How To Get Started In AI Even If You Don’t Have Technical Skills

From ChatGPT doling out medical diagnoses to chatbots saying they want to become human, the hype surrounding artificial intelligence is impossible to ignore. According to major media outlets, you can’t afford to take a wait-and-see approach. It’s easy to feel overwhelmed by the prospect of staying up to speed, even more so if technical skills aren’t your strong suit.

Do I need AI training? Where should I start? How can I find the time? To answer these questions, here’s a quick guide to getting started in AI—even if you’re not tech-savvy.

Get Some Perspective

Based on recent headlines, you might be convinced you’re light-years behind if your company hasn’t already integrated AI into its processes. According to a recent survey from EY, the speed of AI adoption is one of the biggest triggers for AI anxiety. In that same survey, polling 1,000 Americans with desk jobs, 90% said that their organization uses at least one AI technology, with Gen AI topping the list.

On the other hand, a Census Bureau survey from November of last year, which sampled approximately 1.2 million businesses in America, examined AI use across industries. While 13.8% of businesses in the information sector reported AI use, only 3.8% reported using AI to produce goods and services. 6.5% of companies planned to use AI in the next six months.

In short, not everyone is on the AI train and it certainly hasn’t left the station. What’s more, Gen AI, the most popular form of AI according to the EY survey, is also the most accessible. Tools like ChatGPT can be incredibly useful. But while prompt writing has become an art (and has spawned new job titles), leveraging ChatGPT can be as simple as conducting a Google search—it’s intuitive and easy to start tooling around with. Don’t let AI anxiety stop you from dipping your toe in the water.

Focus On The Problems, Not The Tools

You may have heard of Sheena Iyengar and Mark Lepper’s seminal Jam Study. The psychologists set up jam displays in an upscale food market. While the display with 24 varieties of jam drew more attention, it also led to fewer purchases versus a display with just six options. The takeaway: you can have too much of a good thing. Too many options can lead to choice overload and ultimately hinder decision-making.

In my book, I give readers an index of my favorite AI and automation tools. I also recommend that people check out the latest tools and applications on sites like G2. But if you’re just getting started in AI, those useful lists can start to feel overwhelming.

Harvard Business Review offers smart advice for eliminating choice overload: focus on the problem you’re trying to solve, not the AI tool.

“Wielding a (generative AI) hammer, everything starts to look like a nail. But, instead of asking how to do generative AI in your company, ask what you need to accomplish.”

The idea is to incorporate AI where it makes the biggest impact. At Jotform, for example, we use an AI-powered scheduling tool to coordinate interviews with job candidates. That frees up our HR team to spend more time and energy screening candidates and conducting the actual interviews, which we still do the old-fashioned way. HBR cites the Dutch company KLM—the air carrier uses AI to predict which passengers are most likely to miss their flights, and in turn, reduces delays by keeping their bags more accessible. But it also uses traditional analytic optimization techniques for a host of other tasks.

As you begin exploring AI, it’s a quality-over-quantity situation—start small and impactful.

Learn in practice, not in theory

One thing is abundantly clear: employees want upskilling. A recent Gallup study found that 57% of workers were “very or extremely” interested in upskilling. Interestingly, that number shot up to 71% if the training was free and offered during regular work. A growing body of research shows that employee training is more effective when offered during the workday. Employees don’t have to take time out of their personal lives. On top of that, passive learning tends to be slow and inefficient. The best way to acquire new skills is to apply those techniques and knowledge right away. That’s why I encourage our team members to find ways to incorporate new AI tools and applications into their normal workflows, even if it requires building a bit of slack into their schedules during the learning process.

Start by mapping out your workflows, listing every step that a single task requires, and looking for AI opportunities—tedious, manual steps that don’t require your personal input. Note the processes that might be ideal candidates for automation and search for relevant AI tools. This phase may take some time. It may require some discipline—building what I call an “automation machine” isn’t exactly a party. But rest assured it will be worth it once the AI tools are in place, your machine is in motion, and you reap back all of the time and mental effort (and then some).