What Drives Other AI / SaaS Board Members?

• •gaining first-mover advantage with ai adoptionNew
• •maintaining talent density and quality over hypergrowth
• •optimizing token efficiency and cost-per-query to make ai economically viable at scaleNew
• •understanding actual operational profile through data, not assumptions
• •bringing together all relevant b2b buying signals

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• •managing customer expectations and correcting misconceptions about drones
• •developer anxiety about automation replacing their roles in 2-3 year timeframesNew
• •frustration with 'shitty phone trees' and ineffective bots
• •current ai tools are often built for hackers, requiring intricate prompt setups and human intervention
• •annoying customers or not liking what customers are saying

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• •lifetime value of the customerNew
• •net promoter score (nps)
• •conversion rate
• •who's responsible for lifetime valueNew
• •revenue growth (unlocking new selling, marketing, upsell opportunities)

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• •responsibility attribution - if waymo vehicle at fault, waymo accepts liability; if other actor, follow established insurance evaluation processes
• •bridge-building approach - identify gaps between technology capability and operational requirements before implementation
• •hiring/retention decision: speed over delay — 'never had regrets getting rid of someone too soon,' quick band-aid rip vs slow attrition
• •conviction-over-validation - move fast on strong conviction rather than exhaustive pre-evidence gathering
• •build-configure model shift: 80% customization/refinement during project (vs. 20% in traditional saas) because ai learns on customer data and processesNew

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• •engaging with the cfo too late in the sales cycle
• •building a tool without solving a specific problem firstNew
• •poor data quality or 'junk in junk out'
• •expecting one 'ai engineer' hire to unlock all transformation without engineering team support
• •pitch deck isn't enough for vcs

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