Let’s first define AI from a practical engineering point of view. That is, from the perspective of building functional and efficient algorithms that get better and better over time. In that sense, AI is the confluence of tradecraft, intellectualization, and imagination.
Tradecraft – While this is a term more associated with the spook or intelligence community, it applies to all disciplines, like sales, where an individual can understand and manage the environment, and all that takes place within it, to the point of being able to take a quantitative approach to creating timely and relevant insights. And, of course, the acropolis of insight is prediction. Seeing the future, which brings us back to AI.
Intellectualization – Often seen as understanding detached from emotion, but not rationalization, it’s really about the ability to critically study, think, and reflect upon a domain. In sales, it’s the ability to apply this to the client environment, the sales campaign, and as context, the competition.
Imagination – This is the fun part, but not in terms of fantasy. Here the individual can conceive ideas and concepts outside of current reality. That live beyond conventional thought. In sales, when you stop chasing every deal, responding to RFIs and RFPs, and begin identifying the potential to create opportunities, you bring unexpected business value to the client, influence and shape the buying process, and blow the doors off the competition.
Combine all three of these factors, add an engineering perspective, and you have what you need to build very powerful algorithms. Powerful, in the sense, that they will produce relevant and timely insights that will enable you to do things that others cannot even imagine. In any kind of competitive environment, it enables you to see unexpected or discounted adversary or competitive vulnerability and exploit it with rapid and autonomous thinking. It’s quickly achieving relative superiority though AI empowerment. In sales, it means the AI-guided ability to bring thought leadership to the client, providing unprecedented business value, and anticipating competitive moves. Near real-time AI guidance and direction.
So, how do the algorithms get even smarter? Conceptually, you tie into a bit of machine learning. That is, you segment the environment, look for nuances that matter, and build algorithms that enable an overlay of functional logic to modify the functional algorithms. In sales, algorithms predict the likelihood of being down selected and winning. But, before a prediction is made, the overlay logic conceptually might say, “Hey, wait a minute, you’re competing in Germany with a manufacturing client, up against SAP.” The algorithms then take into consideration certain factors that influence the prediction and directional guidance provided.
You can see that time is a constitutive factor in all of this, which means that when we build AI and think about self-learning, we should not think in terms of days and months, but of centuries. Build AI right and give it a hundred years to grow and you’ll push to the borders of synthetic consciousness!