Is ChatGPT a welcome addition to the digital toolkit or a harbinger of unemployment and misinformation? How can companies best position themselves toward the former? 

Ultimately, service and product-based companies will be impacted by AI in different, but similar, ways, because both have been built in the information age around knowledge work.

The real question is, what's next?

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Moving beyond the information age

Let’s start by answering the question I posed in the title of this article. In the case of friend vs. foe, my initial verdict would be something like “frenemy.” Now, if I ask Google about the term frenemy, I get information about its meaning, etymology, an explanation of portmanteau, “9 Ways to Spot a Frenemy,” etc. When I ask ChatGPT where I’ve heard this term before, it confirms for me that it appeared in the 2001 film Legally Blonde.

The difference here is illustrative. In one instance, I am delivered a smattering of information, and it’s up to me to make something of it. In the other, I get an answer, plain and simple.

We have been living and working in the information age. Anything you could ever want or need to know is out there, and our tools, the way we work, and the value we provide to our clients and customers have all been tied to the knowledge of how to work with that information. It started with simple websites. Click and scroll. Then came the virtual remote controls (otherwise known as iPhones), bringing all that information into your pocket. Information became more and more accessible, but you still had to navigate it yourself (or hire someone to help you).

Now, we find ourselves with a knowledgeable co-pilot that takes all the information and gives us one answer. What we have is not only a new tool but a new way of interacting with the digital world. Take SEO, for example. What does the web mean when it is based on answers rather than a conglomerate of search results? Search, and the way we optimize for search, will fundamentally change. First, we shape the tools, then the tools shape us. 

It’s important to note that this transformation has been underway for some time now. No-code, for instance, was already democratizing the web, taking specific domain knowledge and applying it in a way where people could work with it. But AI has accelerated this type of transition 100x — bringing us out of the information age and into one of wisdom. 

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Welcome to the age of wisdom

Information is not knowledge.

Knowledge is not wisdom.

Wisdom is not truth.

Truth is not beauty.

Beauty is not love.

Love is not music.

Music is THE BEST.

‍– Frank Zappa

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The advent of AI has marked the end of the information age and will prompt a steady decline in the value of knowledge work. As we transition to the age of wisdom, it’s no longer enough to know how to build something, because increasingly, the machines know how to build those things, too. Instead, having the wisdom to know what to build will be much more valuable. 

The information age called for knowledge workers.

The knowledge age will rely on wisdom workers.

What does this mean for business? Currently, we see companies that either sell products (tools) or services (time). Product-based companies build tools that augment processes to repeatedly solve a customer’s problem. Service-based companies, on the other hand, bring processes augmented by people to repeatedly solve a client’s problem through project-based delivery.

But when knowledge becomes a commodity, the collective moat of product and service companies starts to disappear. As we exit the information age, knowledge work that is repeatable becomes replaceable with AI. 

The good news here is that we are at the dawn of hyper-cycles of innovation. Moving forward, businesses will be able to bring tools and time together to create wildly tailored solutions for customers. These super companies will combine processes, people, and technology to yield hyper-customized solutions. 

And it’s already happening. Take a service-based sector like accounting. 

At PricewaterhouseCoopers (PwC), 30% of the data and analytic practice staff are now experts in data science and AI. Bloomberg has been following a similar path, building their own AI model, “a large language model specifically trained on a range of financial data to support a diverse set of natural language processing (NLP) tasks within the financial industry.” In both examples, the ability to process information and turn it into knowledge has been systemized at the business’s core, creating opportunities to augment those capabilities according to a customer’s specific needs.

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Productize today, thrive tomorrow 

Here at Edgar Allan, we’ve been taking a lot of the processes we’ve used throughout the years and turning them into products. Last year, we created Letter Run, a magical clubhouse built with Three.js in Webflow. This wild Webflow idea ended up requiring us to write a lot of very specific JavaScript, which made us realize how useful it would be to have a tool to do that for us. So we went to work developing that tool. Enter Slater, a coding environment designed specifically for Webflow that uses AI to prompt a large language model to write Javascript. 

Our takeaway? If it’s repeatable, abstract it into something sellable.

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What does this all mean for businesses and marketing teams?

AI models and their applications are evolving at a rapid rate, meaning that companies will have to become more dynamic in how they think about serving their customers. From customized chatbots to sophisticated assistant models, the possibilities are ever-expanding. As we move from content management systems to true knowledge management systems powered by LLM (large language models), customer enablement is taking on a whole new meaning. The companies that can work with their customers closely and quickly in hyper-cycles will win out in the end.  The more we can bring these elements together, the faster we’ll elevate the entire market — and reshape how we deliver value to clients.

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