Evolution of AI in Sales (Part 1)


AI – Artificial Intelligence, Machine Learning, Machine Intelligence, Thinking Machines, Knowledge Engineering, Natural Language Processing – unless you have been living under a rock these past few years, you have heard one or more of these terms many times. As a business leader you have heard the industry tell you that if your strategy does not involve these knowledge competencies, you don’t have a strategy at all. And if you are sales leader you have probably heard widely varying estimates of the immense untapped potential of AI in sales. This potential is supposed to manifest itself in various forms across the sales funnel, from undiscovered leads to unconverted or lost opportunities. Have you ever wondered when and how did AI become so important in your life? What can you as a sales leader learn from the extraordinary journey of AI that has put it front and center of sales strategy.

In this series, we will explore how AI established itself as the catalyst for a step function change in how we do business. Specifically, we will explore how AI is now transcending from being a cost cutting and optimization force to becoming the single biggest weapon for revenue augmentation.

In this first part of the series, we will see the story of the birth of AI, the ups and downs in the very early part of its story and the story of AI coming of age by becoming a defining force in how we do business.


Artificial Intelligence (AI) has existed in many forms for a long time now. In fact, it is possible to date the concept of artificial intelligence back to various points in history. Depending on who you ask, it could be as relatively-recent as 1898 – when Nikola Tesla demonstrated the world’s first radio-controlled vessel with a ‘borrowed mind’ to use Tesla’s own words, or as ancient as the Greek myths of Hephaestus, the blacksmith who manufactured mechanical servants. The term itself, Artificial Intelligence, was first coined in the 1956 Dartmouth Conference by John McCarthy. He coined this new discipline by combining hitherto separate research streams like automata theory, cybernetics, information processing, complexity theory, language simulation, the study of the relationship between randomness and creative thinking and neural networks.

This was a remarkable recognition of the fact that these independent, emerging fields of study were slowly but surely lending to and leaning on one another, paving the way for development of machines that could simulate multiple aspects of intelligence, thereby fundamentally changing how we as a society manifest and experience intelligence. Clearly, these independent fields of research had seen significant progress in their own right by the time McCarthy connected the dots to give birth to AI. Since then, many separate advancements that have marked the ascent of AI through the past decades.

However one must understand that the ascent of AI has not been a smooth upward curve, but in fact punctuated by two windows of time known as AI winters (1974-1980 & 1988-2011). AI winters is the name given to time periods spanning multiple years when perceived over-promise and under-delivery resulted in AI fading out of the larger socio-economic focus. Let us now look at the story when our hero – AI, finally comes of age.


While the present superhero status of AI has been in the making for many decades now, never before has AI been front and center of the business decision making universe as it has been for the past 3-5 years. Why has AI entered the mainstream agenda of business leaders across industries and continents now?

Significant advancements in research and development areas like big data, mobile communications, algorithms and data science, natural language processing/generation, image recognition, compute power and cloud computing have begun to make the promise of AI real. What does it mean to make real the promise of AI? It means, making

  • AI predictions that are accurate (enabled by algorithmic advancements as well as big data),
  • AI-driven processes and automation that are scalable (enabled by big data and cloud computing)
  • And most importantly insights and recommendations that are consumable and actionable

Turns out the story of AI coming of age bears a startling resemblance to its origins story. Emerging fields of study coming together, paving the way for machines that can take business decisions and deliver recommendations with accuracy and responsiveness equal to or greater than humans.

I would end this section with another caution against generalizations – it would be naïve to think that the curve of AI’s advancement was secular across all the key domains of big data, cloud computing, algorithms, compute power and mobile communications. For example, some of the most popular and impactful classification models used in the world of sales today have been with us for decades, going all the way back to the second summer of AI. However, it was memory-tech advances enabling cost-effective storage of big data that allowed us to use these powerful algorithms over meaningfully large data-sets.

By chronicling through the AI’s origins and coming of age stories, in this post I have tried to draw some parallels between those stories, thereby bringing out a certain trend in how AI evolves and defines itself at various points in time. As a sales strategy professional, I am deeply intrigued by how this evolutionary trend has manifested itself in the world of sales. So in the next part of this series, we will look at how AI is rapidly transcending its traditional fortes like predictive maintenance and supply chain optimization to establish itself as the largest revenue augmentation proposition in front of your industry today. In other words, having read about how AI is changing the way we do business, we will now see – how AI is changing the way we sell.

Author: Kartikey (TK) Tatavarty