dissecting datasets (like our Argus platform for example). If you're not already using some forms of machine learning, it might be helpful to understand why some sytstems have been reported to increase the productivity of business by 40% (Source: Accenture) and how you can effectively incorporate machine learning into your marketing strategy.
A survey last year found that 98 percent of smartphone owners had used their device's artificial intelligence-based virtual personal assistant (VPA). The majority of those surveyed were inhibited about talking to their artificial intelligence (AI)-powered VPAs in public, but that's likely to change as AI becomes more firmly entrenched in everyday life. As AI becomes a part of daily living, brand leaders are realizing the potential the technology has to transform marketing. With AI, marketers can understand customers more completely and connect with them on a deeper, more personal level. This can allow brands to deliver a buying experience that is relevant to the customer.