Artificial intelligence (AI) is poised to transform the way that marketing professionals work, and how organizations target, engage and connect with customers and prospects. Just like how marketing automation created new tasks and job functions, AI will revolutionize the way marketing is performed – and dictate a new set of job needs and skills.
The amount of data that organizations collect and use to inform their strategies and power applications is truly astounding; IDC expects that the amount of data created in 2023 will reach over 100ZB (one trillion gigabytes) or 10 times more than the amount of data created in 2014. Organizations need to make sure that their data is secure, which is why the data protection industry is so important. Data production as a service (DPaaS) solutions are the fastest growing segments of the data protection industry.
We’ve discussed how crowded the overall Internet of Things (IoT) market is, but the ultimate value in IoT lies in IoT applications. However, IoT applications require a strong technology base in order to be successful; hardware, other software platforms and software analytics, and connectivity are all important pieces to the IoT applications puzzle.
Earlier this month, GM announced that it will be adopting Google’s Android Automotive operating system, which included Google’s voice assistance (Google Assistant), embedded navigation (Google Maps), and in-vehicle applications (via the Google Play Store), for all of its vehicle brands beginning in 2021. This landmark deal reinforces the importance of developing and delivering a differentiated in-vehicle experience, as well as demonstrates how large horizontal technology platforms and brands are targeting IoT and key verticals (like automotive) for growth.
We’ve discussed how the term artificial intelligence (AI) covers a wide array of applications; just like many of these functionalities, affective computing is beginning to see some growth in the market. Spanning across computer science, behavioral psychology, and cognitive science, affective computing uses hardware and software to identify human feelings, behaviors, and cognitive states through the detection and analysis of facial, body language, biometric, verbal and/or vocal signals.