AI as a Business Consultant: How I Validated My Healthcare App Idea Using GPT-4o

Gowri Shankar is a leading AI expert in the A.Team Network. Book a consultation with an AI expert like Gowri here.

Like every over-caffeinated founder with a “revolutionary” idea, I thought I was onto something BIG: saving doctors from the never-ending doom of paperwork. 

They signed up to save lives, not to moonlight as data entry clerks, right? So, with the confidence of someone who just watched a TED Talk, I got to work. AI-powered documentation assistant? Easy. A few late nights, gallons of coffee, and some speech-to-text magic later, I had a prototype. 

The feedback? “Oh wow, this is cool!” The doctors I showed it to were intrigued.  I was pumped. Was I destined to be the Elon of healthtech? Then reality hit harder than a Monday morning. “Cool, but… will anyone actually use this?” 

At that moment, I turned to someplace unexpected: AI. This time, it wouldn’t play the role of my pair-programming buddy. Instead, it’d be my brutally honest business consultant. 

Note: This blog is part of an ongoing series exploring how I’m integrating AI into different aspects of my work. Previously, I shared how AI became my pair programmer, helping me debug complex issues like profile picture uploads. Now, I’m shifting gears to showcase AI as a business partner, pushing me to validate and refine my healthcare app idea. Note: You can also listen to the audio version on Spotify:

The Problem Statement

Before I get into how I used AI as a business consultant, let’s take a step back with two crucial pieces of context: The problem statement I was working off for my product build, and the approach I took with the prototype.

My problem statement:

Doctors lead incredibly demanding lives. Their primary focus should be patient care, yet they spend an overwhelming amount of time on documentation. Every consultation, diagnosis, and prescription requires detailed notes, often cutting into the time they could be spending with patients.

Existing solutions either lacked accuracy, required too much manual correction, or didn’t integrate well into their workflow. I believed AI could bridge this gap, but I needed more than belief… I needed validation.

My First Attempt: A Quick Prototype

I enthusiastically hacked together a proof of concept. The idea was straightforward:

1) Use speech-to-text for real-time transcription.

2) Translate into different languages for global accessibility.

3) Generate lip-synced video summaries for better engagement.

It looked promising. I shared it with a few doctors, and their initial reactions were positive. But as I probed deeper, they expressed significant hesitation:

“This is cool, but would I actually use it in a clinical setting?”

“It’s helpful, but does it integrate into my workflow?”

“How do you ensure accuracy? Even a minor mistake in documentation could be critical.”

That’s when it hit me: A cool prototype isn’t enough. I needed a real-world validation process.

AI as my business consultant

I turned to AI, not to write code, but to challenge my assumptions. I structured my interaction like a Generative Adversarial Network (GAN): I was the Generator, coming up with ideas, solutions, and execution plans. AI was the Adversary, interrogating me with tough questions, playing devil’s advocate, and pushing me to refine my thinking.

I prompted the AI to interview me like an investor or a skeptical stakeholder. No sugarcoating. No easy answers. Just hard questions designed to challenge my assumptions.

The Tough Questions AI Asked Me

Over several days, AI put me through a brutal yet insightful validation process. Here are the 10 toughest questions it asked:

1) Value Proposition: Does this actually reduce doctors’ workload without introducing new complexities?

2) Real-World Impact: Beyond the initial excitement, what makes this sustainable in a clinical setting?

3) User Adoption: Doctors are time-strapped. How do you ensure they integrate this into their practice without frustration?

4) Privacy & Security: Healthcare data is sensitive. How do you handle HIPAA and GDPR compliance?

5) Scalability: Hospitals and clinics have different workflows. Can this adapt?

6) AI Accuracy: Can the AI guarantee error-free transcription? What happens when it makes a mistake?

7) Regulatory Compliance: What regulatory approvals are required before this can even be deployed?

8) Cost vs. Benefit: Will hospitals and private practitioners pay for this? How do you prove the ROI?

9) Competitive Advantage: What makes this better than existing medical dictation and documentation tools?

10) Future Expansion: How does this evolve beyond documentation? Can it become an AI assistant for doctors?

Breakthroughs & Realizations

By the time I was done, I had a mix of breakthroughs and brutal reality checks.

Breakthrough: AI helped me pinpoint the exact pain points doctors face beyond just documentation.

Reality Check: Privacy and compliance were way more complex than I initially thought. It wasn’t just about tech - it was about trust and regulations.

Breakthrough: I refined the go-to-market strategy, shifting focus to specific medical specialties where documentation pain was highest.

Reality Check: The AI forced me to confront user adoption friction. If the tool wasn’t seamless, doctors simply wouldn’t use it.

Conclusion

AI didn’t just validate my idea… It refined and strengthened it.

Instead of moving forward blindly, I developed a clearer, more actionable strategy. AI pushed me to think like an investor, a product manager, and a user… all at once.

And this is just the beginning. In the next blog, I’ll explore another role AI can play… not just as a business consultant, but as a go-to-market strategist, and then AI as my design consultant. Stay tuned! 🚀