One of the fundamental laws of physics tells us that for something to be created, something else has to be destroyed — a principle we're experiencing in real-time. In the case of generative AI, the dismantling of one type of work begets another, challenging product and service-based companies to redefine how they serve their customers moving forward.
The information age: a retrospective
What’s the big deal? Well, the information age is coming to a close. We can trace its origins to the arrival of scrolling websites and the clunky Compaq Presarios that housed them. Before long, the information that once danced the big screens on our desks had made its way onto the smaller ones stored in our pockets and ironic fanny packs.
Now, you can look up just about anything you’d like on your phone or laptop, anywhere, anytime. What you still have to do, however, is sort through whatever it is that you find to get the thing you’re looking for. And because no one has the time or skill to sort through it all, we have tools that organize all that information for us, as well as services to activate those tools and extract value from them.
In the information age, skills and context have to be actively applied in order to craft a valuable deliverable out of raw information, whether that’s code for an application, migration of files to the cloud, or the design of a logo.
The dawn of the knowledge age
So, the information age eliminated the active gathering of data points, centering our efforts on the task of turning those points into actionable knowledge.
It made information more accessible but not necessarily more manageable.
With the advent of Open AI and Chat GPT, however, we no longer have to synthesize and sort through all that information. Instead, we’re provided with pre-packaged answers. We now have tools capable of building other tools, generating everything from code to virtual assistance and legal review.
In the knowledge age, knowledge — the awareness or understanding that you derive from information’s facts — flows freely, losing its status as a rare, hard-won asset.
The question is, if machines can now perform knowledge work, what does that leave for humans? The answer:
Whereas the information age demanded knowledge, the knowledge age demands wisdom work, or rather, insight into what we should build and how we should act on those insights.
Knowledge age or era of customer service?
The arrival of the knowledge age means a pivot irrespective of industry or job function. But it’s also great news for the customer experience, as companies can leverage their collective wisdom to iterate rapidly on tailored solutions using generative AI.
Why spend months building one website when we can quickly spin up three and test different strategic approaches?
Why waste time on the repeatable when we can home in on maximizing performance and increasing customer service levels?
Wisdom tells us it’s time.
What exactly does wisdom work entail, and how can you harness it throughout your organization to deliver value? Stay tuned for the next article in our AI series to find out.
Interested in partnering with Edgar Allan to create a branded large language model, on how to use AI in your agency, or on a web design, brand, or content design project? Get in contact with us today.
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Introduction to branded large language models
How to prep your marketing org for branded large language models
Asteroid or dinosaur - pick one (exploring the implications of AI to agencies)
Driving growth with programmatic no-code
The end of knowledge work, long live wisdom work
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Slater early-release: An open letter to the community
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