Why AI ROI Stalls: The Case for Disciplined Decisions
AI initiatives often underdeliver because organizations never define which decisions AI should actually improve — a fixable focus problem, not a technology gap. Summit Partners’ Sharon Lin argues that generative AI has lowered the cost of analysis so dramatically that many teams now face “analysis paralysis at scale,” producing endless dashboards and scenarios while struggling to act. She warns against automating away interpretive, relational work — consultative sales, creative brainstorming, customer insight — where friction itself carries signal. The limiting factor in AI-enabled organizations is rarely access to insight or technology; it is the discipline to identify which decisions AI can genuinely improve and the willingness to protect space for human judgment.
For multi-site operators pouring budget into AI tools, the lesson is to start with decisions, not software. Value comes from narrowing where AI is applied and preserving human judgment on high-stakes, relationship-driven calls — not from generating more analysis nobody acts on.
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Why do so many AI initiatives fail to deliver ROI?
Most AI initiatives underdeliver not because of the technology but because organizations never define which decisions AI is supposed to improve. Summit Partners’ Sharon Lin frames this as a focus problem — one that is more fixable than leaders assume once they tie AI to specific, high-value decisions.
What is “analysis paralysis at scale”?
It describes a failure mode where AI makes generating analysis nearly free, so teams keep producing one more dashboard, scenario, or deck before committing to a decision. The volume of insight outpaces the organization’s capacity to act, slowing decisions rather than accelerating them.
Which business activities should leaders avoid automating with AI?
Interpretive, relational, and context-dependent work — consultative sales conversations, creative brainstorming, and moments of genuine customer insight — where friction often carries useful signal. Lin argues that protecting human judgment in these areas is a sign of organizational maturity, not a failure of AI adoption.