The Revolution is Coming – but Not for Everyone
As digital marketing continues to evolve and grow, so have the roles of Artificial Intelligence (AI) and Machine Learning (ML). The tech’s promise is compelling; continuous, automated improvement within an ecosystem that, on a daily basis, expands in both complexity and scale. So is its potential impact on marketers; while the machine handles rote chores, we’ll be free to focus on higher-level strategy.
But, Alas, Not So Fast
AI and ML will not immediately give us the final independent product that we crave. In some instances, we might feel more burdened by its administration than liberated by its utility. Even when initial results feel profound, the adoption or implementation of AI and ML is merely the beginning of a long road. Marketers should brace themselves for the continuous, iterative effort it will take. Persistent evaluation of the effectiveness of the tools will be necessary, as well as strict oversight of the data made available and activated by the technology.
In short, getting to frictionless won’t be effortless.
There are two primary roadblocks to the AI/ML revolution we’ve been promised.
Not All Algorithms Are Created Equal
Nearly all marketing technology companies claim that their differentiator is their vastly superior AI and/or ML capabilities. In a commoditized vertical, this is suspect. Before moving forward with solutions or testing providers, it’s imperative to set expectations with benchmarks you aim to clear — both for metric and cost performance.
Algorithms Are What They Eat
Regardless of how powerful your technology is, and the range of digital data inputs it can process, your outputs will fall short if you fail to feed your algorithm the right data. The most powerful marketing systems in the world are rendered useless if they aren’t oriented in service of the goals and desired business results of their masters.
Unfortunately, many organizations fail to include their most critical business inputs in their algorithms; either due to a lack of technical capabilities, office politics, or failures of foresight. This is a difficult but solvable challenge. In the absence of integrated customer data, brands should employ practices that strategically dissect customer data; examining it for traits, patterns, behaviors, and interests that separate signal from noise. This is not a light task and requires analysts and data scientists with considerable training in programs outside of traditional MarTech platforms.
With the investment in AI and ML, the industry has made, and is certain to increase, it goes without saying that the success of many organizations rests on how quickly they can make good on this outlay. This requires acknowledging the full breadth of support AI and ML programs need in order to approach self-sustainability.
Further, only those who put in the effort today to fully vet and feed their programs will benefit tomorrow from their incredible promise. It’s ironic but true; the success of our algorithms hinges on the efforts of a less-celebrated kind of intelligence. Human ingenuity.