Artificial intelligence (AI) is rapidly transforming how we all live, work, and learn. It will undoubtedly impact every industry, redefining how leaders in the business of lifelong learning need to think and act. One company helping organizations, particularly marketers, do this is Marketing AI Institute.
Marketing AI Institute’s mission is to make artificial intelligence approachable and actionable. And leading that mission is founder and CEO, Paul Roetzer. Paul is co-author of Marketing Artificial Intelligence: AI, Marketing, and the Future of Business as well as the creator of the Marketing Artificial Intelligence Conference (MAICON) and the AI Academy for Marketers.
In this episode of the Leading Learning Podcast, co-host Celisa Steele talks with Paul about his definition of artificial intelligence, why he thinks we’re at a tipping point with AI, the 5Ps of marketing AI, and use cases where a learning business looking to leverage AI for marketing might focus first.
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[00:00] – Intro
Curiosity About Artificial Intelligence
[01:21] – Tell us about Marketing AI Institute and your role.
Paul’s educational background isn’t related to artificial intelligence, but he was curious and felt he had an opportunity to help other people make sense of AI and its implications. That was the origin of Marketing AI Institute.
Today it’s a media event and education company that makes AI approachable and actionable to marketers, primarily, but more and more its businesses as a whole.
[02:43] – What first got you interested in AI in 2011?
IBM Watson won on Jeopardy in February 2011. That intrigued Paul, and he wanted to figure out how it worked.
He had a very specific use case in mind—whether you could use AI to build smarter marketing strategies.
Defining Artificial Intelligence
[04:18] – How do you define “artificial intelligence”?
Many don’t understand what AI is or agree on a definition. Paul spent years trying to find a definition that he thought made sense and that could be explained to non-technical people. He came across a definition from Demis Hassabis, CEO of DeepMind, an AI research lab that Google purchased in 2014:
AI is the science of making machines smart.
Think of the software you use to do your job. It never gets better unless new features are released in the software, and then you have to learn how to use the new features. The software never does anything that you don’t tell it to do. Most software we use in marketing, sales, business, and learning is human-powered. Humans write the rules to tell the software what to do.
AI is the science of making machines (or software) smart, so that the software learns on its own. It adapts and evolves what it does and what you’re capable of doing based on information it gathers. Rather than telling the software what to do, AI can learn from new information, and it can improve its recommendations, predictions, and output based on that information.
A Tipping Point in AI
[07:00] – You’ve talked about it as being a tipping point in AI (this interview was recorded in February 2023). Explain what you mean by that and why you think we are at a tipping point.
Paul thought we reached an inflection point in the middle of 2022 when OpenAI released DALL-E 2, an image generation technology. It was free initially, and then it was $15 a month. You could give a text prompt, and it would generate an image. That was an important turning point because DALL-E 2made AI tangible.
We use AI dozens, if not hundreds, of times every day with Netflix, Spotify, Amazon, Google, etc., but we don’t think of it as using an AI tool.
DALL-E was the first moment where anyone could use an AI tool and be in awe.
Then, on November 30, 2022, ChatGPT came out, and it blew DALL-E out of the water. ChatGPT is the fastest-growing consumer technology in history. It was the right tool at the right moment with the right interface.
But it changed everything. The average person now knows what ChatGPT is, and people from all walks of life and all business backgrounds have tried it. I get the texts from family and friends that I didn’t even know knew I worked in AI about like “I used this tool, and, oh, my gosh, do I have a job in six months?” It changed everything. It made AI accessible and, to some degree, understandable.Paul Roetzer
[10:04] – How would you characterize the current state of marketing AI, and where you see marketing AI headed?
For a few years, Paul has said that in three to five years at least 80 percent of the things marketers do every day would be intelligently automated to some degree. Recently, he’s changed that prediction to the next two to three years.
And Paul thinks even that revised estimate might be conservative. It’s not unrealistic to think that in one to two years, 90+ percent of what we do will be assisted by AI.
ChatGPT changed the urgency for other major companies like Microsoft, Google, Meta, and Amazon to release the AI that they have sitting in their research labs.
In the very near future, AI is going to be baked into every customer relationship management (CRM) system and will assist writing. We’re going to see an accelerated adoption curve for AI in a lot of technology.
Sponsor: WBT Systems
[12:35] – We’re grateful to WBT Systems for sponsoring the Leading Learning Podcast.
TopClass LMS provides the tools for you to become the preferred provider in your market, delivering value to learners at every stage of their working life. WBT Systems’ award-winning learning system enables delivery of impactful continuing education, professional development, and certification programs.
The TopClass LMS team supports learning businesses in using integrated learning technology to gain greater understanding of learners’ needs and behaviors, to enhance engagement, to aid recruitment and retention, and to create and grow non-dues revenue streams.
WBT Systems will work with you to truly understand your preferences, needs, and challenges to ensure that your experience with TopClass LMS is as easy and problem-free as possible. Visit leadinglearning.com/topclass to learn how to generate value and growth for your learning business and to request a demo.
Marketing Artificial Intelligence: AI, Marketing, and the Future of Business
[13:37] – You co-authored a book published June 2022 called Marketing Artificial Intelligence: AI, Marketing, and the Future of Business. What prompted you to write it, and what do you hope to achieve with it?
Paul initially tried to write the book in 2018. He was clear on the beginning of the book (how we had arrived at where we were with this 70-plus-year-old technology that is AI).
But he realized he didn’t know the middle part of the story—how AI was being used in the moment. Looking for examples and talking to software companies, marketers, chief marketing officers (CMOs), and other marketers, Paul had a “Fermi paradox” moment. Fermi looked out at the universe and said, “Where’s all the intelligent life? We have all these stars, all these galaxies, and yet nothing.” That’s how Paul felt about AI. It seemed like it should be everywhere, yet everywhere he looked, it wasn’t.
This made Paul think he couldn’t write the book yet because he wasn’t sure if the story was there yet. Then in 2021, he read Genius Makers by Cade Metz, which made clear why the Fermi paradox moment was happening for him in 2018, and why the tech wasn’t there yet. The breakthroughs hadn’t happened yet that have enabled what we’re now seeing today.
By late 2021, Paul was ready to take a shot at telling the story because he thought it was critical for the industry and the business world at large that a more approachable, non-technical text existed that was written for marketers and business leaders. The challenge was how to write something knowing the technology was going to make parts of the text obsolete almost immediately. He made a strategic and intentional effort to write a book that would stand the test of time.
AI Limitations and Opportunities
[16:54] – What are some of the primary hurdles, obstacles, and risks to AI? What are some of the biggest advantages and opportunities if organizations embrace and use AI to develop, promote, and sell their learning products and services?
Limitations and Obstacles
There are limitations in the technology itself because AI doesn’t know facts. Currently, ChatGPT has no idea of facts. It’s not connected to the Internet, and it doesn’t have knowledge of anything new since the end of 2021. The models work by predicting words, so they have no idea of facts and no context in which to process information.
There’s both an unrealistic expectation and underestimation of what AI is capable of doing. People don’t understand the breadth of the impact AI is going to have on learning, marketing, sales, services, product development, learning management systems, etc. Some of the limitations are that people just haven’t built it yet.
Then there are more macro-level limitations. Some of the big organizations have more advanced technology but won’t release it because of fears of misuse, bias, and ethical concerns about the responsible use of AI.
There’s also the internal issue of very few people truly understanding AI. There’s a massive lack of understanding and education in all industries. This is a fundamental problem because nobody on the business side really understands it, and that makes it heard to build business strategies around.
Advantages and Opportunities
[21:03] – Right now, there’s a first-mover advantage to figure AI out before everybody else because this will produce not incremental but magnitude gains. In May 2022, Paul wrote a thesis that said “the future of business is AI or obsolete.” The basic premise is that every business in every industry will either on of these three:
These are new companies built from the beginning with AI in mind to be smarter.
These are established organizations that move quickly to infuse AI into the marketing, sales, services, products, and other aspects of their business to make it smarter.
These organizations are irrelevant. It won’t happen overnight, but over time their ability to remain competitive will crumble.
Paul sees the opportunity for those that become AI-emergent:
[Y]ou don’t have to flip a switch and become all AI overnight. You don’t have to solve all of this right away. But you can learn the fundamentals of this stuff applied to business in your industry, in your focus, in weeks. You don’t have to go back to school. You don’t need a degree in this stuff. It’s just learning to look at problems differently, learning to look at how to run your business in a smarter way.Paul Roetzer
The 5Ps of Marketing AI
[23:16] – Can you explain the 5Ps of marketing AI?
Back in 2016 and 2017 when Marketing AI Institute was getting started, they were trying to categorize vendors of AI technology. They started seeing five buckets where the technology fit.
Paul has never felt those five buckets were always going to be the way to categorize everything, but it helped him in the early days develop clarity around the different macro-level ways AI could be applied in business. They’ve continued to reference the 5Ps, but they aren’t fundamental today to how they position AI.
Getting Started with Marketing AI
[26:26] – If a learning business is looking to get started with leveraging AI to help it market its educational products and services, what would you recommend? Are there obvious places or use cases to start with?
Paul recommends starting with smarter, more intelligent, automated versions of what you’re already doing. The best way to look at it is using a use case model. In a spreadsheet, list the technical things you do to create or market your courses. Look at how many hours a month you spend doing those things, the tech you’re using, and how much you’d value having AI help with it. If you’re investing a bunch of time in something, that might be a good place to start.
So start with what you already do. The easiest way to understand the impact of AI is to look at a current workflow or process. Pick very tangible things where you can be clear on whether it worked or not. Don’t get a bunch of AI tools just to say you’re using AI.
[29:28] – Is picking use cases where you’re spending a lot of time and thinking about an AI tool that might help you reduce that effort how you get to be an AI-emergent business?
That’s how you start, but there are two parallel paths.
One is the use case model, which Paul thinks about as quick wins with very narrow specific use cases. He recommends you do them in four-month sprints. Give yourself 30 days to assess the use case, evaluate a few different technologies, pick the tech to use, and get onboarded. Then allow 90 days to run it and see if it worked or not.
Then you either keep the AI tech or get rid of it at the end of 90 days.
Maybe you’ll build to having two or three of these use-case projects going on simultaneously. You’re constantly testing, not committing long term.
The second path is a problem-based model. This is where you look at larger-scale issues in your organization. Look at problems, and then figure out if there’s a smarter way to solve them using AI.
You want to walk both paths. You want to have quick wins with the use cases, and you want to work on bigger problems. Paul generally advises one to two bigger-problem projects per year per business function.
[31:47] – How would a learning business help its team understand what they need to know to start leveraging AI for marketing?
This is the problem they’ve been trying to solve at Marketing AI Institute. They’ve thought through the learning journey of someone being asked to figure out AI for the company. They’ve tried to devise step-by-step learning journeys for those people.
You’re going to need (particularly if you’re a bigger team) some dedicated AI internal training program. That could be a collection of existing paid and/or free online resources.
When piloting the “AI for Marketers” course (which Marketing AI Institute created as a step-by-step journey and launched in December 2022), they had a lot of individual learners trying to figure AI out for themselves because they saw an opportunity. Then they start to see organizations looking at leveling up everyone.
Again, we’re talking about online learning and learning professionals here. We all get it. You can go after the individuals and try and create change agents, or sometimes you get lucky and you get the people who have the vision to transform organizations and teams. I think we’re at the stage with how quickly this is all going to move. People have to have a vision to transform and upskill teams. It’s going to be essential. You can’t hire these people. You have to upskill people.Paul Roetzer
Lifelong Learning Habits
[34:33] – Do you have habits, practices, or resources that you draw on for your own lifelong learning? And what role does AI play in your learning?
Paul is a huge believer in the importance of ongoing learning. After college, once he found a purpose, he became intentional about he learned. He reads a lot, and his learning includes a heavy mix of audio books and podcasts. Audio books force him to learn in downtime. Then, if the book is good, Paul buys the digital version, rereads it, highlights and makes notes, and then exports the notes to synthesize them and uses that for inspiration.
Twitter is Paul’s primary learning vehicle for AI because many of the leading AI researchers and scientists are active there. He connects a lot of dots about the state of AI and where it’s going by listening to what they’re saying and then trying to understand why they’re saying it.
[37:21] – Anything else before we wrap up?
We’re talking to people who teach for a living and help other people learn. To me, curiosity is the key to learning, and it is absolutely the key to understanding AI. If you have some level of curiosity—maybe this is the first time you’re really sitting down and listening to something about AI—just pick the part of it that seems fascinating to you and pursue that thread. You don’t have to learn all this stuff. You don’t have to be able to give a talk like this or anything. Just find the part that’s relevant to your domain and pursue it. Pick one thing, and I think you’ll find that it probably holds a world of potential for you in your business.Paul Roetzer
[38:12] – Wrap-up
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