Use Case First Mindset: The Most Effective Way to Speed up AI Adoption and Value

Everybody talks about AI; data shows it is being adopted “faster than PC and Internet.” However, almost 2 years after the release of ChatGPT, most people use it for a handful of tasks at work. In this article, I will share why I think we should change our focus if we want to accelerate AI adoption and introduce the concepts of the “use case first mindset” and “the virtuous cycle of AI adoption.” To keep it practical, I will offer methods and tools to get on board the “use case first mindset.” I end with a prompt template to help you discover new concrete ideas for using AI in your job. 

 AI is adopted “faster than PC and Internet,” but still conversion at work lags… 

A recent survey published by the US National Bureau of Economic Research, titled “The Rapid Adoption of Generative AI,” showed that “gen AI is being adopted faster than the PC and the internet.” This got me thinking: if this is the case, why do most employees still use AI occasionally, mostly to edit an email or a memo? Why do people use AI more at home than for work? With the often-cited 40% productivity gains from AI that are promised, why isn’t every employee making AI its primary tool for getting work done? From my experience, as of now, only a small percentage of people see a significant change in their work. These employees already use AI often for diverse tasks and drive immense value for themselves. They are still the exception. We still dim them as “the early adopters” or “super-users” and are slow to convert others to this status. There is a clear “AI-usage conversion gap”. 

This “conversion gap” is not due to a lack of awareness. I have never seen people more aware of AI than they are today. A surprising number of people can mention advanced AI terminology, discuss the risk of peril from AI at length, and show you some cool videos they generated. Some would offer fear or risk aversion as the primary reasons for the “conversion gap.” Though these exist, in my opinion, they are not the primary reason for this gap. I have another hypothesis that I would like to offer. I think we are not going about AI adoption optimally. If we want to convert more users to “heavy users,” we should adopt a “use case first mindset.” 

 The Virtuous Cycle of AI Adoption

Did you know that using generative AI at work can be addictive? In a good way, that is. After you have a few very good concrete experiences of what you could achieve with this technology to help with your work in a non-trivial way, chances are you will want to use it repeatedly. Moreover, you will look for more creative and bold opportunities to use it. With experience comes better usage and more value. Thus creating what we can call “the virtuous cycle of AI adoption.” You will also start talking about it to anyone willing to hear. Sounds good, right? So, how do we get there then?

To get hooked, the “let’s talk about how AI will change the world” training sessions are not enough. Nor do “the cool video of an AI-generated swimming baby hippo” or even the occasional memo and email writing experiences convert you. You need to have a concrete, job-specific experience with several use cases that blew your mind with how helpful they were. 

 The Accelerator: Adopting a “Use Case First” Mindset

With this virtuous cycle in mind, I believe that we need to adopt “a use case first mindset to accelerate AI adoption.” This means that whether you’re an individual or an organizational leader who wishes to accelerate the pace of or value from AI adoption, focus on job-specific use case discovery!

Here are some excellent ways to help you do just that:

1. AI training sessions focused on use cases and not generalities: 

In our “AI Everywhere Program,” the most popular course is “Generative AI in Practice,” where we move very quickly from prompting to showing multiple concrete use cases and tools every knowledge worker can adopt. The overarching feedback is that this practical approach is the missing piece for many people who stayed mainly on the “sideline” with AI adoption until that point. 

Many training platforms also include sessions of this kind. For example, on LinkedIn Learning, the following courses include many practical use cases: How to Boost Your Productivity with AI Tools by Dave Birss; Generative AI in HR. There are many other practical courses on all the learning platforms, including YouTube. Bottom line: Search for use-case-heavy courses and choose ones that share prompt templates to ease hands-on experimentation. 

2. Get peer-inspired

The best ideas are the ones closest to home and your day-to-day work. Bring the heaviest AI users to share what they use it for. Often, they have refined it to an art, working around any challenges the organizational policies, data, and tools bring their way. For example, to inspire peer learning, as part of our “AI Everywhere community,” we have started sharing a “prompt of the week,” demonstrating a good use case with an actionable prompt and where to use it each week. Then, we encourage people to come and share how they used this prompt and top it with additional augmentations and use cases they’ve tried. 

3.  Use a dedicated platform

Some platforms make it easier to share the use cases. OpenAI did it, to some extent, with their “GPTs” option, which lets you discover and create custom versions of ChatGPT. Note, though, that while it’s a good place to start, it can be less relevant if you don’t want or can’t use GPT for your task. Also, beware that the search is not great there. 

For a tool-agnostic option, one platform that has already adopted the “use-case first” mindset is Superintelligent. It's a one-of-a-kind platform that helps individuals and teams learn, discover, and share the actual use cases they use through an interactive AI use cases registry and capabilities building.

4. Use an LLM for job-specific use case discovery

Finally, I will be negligible if I do not offer to discover ideas for gen AI usages, well... with gen AI. I recommend using the following prompt (fill in your company information and job details). It works well with any AI tool or model (I have tested it on GPT, Claude, Llama, and Perplexity). 

Job-specific use case discovery template:

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You are an expert in utilizing AI at work to maximize productivity boosts, as well as discovering diverse, practical, job-specific AI use cases and refining them to be helpful for the specific individual. I am a {describe your role} at {describe your company by name or general properties}. I have the following responsibilities in my job: {describe the main things you do or are accountable for at work}. Help me create a list of at least 10 concrete but non-trivial use cases that are most likely to help me perform my job better. Start by providing a title for each use case and a brief description of the use case. Then stop and ask me: “On which of the use cases would you like to get more details?”. Once I make my choice, for each use case, add the following information: When should I use it; How can I drive value out of it; Pitfalls to be aware of; Recommended tools; and, when applicable, suggest a Prompt Template for an easy application.

{describe your role}:

{describe your company either by name or general properties}:

{describe the main things you do or are accountable for at work}:

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If you have read thus far, let’s make a pact. You hereby commit to stopping mostly talking about AI and starting to do stuff with it, not just generating hippo videos or emails but actual non-trivial use cases that will yield high value in your specific role and company today. Deal?