From idea to POC in just a few days

I’ve been dreaming about the future of software development for some time now. And I admit, my vision may seem like a nightmare to some of you. But listen to me.

Picture this: You write a single prompt. Something pretty simple like “Make me a text editor like Microsoft Office Word, just make it better.” You press enter, grab a coffee, maybe have lunch with your team, do some team building exercises.

When you come back, he’s there. A perfect product is waiting for you.

Software predicts everything. Understands how humans interact technology. It guides users effortlessly through complex tasks. All that’s left is to call the customer, show them your creation, and send the invoice.

This is my dream. And yes, I know we’re not even close to that reality yet.

The harsh reality of generic AI in software development

Working with generative AI tools today looks quite different from that utopian vision. Whether you’re using them for software development, marketing, or any other domain, you’ve probably noticed a gap between promise and reality.

Limiting factors are important. Let me tell you what is stopping us.

Implicit knowledge remains our biggest challenge

One of the most significant limitations of generic AI is not technical – it is human. Most of what makes great software comes from experience and intuition, not documentation.

In practice, this makes a difference:

• trust developers implicit knowledge built over the years
• Teams intuitively understand users and business context
• this knowledge Cannot be fully documented or standardized

Reluctant stakeholders create real barriers

Then there is the human element. There may be stakeholders in your organization or your customer’s organization who simply say ‘no’. “Don’t use AI. We want you to write everything by hand.”

These concerns often come from legitimate places. Security concerns keep people up at night. What if that beautiful new text editor you deployed leaks all user data into an insecure S3 bucket somewhere on the web?

These are not imaginary concerns. These are real risks we have to address.

Business complexity overwhelms current AI capabilities

Modern businesses work with complex data structures that often confuse even employees who have been there for years. How can we expect AI to overcome this complexity when humans are struggling with it?

And then there is the most serious challenge: dependency. Software components need to work together seamlessly.

When you make changes to component A, you may break component B. zoom out enterprise level, and you see external systems, APIs, processes, and compliance requirements interconnected in a way that current generative AI simply cannot handle all at once.