AI needs to be meaningful, more thought driven, it needs to solve problems
With the growth in automation and AI, brands are looking to AI as a feature that they might add and what that can do to distinguish them, modernize them, and to give them a new look and a competitive edge.
But Artificial Intelligence cannot do much without data. Data is the new currency and AI is the tool that connects disparate sets of data to make sense out of it. It needs to be more meaningful, thought driven and help solve problems. This was the central theme of the session “Man vs. Machine: Media Planning in an AI-fuelled world”, consisting of Paul Silver, COO at Media iQ, Erin Rech, Head of Digital at Initiative, Rick Watrall, Chief Analytics Officer at Horizon Media, and Bonin Bough, Host of CNBCS Cleveland Hustles
As more marketers become interested in the potential of AI, they need to first answer the question of how tangible the technology and capabilities are. At its most basic, intelligence is a three-step process: the ability to acquire information, distill it into knowledge, and then apply it to behaviors in a variety of contexts. It is the capacity to record data, analyze them, and then act on the findings.
Artificial intelligence is a subset of machine learning and is going to be part of our lives including media. Some of the data and analytics tools will be used for the foreseeable future and AI will only enhance those. It will act as wrappers around some of the existing tools and enhance something as simple as data integrity, proving to be life-changing for data analysts.
Though we are in the nascent stage of AI today, the opportunity for AI in the digital media world lies in personalization. On the advertiser side, it is an extension of what programmatic traders have been trying to do with data-fueled real-time bidding markets for years– deliver the most relevant ad to the right person at the right time. With its lightning-fast processing power, AI could alter the targeting process at unimaginable speed.
So how do you tangibly embed AI into your stack?
In data and analytics, the model has changed from what it was 10 years ago, where a client would just ask for analysis and data analysts would perform that job, but today with the integration of AI, an analyst’s role has changed from someone who can write an R code to someone who can sit in front of the vendor and absorb what they are doing, what their value proposition is, and cut through to the noise to provide more value.
There’s certainly a lot of development still to come and one cannot magically hook AI into their solution stack and expect highly personalized experiences to instantly indulge their audiences. But the answer lies in experimentation.
Watch the highlights of the session here –