Artificial Intelligence (AI) is the new black, the shiny new object, the answer to every marketer's prayers, and the end of creativity. 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 undertaken by tech giants like Amazon. Google and Tesla. But the hype exceeds the day-to-day reality.
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 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, which are multiplying each year. The growth of mobile media, social networks, apps, automated personal assistants, wearables, electronic medical records, self-reporting cars 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, fill-in and connect disparate data long before any analysis 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 and appropriate privacy protections. It also requires considerable skill in modeling and a cadre of data scientists capable of seeing the forest rather than the trees.
One-to-One 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.
Using AI, we are in an early test-and-learn phase led by companies in the financial services, telecom and retail sectors.
People Prize Predictive Analytics. Amazon trained us to expect personalized recommendations. We grew up online with the notion, "if you liked this, you'll probably like that." As a result we expect favorite brands to know us and to responsibly use the data we share, knowingly and unknowingly, to make our lives easier, more convenient and better. For consumers 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.
Platform Proliferation. It seems that every tech company is now in the AI space making all manner of claims. With more than 3500 Martech offerings on top of countless installed legacy systems, it's no wonder marketers are confused and IT guys are stymied. A recent Conductor survey revealed that 38 percent of marketers surveyed were using 6-10 Martech solutions and another 20 percent were using 10-20 solutions. Cobbling together a coherent IT landscape in service to marketing objectives, finessing the limitation of legacy systems and existing software licenses while processing massive data sets isn't for the faint of heart. In some cases, AI needs to work around installed technology platforms.
Artificial Intelligence is valuable and evolving. It's 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. Operated in this fashion, AI will deliver much more value to consumers than drones or robots.