Artificial Intelligence (AI) is the new black, the new shiny object, the answer to the prayers of all marketers and the end of creativity. The recent emergence of AI from the arcane corridors of academia and the back rooms of data science has been fueled by stories of drones, robots, and driverless cars undertaken by tech giants like Amazon. Google and Tesla. But the exaggeration exceeds the reality of the day to day.
AI has a fifty-year history of development, experimentation, and thinking in mathematics and computer science. It is not an overnight sensation. What makes it exciting is the confluence of large data sets, improved platforms and software, faster and stronger processing capabilities, and a growing pool of data scientists eager to exploit a wider range of applications. The prosaic everyday uses of artificial intelligence and machine learning will make a bigger difference in the lives of consumers and brands than the flashy apps touted in the press.
So consider this AI reality check:
Big data is messy. We are creating data and connecting large data sets at extraordinary speeds, multiplying every year. The growth of mobile media, social media, apps, automated personal assistants, wearable devices, electronic medical records, self-reporting cars and appliances, and the upcoming Internet of Things (IoT) create enormous opportunities and challenges. In most cases, there is considerable and time-consuming work to align, normalize, complete, and connect disparate data long before any analysis can begin.
Collecting, storing, filtering, and connecting these bits and bytes to any individual is complicated and intrusive. Compiling a so-called “Gold Record” requires considerable computing power, a robust platform, fuzzy logic, or deep learning to link disparate pieces of data, and adequate privacy protections. It also requires considerable skill in modeling and a group of data scientists capable of seeing the forest instead of the trees.
One by one is still aspirational. The dream of one-to-one personalized communication is on the horizon, but it is still an aspiration. Key drivers are the need to develop common protocols for identity resolution, privacy protection, understanding individual permissions and sensitivities, identifying tipping points, and a detailed plot of how consumers and consumers individual segments move through time and space on their journey from need. to brand preference.
Using AI, we are in an early testing and learning phase led by companies in the financial services, telecommunications and retail sectors.
People Prize Predictive Analytics. Amazon trained us to expect personalized recommendations. We grew up in line with the notion, “if you liked this, you’ll probably like that.” As a result, we expect favorite brands to know about us and 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 less than that is SPAM.
But making realistic and practical predictions based on data is still more of an art than a science. Humans are creatures of habit with some predictable patterns of interest and behavior. But we are not necessarily rational, often inconsistent, quick to change our minds or change our course of action, and generally idiosyncratic. Artificial intelligence, using deep learning techniques in which the algorithm trains itself, can help make sense of this data by monitoring actions over time, aligning behaviors with observable benchmarks, and evaluating anomalies.
Platforms proliferation. It seems like every tech company is now in the AI space making all kinds of claims. With over 3,500 Martech offerings plus countless legacy systems installed, it’s no wonder marketers are confused and IT technicians are locked in. A recent Conductor survey revealed that 38 percent of surveyed marketers used Martech 6-10 solutions and another 20 percent used 10-20 solutions. Fixing a coherent IT landscape serving marketing goals, fine-tuning legacy systems limitation and existing software licenses while processing massive data sets is not for the faint of heart. In some cases, AI needs to work with installed technology platforms.
Artificial intelligence is valuable and evolving. It is not a silver bullet. It requires a combination of trained data scientists and a powerful contemporary platform driven by a customer-centric perspective and a test-and-learn mindset. Operated in this way, AI will offer much more value to consumers than drones or robots.