Inside the Minds of Startup AI / SaaS Board Members

• •be aware of how to connect with different bots
• •fostering workplaces where strengths are recognized and valued
• •validating ai solutions for real problems
• •maximize behavioral data and minimize business riskNew
• •leverage analytics, ai, and data for customer journey

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• •cybersecurity issues compounded by ai and quantum computing
• •crms often fail to meet user needs
• •managing the risk of hallucinations with ai tools
• •internal governance of knowledge across ai tools is a concern
• •high cost and difficulty of hiring and training logistics staff

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• •speed of regulatory implementation across regions
• •80% accuracy/sufficiency standard - example cited for auto-response delegation
• •improved productivity (faster, less errors)
• •engagement (correlated with closing rates)
• •number of legal entities a job is run on

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• •assume good faith protocol: level-sets decision-making by assuming all parties working toward same goal regardless of experience disparityNew
• •get at bats: use widely available ai in business as broadly as possible, knowing not all experiments bear fruit, to gain experience before technology matures
• •finding what's interesting: ai trained to identify unique value from data rows
• •bold founder + real problem solving = investment worthyNew
• •deal-by-deal analysis then aggregate pattern identification - understand bottlenecks before scaling solutionsNew

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• •lack of verifiable knowledge based on experience
• •just ticking the box for compliance without guaranteeing safety
• •companies claiming innovation when purely scaling existing approaches
• •ai approaches that only solve 'little problems' or 'narrow problems'
• •building features without observing how real users actually need and adapt themNew

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