The AI Revolution: A CDIO’s Personal and Professional Odyssey

Growing up, one of my childhood favorite television shows was Peewee Herman’s Playhouse. The premise centers on a quirky and child-like character, Peewee Herman, and his adventures in his magical playhouse. Every show started with a “word of the day,” to which Peewee and the show burst out into animation, dancing, and screaming. I play this scene out in my head every day when I read about the technology word of the year: Artificial Intelligence. 

There is no debate about the worthiness or value connected to the hype of AI. It’s here to stay, and the story of its impact on society, work, and individuals is still being written. Rather, this piece is centered on stimulating dialogue about its origins, sharing my personal experience with it, and what AI means for us at Insight Global. Let’s dive in. 

The First Impression 

My first impression of AI lasted a lifetime. I was a sophomore in college at Penn West California, located just outside my hometown of Pittsburgh, PA. At the end of my freshmen year, I switched my major from electrical engineering to computer science. 

That switch kickstarted my learning of software programming languages. One of those early languages I learned was called LISP, which stands for List Processing. LISP was founded on the mathematical theory of recursive functions and was created at Massachusetts Institute of Technology (MIT) by one of the founding members of artificial intelligence, John McCarthy. It always carried a close tie to the early development of AI.  

LISP really sparked my intrigue in AI and its impact on technology. The concept of machines emulating human intelligence fascinated me. Like most things, understanding something new requires a step back into its original creation. Hence, my studies in college did that and, as a level set for this blog, let’s do the same.  

Origins of Artificial Intelligence 

I did an unofficial public poll a few weeks ago on LinkedIn asking who are the founder(s) of Artificial Intelligence? There was at least one vote for each of the four respective choices, which supports the perspective of how the answer to this question varies based on perspective. Lots of great inventors, developers, and creators had a hand in getting AI to where it’s at today. 

The most common answer (59%) aligns with most sources that site Alan Turing as the founder of Artificial Intelligence in 1950 thanks to his paper Computing Machinery and Intelligence. Turing was a great theorizer of AI. 

The second most common answer (17%) identified Allen Newell, Cliff Shaw, and Herbert Simon as the founders. These three earn the credit for turning the theory of AI into practice with the development of the logic theorist program. This was a computer program designed to mimic the problem-solving skills of a human.  

Coming in a close third place (16%) in the poll was John McCarthy and Marvin Minsky. The logic theorist program was presented in 1956 at the Dartmouth Summer Research Project on Artificial Intelligence (DSRPAI), which was hosted by John McCarthy and Marvin Minsky. This historic Dartmouth conference was an early attempt of a collaborative effort across the disciplines to standardize the development of artificial intelligence as a practice. As the hosts, John and Marvin are quoted as AI founders.  

Rounding out the poll options was Steve Jobs and Bill Gates (7%). While these two are technology titans in their own rights, mainly for pioneering the personal computer era while building two of the most iconic and valuable organizations in history. Unfortunately, they were not old enough to be in the debate of AI founders, but the companies they help build are on the short list of AI game changers that are taking AI to new levels in the current digital age.  

Digital Revolution Fuels Artificial Intelligence

During the 1990s and early 2000s, the “digital revolution” started to really take hold. It was in this era that widespread adoption of personal computers, the internet, and digital communication technologies started to transform society. It was also in this era that I made my pivot in college mentioned earlier. I fell in love with the ability to solve problems using code. I never learned a foreign speaking language, but I could talk C, C++, Fortran, COBOL, and this new coffee language called Java. My new languages guided me to my first professional employment at State Farm Insurance to be an IBM mainframe COBOL software developer.  

Let me be frank about this: I HATED COBOL software language. It didn’t speak to me creatively. It did, however, capture the history of programming, was not very technically challenging), and it forced me early on in my career to focus on understanding problems of the business as an anchor to what we were working on in the technology department. COBOL set my bedrock for connecting technology decisions and business decisions, and that mindset holds true today with AI.  

Eventually, I got out of COBOL development and into Knowledge Base Systems, also known as Expert systems. These systems were first developed in the 1970’s and represent the earliest successful forms of AI software. My new job was to support the creation of these systems at State Farm! I LOVED this job. It combined business needs with intriguing technical delivery. Fast forward 20 years, and look at where we are at with AI!

And as Chief Digital and Information Officer at Insight Global, I have the privilege to sit at the helm of working hard to use AI to run the business in serving the needs of our clients and consultants.  

Artificial Intelligence at Insight Global 

Leading AI efforts, it’s important to start with a clear definition the entire organization understands. It’s also important to set a clear goal of what you want AI to do.  

At Insight Global, we define artificial intelligence as a machine-based system that can for a given set of human defined objectives, make predictions, recommendations or decisions influencing real or virtual environments. We use AI in ways that serve our purpose and aligns with our shared values.  

To enhance this alignment, we established four AI principles in no specific priority order: 

  • Privacy & Security: AI at IG will incorporate data privacy and security principles intended to protect the safety of everyone. 
  • Human Oversight: AI at IG will be built with appropriate human direction, governance, and control. 
  • Fairness: AI at IG will be built, managed, and used with fairness to all parties in mind. 
  • Explainability: AI at IG will be used in ways that should be explainable and easy to understand.  

With this framework hammered down and clear vision set across the organization, we’ve focused our use cases on:  

  1. Driving productivity and operational efficiency across our teams 
  2. Strengthening relationships with clients and consultants 

Both goals must be kept in mind.

Use Cases at IG 

We have found that automation and AI has helped us provide premier human connection to our concierge service model toward staffing and professional services. We’ve used a mix of homegrown and purchased solutions to enhance productivity and efficiencies for our front, middle, and back-office operations. 

One example of that coming to life is in connecting great candidates to our open job requisitions. We have a database of hundreds of thousands of candidates at any one time. Our recruiters have gotten great at digging through our own database (on top of searching for passive candidates) to find the right candidate for a certain job, but our AI model accelerates and enhances matching candidates with clients based on their skills, experiences, and preferences. This helps us reduce the time and effort required to find the best fit for each role. It also improves the quality and satisfaction of our placements for both candidates and clients. We’ve seen a statistical increase in efficiency for the time it takes for us to match candidates to the needs of our customers. 

Candidate placement is another AI use case in our business. We use machine intelligence to help our clients by identifying when a candidate might back out of their commitment before their start date. This is common in the staffing industry for many reasons. For example, when candidates may have limited contact with a recruiter leading up to their start date, or there are consistent shifts to their start date, the AI model will flag a candidate as a potential backout. We then address those issues head on.  

This model also helps us flag any discrepancies or errors that might indicate fraud or misrepresentation. Our recruiters can then triple-check and address any issues that this model calls out. 

AI Is Here to Stay. How Will You Use It? 

By leveraging automation and AI, we have improved our productivity, efficiency, quality, and security across our teams and operations. We believe that AI is not a threat, but an opportunity to enhance our human capabilities and connections, and we are excited to explore more possibilities and innovations in the future. 

Consider how your company currently uses AI and how it can affect business priorities. Is it currently doing that? Will it free up your workforce’s time to focus on other critical matters?  

Not only are we on our own AI journey, but we also have a full professional services division at Evergreen dedicated to helping companies strategize and implement their own AI strategy. Fill out the form below if you’d like to find out more!

DeWayne Griffin is the Chief Digital and Information Officer at Insight Global. Connect with him on LinkedIn.

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