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Piercing the AI Hype
By: Danny Flamberg
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Artificial Intelligence (AI) is the new black. It’s the shiny new object, the answer to every marketer’s prayers. The recent emergence of AI from the arcane halls of academia and the backrooms of data science has been prompted by stories of drones, robots and driverless cars. AI, it is. Said, will affect everything and everyone. But beware of the hype.
 
AI has a fifty-year history of mathematical and computer science development, experimentation and thought. It’s not an overnight sensation. What makes it exciting and explosive is the confluence of large data sets, improved platforms and software, faster and more robust processing capabilities and a growing cadre of data scientists eager to exploit a wider range of applications. The prosaic day-to-day uses of artificial intelligence and machine learning will make a bigger difference in the lives of consumers and brands than the flashy applications touted in the press.   
 
So consider this AI reality check:
 
Big Data is Messy. We are creating data and connecting big data sets at extraordinary rates. The growth of mobile media, social networks, apps, automated personal assistants, wearables, electronic medical records, self-reporting cars, voice-activated assistants and appliances and the forthcoming Internet of Things (IoT) create enormous opportunities and challenges. In most cases, there is considerable and lengthy work to align, normalize, and connect disparate data sets long before any AI can be started.
 
Collecting, storing, filtering and connecting these bits and bytes to any given individual is tricky and intrusive. Compiling a so-called “Golden Record” requires considerable computing power, a robust platform, fuzzy logic or deep learning to link disparate pieces of data while insuring and privacy protections. It also requires considerable skill in modeling and a cadre of data scientists capable of seeing the forest rather than the trees.
 
Real Personalization is Still Aspirational.  The dream of one-to-one personalized communication is on the horizon but still aspirational. The gating factors are the need to develop common protocols for identity resolution, privacy protections, an understanding of individual sensibilities and permissions, the identification of inflection points and a detailed plot of how individual consumers and segments move through time and space in their journey from need to brand preference. We are in an early AI test-and-learn phase led by companies in the financial services, telecom and retail sectors.
 
Predictive Analytics Isn’t a Panacea. We grew up with the notion, “if you liked this, you’ll probably like that.”  As a result, we expect brands to know us and to responsibly use the data we share, knowingly and unknowingly, to make our lives more convenient. Predictive analytics works if the content is personally relevant, useful and perceived as valuable. Anything short of that is SPAM.
 
But making realistic, practical data-driven predictions is still more art than science. Humans are creatures of habit with some predictable patterns of interest and behavior. But we are not necessarily rational, frequently inconsistent, quick to change our minds or change our course of action and generally idiosyncratic. AI, using deep learning techniques where the algorithm trains itself, can go some of the way to making sense of this data by monitoring actions over time, aligning behaviors with observable benchmarks and assessing anomalies. 
 
Artificial Intelligence is not a silver bullet. It requires a combination of skilled data scientists and a powerful contemporary platform directed by a customer-centric perspective and a test-and-learn mentality. It’s not the universal right answer.
 

   

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About the Author
Danny Flamberg, EVP Managing Director of Digital Strategy and CRM at Publicis based in New York, has been building brands and building businesses for more than 30 years.Prior to joining Publicis, he led a successful global consulting group called Booster Rocket, as Managing Partner. Before becoming a consultant, he was Vice President of Global Marketing at SAP, SVP and Managing Director at Digitas in New York and Europe and President of Relationship Marketing at Amiratti Puris Lintas and Lowe Worldwide.
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