A workflow you use regularly for tasks you do often is the best use case for piloting AI.
AI Makes Software Smarter
Most if not all who are reading this post have tried the free version of OpenAI’s ChatGPT. Its arrival in November 2022 is what sparked the Artificial Intelligence (AI) craze. But AI is not new, and it is not limited to ChatGPT.
In fact, you have been using AI for a long time. Amazon, YouTube, and Google Maps all use AI. AI is what makes your apps and software smart, and the more you use it, the smarter it gets. Through Machine Learning, AI-infused software can make predictions and personalized recommendations.
Put simply, your most repetitive online actions train the software. That's why it often seems that AI already knows what you need before you even ask the question. In the case of Gmail, for example – like a dear friend – it will offer to finish your sentences for you.
Those are examples of passive AI. Now consider the possibility of customizing AI so that you have more control over when and how AI assists you. Ideally, you wouldn’t have to wait for AI to make general predictions based on passive learning. Rather, you could intentionally train the AI to do specific tasks for specific use cases.
Ultimately, every workflow in your operations can be made more efficient by using AI-infused software. Approach the learning curve by recognizing how AI is already assisting you with some of your repetitive and data-driven tasks.
Recognize AI Assistance
For the purpose of becoming familiar and comfortable with AI, use a specific use case and workflow as a pilot project. Choose a use case that you do regularly. This will allow you to easily recognize AI's strength for handling repetitive tasks. AI is also well-suited for data-driven, predictive and generative tasks, which most workflows include to some degree. Some example use cases:
- Creating a monthly newsletter or podcast.
- Preparing periodic sales or productivity reports.
- Building lists of qualified buyers for your next marketing campaign.
- Organizing quarterly or annual events
After choosing a workflow that you’ll use to pilot your use of AI, do a high-level audit of the tech stack you use (or could use) for that workflow. Use the audit to determine:
- Are tasks mostly done manually, or is technology involved?
- What software are you using?
- Is the software cloud-based? (SaaS)
- Does the software use rules-based automation?
- Are you using the software’s AI features?
This fairly simple exercise can be a valuable first step in navigating the learning curve toward achieving AI literacy. It will help you know where you are in the process, and where you have opportunities to make quick advancements. Auditing the tech stack you use in a specific workflow will help to map out the next steps of your learning process.
Pilot AI By Exploring and Experimenting
Once you recognize how AI assistants are already at work within your workflow, you can confidently explore further. You may discover that the software you use every day has AI features you weren't aware of. Give those a try. Or you may find that the software you use every day isn't adequately infused with AI, in which case, you should explore alternatives.
Take the initiative to learn how AI can be used, but to keep it manageable, limit your exploration and experimentation to one use case . . . at least at first. Give yourself a target time frame. Could be 30, 60 or 90 days depending on the complexity of the use case. You'll start recognizing the differences between rules-based automation and intelligent automation – predecessors of predictive and generative AI.
Piloting AI is a simple but strong first step in a structured approach toward AI literacy.