Defeating the Blinking Cursor Issue with AI

** This was written by a human and final polish provided by AI *

We've all seen the headlines. We watched as panicked investors sent some AI stocks tumbling last week. "95% of organizations are failing in their AI projects"—really?!

That wasn't my experience. But I have noticed something else: people are still searching for use cases. Users struggle to see where AI could transform their workflows. I see teams constantly fighting for the next victory, only to find that previous wins don't guarantee momentum.

Why are organizations failing?

You guessed it—the blinking cursor problem.

It's the blank canvas that paralyzes the artist or the empty page that stops the writer cold. In creative work, we often struggle with where to even begin. When AI was sold to us, it came wrapped in magic—a tool that can do anything. The last technology that promised such versatility? The smartphone. Even that took years to master and required a massive "there's an app for that" campaign.

The reality is harsh: a basic LLM chat interface might get consumers to pay $20 monthly and enhance their personal lives, but that won't drive enterprise success.

My Solution

In my role, I build frameworks around the chat experience. I augment AI with datasets and instruction sets that deliver predictable results businesses need to keep moving forward. I transform complex processes into well-documented, executable mini applications.

That's why MIT's failure rate statistics seem so foreign to what I accomplish daily.

How Did I Get Here?

Curiosity

If you lack this trait, I have bad news about your ability to harness generative AI effectively.

One of my favorite examples demonstrates how curiosity makes all the difference. I call it the story of the "twice crème'd brûlée."

It was Super Bowl Sunday. I was juggling three entrees and a dessert in the oven. I left the crème brûlée in too long. The result? The eggiest, most curdled mess you could imagine. My dessert was ruined.

Or was it?

There was no reason not to ask: can it be saved? I queried my LLM, and it suggested scooping the mixture from the ramekins into a bowl, hitting it with an immersion blender, then passing it through a fine mesh sieve, back into the ramekins and re-chilling before sprinkling sugar and hitting it with the blowtorch.

Not only did this save the dish, but honestly, the twice crème'd brûlée was the finest I'd ever tasted—so airy and light. You'll have to take my word for it since I forgot the after photos.

Approach each LLM session with this same level of curiosity.

Effort

I hear constant complaints about the banality of generative AI content. I believe this stems from insufficient effort in building and using these tools. It's been captured in the flood of derivative content flooding our feeds as well as AI slop that immediately has us clicking next.

But something remarkable happened when we built our tools. We gained the ability to ingest market research into personas and create communication rules based on that data. We've always understood how to write for our audiences, but it was varied and anecdotal. Now we could apply hundreds of pages of data to influence our voice precisely.

This required significant work for each audience we added, but it goes a long way toward creating quality content that resonates. If you want to stand out in the crowd you will need to do work with your data and with your instructions on voice.

Problem Solving and System Innovation

Another common refrain: "AI isn't suited for XYZ task." This often results from inflated expectations meeting lazy implementation (back to effort again).

I frequently hear people defining success as "100% final assets." For marketing especially, this is an incorrect assumption. I also sometimes see teams drafting minimal prompts while expecting miraculous results which is unrealistic.

Two approaches have consistently worked:

No-code interfaces: Whether saving extensive prompts in documents (creating effective "runbooks") or using no-code platforms to build business logic with plain-language instructions, this has been critical for organizational repeatability.

Break down complex tasks: Larger tasks face multiple issues: hitting context limits, LLMs forgetting earlier instructions, and various hallucinations. When you encounter this behavior, the AI isn't incapable—you're giving it too much at once.

Ever tried assembling furniture and skipped ahead in the instructions? I'm terrible at following sequences. Breaking tasks into sequential components and stitching them together transforms the impossible into possible. It's like training to do 100 pushups versus attempting 100 on the spot. Impossible tasks often result from failure to prepare and position yourself for success.

Closing Thoughts

Will everyone win this battle? Probably not. There will always be people struggling with emerging technologies. But I can attest it's possible to succeed. Once you start conquering use cases and developing your methodology, it becomes remarkably transferable to new challenges.

We must remember as fantastic and magical as this tool appears, it remains a tool. You need to ensure your team understands how to use it effectively and when to apply it strategically.

The blinking cursor doesn't have to win. With curiosity, effort, and smart problem-solving, you can turn that blank canvas into your competitive advantage.

The question isn't whether AI will transform your business—it's whether you'll be ready when it does.