<ul><li>Misjudging Data Quality Requirements: Startups often underestimate the importance of data quality, which can lead to unreliable products. CTOs should establish robust data collection and cleaning processes early on.</li><li>Ignoring Ethical and Bias Concerns: Bias in AI systems can lead to flawed outcomes. CTOs should evaluate datasets for bias, audit models regularly, and be transparent about limitations and risks.</li><li>Overpromising AI Capabilities: It's important to manage expectations and be transparent about the limitations of AI. CTOs should start with smaller, tangible goals and ensure scalability once foundational models prove their value.</li><li>Underestimating Infrastructure Needs: AI can be resource-intensive, so CTOs should evaluate computational requirements early on and consider cloud-based solutions or partnerships to manage costs without sacrificing performance.</li></ul>

7 Common AI Startup Pitfalls and How CTOs Can Avoid Them

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