4-Step Framework for Breaking the AI Adoption Barrier

Why don’t you use AI more often?

I was discussing this question with my co-founder Yuriy over lunch recently.

My first instinct was to blame a lack of interest among the team. But the truth runs deeper.

After thinking and talking about it, I came to the conclusion that the real barrier isn’t disinterest – it’s disbelief.

Many people still see AI as a fancy toy or research tool with no day-to-day applications. And the applications it does have require lots of work, training, and iterations.

Then there’s the learning curve. It can feel so steep that many give up, deciding it’s easier to just do things the old way.

But this creates a self-fulfilling prophecy. By dismissing AI as too complex or impractical, we miss out on its potential to transform our work.

To break through this mental barrier, try this simple 4-step framework.

TLDR

• The goal: Break through the mental barrier preventing AI adoption
• The tactic: Pick an annoying task and try to create a working AI solution in 1 week
• The result: See if AI is a practical tool for real work and not just a technological novelty

Step 1: Pick a problem that eats up your time and energy

The first step is surprisingly simple yet critical: Pick a task that’s draining your time, energy, and resources.

It should be something that’s constantly frustrating you or taking more time than you want. This could be anything from formatting documents and cleaning CSV spreadsheets to scheduling meetings and writing cold emails.

The key is to be specific. The more specific you are about your problem, the easier it will be to tackle it with AI.

Don’t think “I want to be more productive” or “improve customer service.” Instead, zero in on a concrete task like categorizing support tickets or responding to customer inquiries.

If you’re struggling to identify a problem, try asking yourself these questions:

• What tasks do you repeatedly procrastinate on?
• Which tasks make you sigh or groan when they appear in your backlog?
• Which routine tasks prevent you from focusing on strategic tasks?
• Which tasks do you wish you could delegate but can’t?

As you narrow down your list of problems, you should look for problems that are:

1. Regular enough to be worth automating
2. Clear and defined in scope
3. Currently taking up significant time or energy

Step 2: Try to use AI to automate it

Once you’ve identified a problem, it’s time to see if AI can help.

For now, there’s no need to find the perfect fit. Instead, start with the most accessible AI tools and language models, then experiment.

This means platforms like ChatGPT, Claude, or Bard.

They’re great starting points because they don’t require coding knowledge, and they can handle a wider range of tasks.

Here’s a simple way to start:

• Break your chosen task into smaller steps
• Look for parts that are repetitive or follow a pattern
• Describe these steps to an AI tool and see what it suggests
• Test the AI’s solution with a small sample of your work

For example, if you’re automating email copywriting, you might:

1. Feed the AI some examples of your typical emails
2. Ask it to identify a sales framework or pattern in your writing
3. Provide the AI with data about a few specific leads
4. Tell it to create sample emails

Don’t expect perfection on your first try. 

At this point, the goal is to simply find out if AI can handle a small part of your task. 

Step 3: Get dirty and hack as much as you need

Perfection is the enemy of progress, especially when you’re learning to work with AI.

Don’t get caught up in trying to build a polished solution right away. Rather, embrace the messy reality of early automation attempts.

By getting dirty and hacking, I mean finding quick solutions that work even if they’re not pretty.

Maybe you’re combining AI outputs with manual steps or copy-pasting between multiple AI tools.

Here are a few examples of perfectly acceptable AI hacks:

• Running the same prompt through the same or different AI multiple times and picking the best output
• Building a library of good prompts to reuse
• Combining outputs from different AIs to get the result you want
• Using 1 AI to check the output quality of another AI

Most AI workflows start as rough experiments. 

It’s not that different from building a chair with duct tape and string. It might not be pretty, but if it holds and gets the job done, you’re on the right track.

After all, you can always refine your process later on, but first, you need to prove that AI can help your problem.

So long as the solution saves you time and energy compared to your old method, it’s working.

Step 4: Give yourself a week

One week is the sweet spot for this experiment.

It’s long enough to see results but short enough to maintain focus and momentum. There’s just enough pressure to spur you to push through obstacles without getting discouraged.

This 1-week deadline serves another purpose: It prevents perfectionism from creeping in and taking over.

Without a hard stop, it’s easy to lose yourself in tweaking and adjusting without ever putting your solution into practice.

Result

Remember, the goal of this framework isn’t to build a perfect system in a week.

The goal is to prove to yourself that AI can help you get your real work done.

And if you see the benefits firsthand, you’ll likely find yourself looking for other opportunities to deploy AI in your daily tasks.

It’s safe to say that this comes from our own experience. It’s actually the reason why we decided to build AiSDR in the first place. Yuriy tested AI for sales emails and saw potential in the results, which inspired us to create a sales email solution.