What I Learned Getting AI-Certified This Summer
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Artificial Intelligence (AI) is big. It is transforming the economy globally, and many people fear that it will weigh on jobs. Yes, it will weigh on jobs, but it may create some jobs. Most are worried that jobs will be eliminated, and some will. The key is that it will change a lot of jobs.
I have long been a believer in AI, but the common interface, ChatGPT, has really been bad from my perspective. I asked it about "attractive" cannabis stocks, and I did not like at all the response that it gave me. I also asked it who my wife is, and it got the name right but it got confused and described my cousin, who shares the same first name as my wife. I like Claude, part of Anthropic a lot more, but it makes some mistakes too.
I have been looking for a job for a while now, and am still looking. My best interview was by an AI robot! AI robots certainly have a role. I consider a lot of AI applications as very helpful and better than a human's involvement. Companies care about AI as a "threat" or an "opportunity" in a big way, and investors, in my view, get carried away on the topic of AI. I say "investors" hesitantly, as some investors are overly concerned that AI is a threat or that it is an opportunity, but it's the traders who really are loud about it. The breakdown in certain software stocks as well as the massive run in semiconductor stocks is good evidence of the theme of AI-related themes being present in the stock market.
Hoping to learn more about it and to aid my job hunt, I signed up for this program from Babson College through edX, which is owned by 2U, which bought it from Harvard and MIT in 2021 (for $800 million). I passed the two classes and earned my certificate this week:
The Courses
To get this certificate, two courses were required, Analytics for Decision Making and AI for Leaders. I decided to take the Analytics course first, as it sounded like the backbone. I am glad that I did. The courses were set up for several weeks of study, but I completed them very quickly ( a module per day rather than per week). My review of each of them follows.
The outline of the course, which is also part of Babson's Financial Decision-Making for Leaders program (one of four courses), indicated there were four weeks. I did these in four days:
Data Distributions and Sampling
Avoiding Fallacies in Quantitative Reasoning
Predictive Analytics
Customer Analytics
The program, which is designed to be self-paced, was from May 2018, but, not to worry, as this is pretty classical stuff. I enjoyed the review, as I had extensive exposure to statistics in high school and college. The four professors involved with this from Babson, who all reported to the leader, Rick Cleary, did a fantastic job of helping to identify and describe different data types and data distributions and to analyze sample data to make inferences about an unknown population (which is what AI tried to do, using the quantitative methods!).
To me, this was the big part of the course, the "new" stuff. I quickly realized that the video was shot in 2019, both ahead of the pandemic, which did change the world in many ways, and way ahead of now. The good news is that it was still very useful. The course was taught by the leader, Thomas Davenport, and two other instructors, Barry Libert and Megan Beck, both part of AIMatters. I started this program on July 10th and finished it on July 13th.
The program included five modules:
Machines and Artificial Intelligence
Data for AI
Platforms and Networks
PIVOT - A Five Step Process for Transformation
Capstone Project
I think the big point of this course was to explain how the world has changed with AI, with companies transforming from the sellers of goods and/or services to being part of a platform that includes consumers, producers and other partners and that has connectivity through the internet and mobile phones. Business leaders must learn how the new system works, what it means for their company and how to get there.
I think that the professors at Babson were excellent. I did encounter a few things that I think they can do better with this course, which I shared in a review, but kudos to edX and to Babson. For those who are looking more into AI, there are 69 courses and 7 certificate programs at edX that offer professional certificates. The Babson program that I am reviewing is not included here, but it is a professional certification. edX offers 608 different certification programs, and it offers 3729 courses. These programs and courses are offered by over 250 educational partners.
Anthropic's Claude 101
My daughter's boyfriend told me about another class that I should take when I let him know about the Babson Certificate, which was in process still, and I took Claude 101, which is free of charge and offered through Skilljar, in just one day. I was already a huge Claude fan, and I learned that what I use, the free version, has stronger siblings:
Pro
Max
Team
Enterprise
The course also demonstrated that there are lots of ways to access Claude, and there are lots of models behind it, including Opus (most complex), Sonnet (everyday use), and other models with Extended Thinking Settings. The ways to access it are:
Claude Code
@Claude (an AI assistant)
Claude Design
Microsoft 365 Connector (Claude integrates Excel, Word, PowerPoint and more)
The course covered a lot, and some of what it covered was for things that are beyond the free version that I use. I liked, though, that I could learn how teams or entire organizations could use it.
This course is spread over four modules that include the introduction (Meet Claude), projects (Organizing Your Work and Knowledge), Using Claude (AI) across businesses and using it for deep research (Expanding Claude's Reach), Case studies and examples of ways to work with it (Putting It All Together) and a conclusion.
There are many more courses available from Anthropic regarding Claude, including several on AI fluency. I plan to take an important one in my view, AI Capabilities and Limitations, and AI Fluency: Framework & Foundations, which helps to learn about effective, efficient and ethical collaboration.
Why AI Is So Critical
I see many comparisons in the media between AI and the internet evolution, but the two are quite different in my view. AI has been around for a while, and it currently is extending upon the internet. One of the biggest parts of this program to me was the explanation of how the ideas of platforming and networking have taken off and how companies are trying to enhance services, make better decisions and create new products. Reducing headcount has not been a priority, though these days the media is all over this.
Amazon was cited as a company that has been in a virtuous cycle of integrating data into new products. Understanding data, and how it has changed and how key it is, is a big part of the course for leaders. I found Megan Beck's discussion of platforms (module 3) to be most interesting. Everyone, both the leaders and followers and investors, needs to understand how business models are changing and why automation and AI are essential. She cited Airbnb, Amazon, Apple, Twitter (now X), Uber, Yelp, and YouTube as examples driven by the desire of quick gratification of desires by consumers and technology changes. The new model includes the company, using AI, data and a user interface, and consumers, producers and partners.
Leaders (and followers and investors) need to understand that there are new asset types. Historically makers of goods and services have had a lot of physical assets, but now companies can leverage money and intellectual property as well as networks. People are an asset! Historically, a company has been measured by its hard assets, but intangibles are much more important now. Amazon was compared to Macy's, with the open platform of Amazon explained as well as its investments in technology and networks. For leaders, the idea is to start small, introducing new business models.
Libert did a terrific job of explaining PIVOT, a 5-step process to pinpoint gap to becoming enabled as part of a platform, to take inventory of technology, data and networks, to validate a business case, to organize and operate a team and to track its performance. He pointed out why investors pay up so much relative to revenue for goods makers, service providers, financial companies, digital companies and platform companies. He contrasted GE and Amazon, and this is important. I can remember how loved GE was in the past. It built a conglomerate, but there was no talk of the interconnection between the companies. Jack Welch was a star! Amazon has been investing in technology, which costs money, to build a platform business with partners.
One of the big takeaways for me was how companies need to create an AI division that works across the company rather than within different business units. It's key to these organizations that their leaders have unique offerings, a good go-to-market strategy and revenue model with the right team and organizational structure, a good time-line and a solid financial plan.
Things are changing! AI is not perfect, but it is moving in the right direction to get better. Even if it were to match humans, we must remember that humans aren't perfect. AI will never be perfect, and this is where I think people are already erring. In my view, a good leader will take the steps that this Babson course instructed, but they will realize that they must remember that AI is not perfect. I sure appreciate what companies like Mercor are doing: paying "experts" to help make their model better. They aren’t the only ones doing this, as it is a major theme.
AI is important, and its adoption can and is helping the world get better in many ways through the creation of powerful platforms. The areas I think can evolve the most and help us include education, finance, legal, media, manufacturing, and retail. Information can help in so many ways, and processing the information more efficiently and completely can aid each of these industries. I hope I find a spot with a company to help it transition and to both protect itself and to capitalize on opportunities.
