In a recent LinkedIn post, product discovery consultant Teresa Torres tackles a common misconception holding back individuals and teams from embracing artificial intelligence: the belief that their problems aren’t significant enough for AI to address. Torres argues that this perspective is fundamentally flawed, and the real barrier lies in a lack of exposure and understanding of AI’s capabilities.
Torres, co-host of a recent podcast episode with Petra Wille, highlights that the perceived absence of ‘big’ problems is often a symptom of not yet understanding what AI is truly good at. She states:
“I don’t have big enough problems to use AI for.” Teresa’s take? That’s not the problem. The real blocker is that you don’t know what AI is good at until you start using it — and you can’t start using it well until you know what it’s good at.
The ‘Exposure Gap’ as the True Blocker
According to Torres, the journey to AI adoption begins not with identifying grand challenges, but with simple, everyday tasks. She advocates for a practical, hands-on approach, emphasizing that learning what AI can do requires active engagement. This contrasts with the common tendency to wait for a perfect use case or a fully formed understanding before diving in.
Torres shares her own experience, detailing a daily habit that transformed her perspective. By consistently testing one to-do item with AI each day, she moved from feeling technologically behind to confidently integrating AI into her routine. This iterative process, even with initially ‘terrible’ results, is presented as the most effective way to build familiarity and skill.
Cutting Through the Noise: Focus on Core Tasks
A significant portion of Torres’s discussion addresses the overwhelming landscape of AI tools and technologies. She contends that focusing on sophisticated setups like MCP servers or the latest plugins can be a distraction from genuine learning. Instead, Torres advises prioritizing specific, actionable tasks to understand AI’s practical value.
“If you have a to-do list, you have problems AI can help with. Start with one. Give it 15 minutes. It’ll probably be bad — and that’s exactly right,” Torres emphasizes. This mindset encourages a bias toward action, suggesting that the initial output’s quality is less important than the learning gained from the process.
The Power of Community and Consistent Practice
Torres also points to the crucial role of community in accelerating AI learning. Whether through show-and-tell sessions, online forums, or local meetups, sharing experiences and results with others can provide valuable insights and momentum. This collaborative aspect, she suggests, often proves more beneficial than relying solely on tools or tutorials.
The core message from Torres’s analysis is clear: the path to leveraging AI is paved with small, consistent actions. By reframing the problem from ‘having big issues’ to ‘having tasks,’ individuals can unlock AI’s potential, starting with simple prompts and embracing the learning curve, however imperfect the initial results may be.
As she concludes:
You don’t need MCP servers or Claude Code to get started.
This practical advice aims to empower anyone feeling intimidated by AI, encouraging them to simply begin.
📝 About This Content
This article is based on insights shared by Teresa Torres on LinkedIn.
📅 Originally posted on June 30, 2026 | View original post on LinkedIn →