The MindStudio Method: A New Way To Think About Generative AI
This post is intended to introduce you to a new way of thinking about AI, and to provide you with a framework for how MindStudio can help you use AI today to work more effectively and efficiently—even if you don't know anything about AI beyond having played with ChatGPT.
Chances are you've been hearing about AI everywhere you look. It's going to take over the world, it's disrupting every industry, it's going to take our jobs, it's going to take your job, etc.
You've probably seen articles about how we need to start making more chips, or skimmed through announcements of new AI models that are filled with acronyms and benchmarks and charts and math. You might have even finally succumbed to the hype and thought "I should probably sit down and learn what this is all about," and found yourself watching a YouTube video titled something like "Transformer Architecture for Beginners," or ended up in a seminar called something like “Generative AI and Machine Learning for Business” that did nothing but leave you more confused than ever.
If this sounds like you, then I have good news: You don't need to be doing any of this. Studying the foundations of machine learning to better leverage AI in your day-to-day life is like learning about transistors and electrical engineering to better use your smartphone.
In fact, the way you should be thinking about AI is actually quite simple.
The first thing to understand is that the pace of innovation in AI is accelerating rapidly and will only continue to do so. It's impossible—and unnecessary—to try to keep up with it all. It seems like every day there are new models being released, and with them come new tools and methods, new strategies for doing things like working with documents or getting better-formatted outputs. Everywhere you look there are shiny new things.
Fortunately, all of this noise can be reduced to the following axiom: The world of AI is getting smarter, faster, and cheaper. Because of this, you can just assume that it's all going to get better with time. Instead of trying to keep up with the latest advances, you can instead focus on the foundations of what it is you're trying to do, and build out solutions in a way that allows you to take advantage of all the innovation, rather than locking yourself into a single tool or vendor.
Today, the primary value of generative AI is in automating repetitive tasks—in turning the work you do every day into repeatable workflows that can be executed in seconds—making you more efficient and letting you spend more time on the things that matter.
This is why the most successful organizations, agencies, and individuals using MindStudio all have one thing in common: they spent a lot of time observing, thinking, and tinkering before they started implementing.
We have seen this story time and time again. Someone shows up to MindStudio with grand plans to automate some giant, core piece of the work they do. Maybe it's a marketing agency, and they want to build an AI application to automatically generate copy for all their clients’ social media posts. Or it’s a company that wants to automate their entire customer support function. On the surface, this sounds completely reasonable!
Only, check back a few weeks later and you'll come to learn that the place they've actually found the most value in AI has been somewhere completely unexpected. Maybe they’ve done something like build a small AI that helps account managers onboard new clients, and from this they’ve seen productivity skyrocket. It turned out that writing copy, or engaging with customers, was actually not the place where automation was most needed or most effective. Rather, the busywork of client intake was actually pretty routine and repeatable, and it sucked up a lot of time from folks whose time would have been better spent being creative and doing differentiable work.
This is why, if you want to transform your work with AI, the first thing to do is to really take a step back and understand all the things you and your organization do. Oftentimes the high-level descriptions of our jobs fail to reflect the realities of how we spend our time day-to-day. Ask any programmer how much time they spend typing code, as opposed to being in meetings or writing documents. Or ask a salesperson how much time they spend on the phone with prospects as opposed to doing research, organizing information, and coordinating meetings.
This is the most important and time-consuming part of implementing AI in the workplace. AI helps you go faster—radically faster—but if you don't know where you're going or why, all you're going to do is end up even more off course.
Once you understand what it is you do and why you do it, the next step is to identify some things you might want to automate.
Pick one of these things—and don't be afraid to start small. Imagine that I gave you a magical remote that could automate tasks in your kitchen. You might be tempted to start off trying to program a button that makes an entire dish from start to finish, but you would quickly realize that’s too complex and overwhelming. In fact, the right first button to program might just be called "Dice Onions," because you dread chopping onions and it makes you cry every time.
Once you've programmed your onion button and made sure it works, you can continue working in your kitchen as normal—but every time you need to chop some onions, you just press the button and voilá. Soon, the time saved begins to add up, and you actually start to find cooking a lot more enjoyable now that you don't need to do a part of the job that you didn't really like.
Once you get used to working in this way—once the process of augmenting your work with magic becomes automatic—then you might start programming more buttons on the remote. “Measure X amount of Y,” “Chop X,” “Mix these ingredients,” etc.
The number of automations would grow, until eventually you would reach a point where most of your cooking was done by pressing buttons on the remote—rather than manually doing the work by hand. But it’s key to note that you can still be involved in the process where you want to be. You might want to interrupt every now and then to taste and season, or to try something different and see if it improves a recipe. This same idea applies to working with AI: it's about breaking down your work into steps, composing those steps into recipes, and then playing the role of conductor.
The key takeaway from this example is that the real skill lies in identifying and describing the repeatable steps that make up a process. You shouldn't worry about designing your workflow for a specific model or a specific tool—because by the time you finish there will probably be a better one on the market and you'll need to start from scratch.
Being able to describe work in terms of steps and structure is foundational to succeeding in the AI-empowered future of work into which we are moving. No matter how smart the models get, you're still going to need to be able to articulate what it is you actually do before they can help you do it better and faster.
If you succeed in this core exercise, then learning to use a tool like MindStudio to turn your workflows into AI apps is a piece of cake. After that, you'll be able to leverage all the innovation in AI as it happens, because you can just upgrade the models you're using as new ones become available. The foundations of what you are doing remain the same, but the execution context is able to be swapped and upgraded in real time.
Once you've built these automations and integrated them into your day-to-day work, you will come to realize something else: the nature of your work, and the relationship you have with your work, has fundamentally changed. You have shifted up to a new level of abstraction. From this new vantage point, you'll start to see new opportunities for things to automate and tune. You'll start to recognize other places where you can automate things in your job and in your day-to-day life, and you'll be able to teach this new way of thinking to your colleagues and friends.
AI-empowered digital transformation is an ongoing and never-ending process. It’s a fundamental shift in the way we think about work and business. There will always be new things to automate, new interfaces to build. You'll iterate and refine the things you've built as the technology gets more powerful, and the way you think about what's possible will change and expand. And you'll never need to learn the technologies under the hood–it will just feel like magic.
more about MindStudio: mindstudio.ai
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