Before Building Your AI Solution: Start with the RIGHT Problem

According to a  RAND report, private sector investment in AI grew 18 times more between 2013 and 2024, and over half of mid-sized companies have already deployed at least one AI model.
Managers feel pressure to ‘do something with AI’, but most struggle to translate the leader’s ambition into action (AI transformation).

"AI adoption is almost everywhere today"

The biggest mistake? Organisations focus more on using cool technology than solving actual problems for their users.
If you’re solving the wrong thing, even the most advanced AI won’t help – it might make things worse 😐.

🧠 First Understand the Problem:

• Don’t build a tool based on AI just because it’s trendy.
• Stop and think if AI is truly needed for this problem, because not every problem requires AI.
• Define the problem clearly. Like in each project, think: What user pain point are you trying to solve?

🔍 Use Research & Design Approaches:

• Ensure that technical staff understand the project purpose and domain context.
• Focus on data quality first – not having high-quality data available to train your model will give you ineffective AI models.
• Resist the ‘shiny object’ syndrome – avoid bias toward the latest technology when simpler solutions work better.

🤖 AI Is Not One-Size-Fits-All:

• Choose the right AI model (if needed) – choosing the wrong tool could hurt your product.
• Avoid overengineering solutions.

🧩 Map Current Workflows:

• Before automating, understand the current user journey – how is the user currently trying to solve the problem?
• Which step can AI automate or improve?
• Consider what skills users need to address the larger problem.

🧪 Test Early with Prototypes:

•  Use tools like Google AI Studio, or run a “Wizard of Oz” test.
• Validate assumptions with real users to uncover hidden problems.
•  Think About Diverse User Experience: Different users face the same problem differently, due to language, location, and habits. Align AI behaviour with real user intentions. Make it natural, safe, and ethical.

⚡ Key Takeaways for AI Product Managers:

• Define the primary goal clearly – What should your AI system do for users?
•  Consider sub-goals and alternatives.
• Prototype early
• Test AI concepts before building.

AI can be a powerful tool in helping users solve problems. But before building an amazing AI solution, take a step back. Spend some time analysing the problem, understanding the users, and considering the context. That’s how you build AI that helps.