Part I: Navigating the Currents of the AI Revolution
This blog is the first of a four-part series that examines AI from a non-technical perspective, emphasizing how AI is being used in the pharmaceutical industry and in the field of marketing research overall.
I. Navigating the Currents of the AI Revolution: A brief look at how we got here and what we can expect from AI
II. AI Basics: A Non-technical Review of AI
III. AI Applications in Pharma: A look at how AI is being used in the pharmaceutical industry from drug discovery to sales & marketing
IV. AI Applications in Marketing Research: A brief overview of how AI is influencing how researchers design, execute and analyze data in marketing research
Generative Artificial Intelligence (AI) signifies one of the most exciting advancements in modern technology. Following the emergence and widespread assimilation of the internet, I had never envisioned observing another substantial technological transformation. Yet, here we are, immersed in it. Much like the internet’s widespread adoption, the rapid integration of AI across various sectors exemplifies another significant technological shift. Within a brief period of time, AI has permeated all sectors, mastering standardized tests, professional licensing exams, and even sophisticated sommelier evaluations. Its commercial applications stretch across business automation & efficiency, marketing, sales, and beyond.
This widespread adoption and rapid integration are cause for both excitement and concern. Data privacy, intellectual property rights, and misuse of deepfake technology are just some of the challenges that must be overcome. Despite these obstacles, the proliferation of AI appears unstoppable. The task for those less technically inclined, like myself, is to understand what AI is and how we can optimally use it on both personal and organizational levels.
What is AI – A Very High Overview
AI is a branch of computer science focused on creating smart machines that can function independently and intelligently to perform tasks normally requiring human intelligence. These tasks we take for granted include learning from experience, understanding natural language, recognizing patterns, solving problems, and making decisions.
The foundation of AI is set in computer science, but it draws in other fields like mathematics, statistics, linguistics, psychology, etc. AI technologies use large amounts of data and algorithms to mimic human behavior. In understanding how it does this, it is important to understand two related concepts: machine learning (ML) and deep learning (DL).
These concepts are crucial to fully grasping the concept of AI because they represent the mechanisms that allow AI to “learn” and improve over time, thereby exhibiting what we perceive as “intelligence.” Essentially, ML and DL are the key methodologies that give AI systems the ability to process information, learn from it, and make decisions or predictions.
Machine Learning is the method by which AI systems learn from data to improve their performance. It’s the process that allows computers to find insights without being explicitly programmed where to look. ML is how AI systems can improve and adapt to new situations over time. It’s the heart of many AI systems in use today.
Deep Learning is a more advanced and complex form of ML. It uses artificial neural networks to model and understand complex patterns in data. The reason it’s called an artificial neural network is because it mimics the distributed approach to problem-solving carried out by neurons in the human brain. DL does this by breaking down tasks into smaller ones and distributing them to machine learning algorithms organized in consecutive layers. Each layer builds up on the output from the previous layer. Together the layers constitute an artificial neural network. DL is how AI can handle complex tasks and large amounts of data.
AI can be categorized into two types: Narrow and General.
- Narrow AI: These are systems designed to perform specific tasks such as voice recognition (like Siri or Alexa), recommendation systems (like Netflix or Amazon), or image recognition (like identifying people or objects in photos). They operate under limited constraints and are focused on a single task.
- General AI: This is a type of AI that has the potential to understand, learn, and apply knowledge in a broad range of tasks at the level of a human being. It’s the kind of AI you see in science fiction, like the robots in Westworld or the computer in Star Trek. However, this type of AI remains theoretical and is a subject of ongoing research.
Predicting AI Evolution from the Internet Experience
To get a true appreciation of the technological revolution around us, it helps to consider society’s experience with the internet. It’s hard to imagine that the Internet came into mainstream use less than 30 years ago. A British scientist, Tim Berner-Lee, invented the World Wide Web (WWW) in 1989 while working at the European Organization for Nuclear Research (CERN). The original idea was to create a platform to share documents more easily among scientists and universities worldwide. In 1993, CERN put the World Wide Web software in the public domain and later released it with an open license to amplify its dissemination.
The Web made the Internet more accessible and easier to navigate with the help of web browsers. 1995 is typically considered the tipping point with the proliferation of the home computer. In that year, fewer than 1% of the world’s population had access to the Internet. This figure now stands at 65% globally (~5.2 billion users); in developed regions, it exceeds 90%. Coupled with this adoption, the internet’s usage has evolved from its simple beginnings as a repository of static documents to a sophisticated business tool that can act as a platform for numerous use cases spanning both business and personal life.
It took approximately seven years for the Internet to hit 50 million users. Now compare this to the adoption rate of ChatGPT, the wildly popular chatbot from OpenAI. It took less than a month after its public release for ChatGPT to hit 50 million users. As illustrated below, ChatGPT reached one million users faster than Instagram, Dropbox, and Spotify compared to other social media applications.
Remember, ChatGPT is “just” a chatbot, representing only a single facet of AI. ChatGPT was developed by OpenAI, a private company backed by Microsoft. In late November 2023, OpenAI made this chatbot freely available to the public. As of July 2023, they offer a premium subscription for their more advanced version, ChatGPT4.
Integration of AI: How Quickly and How Creatively
Given the accelerated adoption of AI compared to the internet, could we anticipate a faster and more inventive integration of AI?
The answer is a resounding yes! This is a reality that’s already unfolding. The use of AI predates the advent of ChatGPT, with AI components embedded in numerous applications such as smartphones, social media platforms, e-commerce, streaming services, autonomous vehicles, healthcare devices, and even email spam filters. The rise of ChatGPT merely underscored this ongoing trend by drawing the public’s full attention to it.
A recent report by McKinsey & Company revealed that according to their survey, organizational adoption of AI has more than doubled since 2017, leaping from 20% to 50% in 2022. As the table below shows, AI was most commonly employed in Service Operations, Product/Service Development, Sales & Marketing, and Manufacturing. The primary use case was optimizing service operations.
In the next installment, with as little technical jargon as possible, I will add to the general overview of AI and dig a little deeper into understanding how AI works.
About the Author:
Sugata co-founded Cadence Communications & Research, a healthcare focused agency offering medical communications and marketing research services, in 2008 with Laura Smith. Sugata currently heads the market research group at firm and has spearheaded key strategic initiatives, leveraging his deep technical expertise and industry insights to drive business growth.
Sugata also served roles in consulting and market research at Andersen Consulting (now Accenture), The Wilkerson Group, Amgen, and ICI.
In addition to his corporate pursuits, Sugata has made substantial contributions to the academic realm. He co-authored “Management Consulting: A Complete Guide to the Industry” with Daryl Twitchell (1st ed. 1999, 2nd ed. 2002, Wiley). Sugata regularly shares his expertise through speaking engagements, addressing key topics like healthcare trends, marketing research, and management consulting.
Sugata received his BA in Economics (with honors) from The University of Chicago, an MA in Economics from Utah State University, and an MBA from Yale University. Sugata may be contacted at sbiswas@cadenceresearch.com.
About Cadence Communications & Research:
Cadence Communications & Research is a boutique professional services firm serving the global healthcare industry. Founded in 2008, Cadence offers services in two key interrelated areas: medical communications and market research. Cadence offers Cadence is a certified woman-owned business and has been named to the Inc. 500/5000 fastest growing private companies in America three times. Cadence is a member of Diversity Alliance for Science. For more information, please visit: www.cadencecr.com.
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