In 1994, I was in graduate school and decided to take a scuba diving course offered by the university to get my Open Water I dive certification. As a part of certification, I had to learn how to read and understand the US Navy dive tables, which are the foundation for determining how long a diver can stay at depth. These are based on dive tables created by the research done by Scotsman John Scott Haldane and his colleagues in 1908 that were first published in 1916. They were revised in the 1980s for recreational dive use.
Calculating and planning your dives using these tables requires a basic understanding of several key concepts, including laws of physics, saturation, and half time, to name a few. Additionally, you need to understand no-decompression time limits, surface interval times, and residual nitrogen times. By mastering these concepts and knowing how to read the dive tables, one can plan the day’s dives and have a general confidence of avoiding decompression sickness.
For the first few months I dove, I used these tables to plan my dives. The knowledge I gained by having to manually calculate my dives and understand the principles behind the algorithms that dive computers used allowed me to trust dive computers. By 1995, I had purchased my first dive computer. My dive planning became less complicated and time consuming, and confidence in my safety increased exponentially. My dive computer was able to instantly calculate my future dives on the basis of my past dives. The data was automatically collected without my intervention or input. What dive computers couldn’t do, and what they still can’t do, is assess how much sleep I got the night before, how hydrated (or dehydrated) I was, and my physical and mental fitness. These are all potential contraindicators to dive safety and still require human judgment for planning dives.
AI and sales operations have travelled a similar journey. AI’s founding event occurred in 1956 at a conference at Dartmouth College in Hanover, NH, where the phrase “artificial intelligence” was coined. Since that time, AI has experienced ebbs and flows in its evolution, but in 1997, IBM’s Deep Blue became the first computer to defeat Russian chess grandmaster Garry Kasparov. This brought AI and its capabilities to the mainstream.
Since then, AI and its applications have proliferated, especially within sales operations roles and responsibilities, and a growing debate has evolved as a result. What AI functions can effectively and efficiently replace the sales rep, and what functions should reps continue to perform, influence, or oversee?
How AI Can Enable Sales Reps
I believe that AI can, should, and will enable sales reps to be better at what humans are naturally better at (at least right now) like social and collaborative skills, performing high-level critical thinking, and communication skills. AI should own and perform tasks that are repeatable or predicable, like sales forecasting. AI, if provided with clean, accurate, and comprehensive data, will provide forecasts that are consistently and continuously updated and accurate. Business decisions based on these forecasts will be better trusted and less risky as a result, and because they understand the “method behind the madness,” and sales managers and reps alike will trust the forecasts. Sales reps can be freed up from the hours they spend guessing at opportunity stage, percentage, next best steps, and likelihood to close.
So, just like my dive computer doesn’t know all of the contraindicators that may be present before I dive, and it only can make recommendations around my future dives on the basis of the data from my past dives, so too is AI dependent on the data provided to it. But with good data, AI can continue to learn and evolve, and by doing so, enable sales reps to focus on what they do best. It’s a symbiotic relationship producing an outcome greater than the sum of its parts. The possibilities are as endless as our imaginations and our ability to change our vision of the sales rep’s role.