Artificial intelligence (AI) is the new black, the shiny new object, the answer to every marketer’s prayer, and the end of creativity. The recent emergence of artificial intelligence from the shadowy halls of academia and data science backrooms has been spurred by stories of drones, robots and driverless cars spawned by tech giants like Amazon. Google and Tesla. But the hype goes beyond everyday reality.
Artificial intelligence has a fifty-year history in the development, experimentation, and thinking of mathematics and computer science. It’s not an overnight sensation. What makes it exciting is the confluence of large data sets, improved platforms and software, faster and more powerful processing capabilities, and a growing cadre of data scientists passionate about exploiting a wide range of applications. The clichéd everyday uses of AI and machine learning will make a bigger difference to the lives of consumers and brands than the flashy apps peddled by the press.
So consider this AI reality test:
Big data is messy. We are creating data and connecting massive data sets at an extraordinary rate, which is doubling every year. The growth of mobile media, social networking, applications, automated personal assistants, wearable devices, electronic medical records, automobiles, self-reporting devices, and the prospective Internet of Things (IoT) create enormous opportunities and challenges. In most cases, there is a lot of work involved in aligning, flattening, filling, and connecting disparate data long before any analysis begins.
Collecting, storing, filtering and associating these bits and bytes with any individual is challenging and intrusive. Compiling the so-called “golden record” requires significant computing power, a robust platform, and fuzzy logic or deep learning to link disparate pieces of data and appropriate privacy protections. It also requires great modeling skill and a cadre of data scientists who are able to see through the forest rather than the trees.
One-on-one is still ambitious. The dream of one-on-one personal communication looms large but is still ambitious. Gate factors are the need to develop common protocols for resolving identity, protecting privacy, understanding individual sensitivities and permissions, identifying inflection points, and a blueprint for how consumers and segments move through time and space in their journey from the need for brand preference.
With AI, we are in the early stage of testing and learning led by companies in the financial services, telecom and retail sectors.
Predictive Analytics for People Awards. Amazon has trained us to expect personalized recommendations. We grew up on the Internet with the idea, “If you like this, you’ll probably like this.” As a result, we expect our favorite brands to know us and use the data we share responsibly, 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 considered valuable. Anything less than that is SPAM.
But making realistic, actionable predictions based on data is still more of an art than a science. Humans are creatures of habits with some predictable patterns of attention and behavior. But we are not necessarily rational, often inconsistent, and quickly change our opinions or our course of action and our own in general. AI, using deep learning techniques as it trains the algorithm itself, can go some way to making sense of this data by observing actions over time, matching behaviors to observable criteria and evaluating anomalies.
spread platform. It seems like every tech company right now in the AI space is making all kinds of claims. With over 3,500 Martech offerings as well as countless legacy systems installed, it’s no wonder marketers and IT guys are confused. A recent Conductor survey revealed that 38 percent of marketers surveyed use 6-10 Martech solutions and another 20 percent use 10-20 solutions. Bringing together a coherent landscape of IT in service of marketing goals, easing the constraints of legacy systems and existing software licenses while processing huge datasets is not for the faint of heart. In some cases, AI needs to get around proven technical platforms.
Artificial intelligence is valuable and evolving. It’s not a silver bullet. Requires a combination of skilled data scientists and a strong contemporary platform driven by a client-centric perspective and test-and-learn mindset. By working in this way, AI will deliver far more value to consumers than drones or robots.