When disrupting a market, selling enabling tools to incumbents (e.g., research agencies) is less effective than competing directly. Incumbents have misaligned incentives and are often low-intent "tire kickers," whereas their end-clients will readily switch for a better, faster, cheaper solution.
Related Insights
The primary threat from AI disruptors isn't immediate customer churn. Instead, incumbents get "maimed"—they keep their existing customer base but lose new deals and expansion revenue to AI-native tools, causing growth to stagnate over time.
Conventional wisdom suggests attacking an incumbent's weak points. Serval did the opposite with ServiceNow, targeting its core strength: configurability. By using AI to make customization drastically faster and easier, they offered a superior version of the feature that locks customers in, creating a compelling reason to switch.
Startups often fail to displace incumbents because they become successful 'point solutions' and get acquired. The harder path to a much larger outcome is to build the entire integrated stack from the start, but initially serve a simpler, down-market customer segment before moving up.
Prepared realized it couldn't win against GovTech incumbents on their terms of sales relationships and lobbying. Their strategy was to fundamentally shift the competition. By offering a free, easy-to-use product, they forced the purchasing decision to be about technology quality, an arena where they could excel.
Incumbent companies are slowed by the need to retrofit AI into existing processes and tribal knowledge. AI-native startups, however, can build their entire operational model around agent-based, prompt-driven workflows from day one, creating a structural advantage that is difficult for larger companies to copy.
Economist Bernd Hobart argues that large enterprises are too risk-averse for early AI adoption. The winning go-to-market strategy, similar to Stripe's, is for AI-native companies to sell to smaller, agile customers first. They can then grow with these customers, mature their product, and eventually sell the proven solution back to the legacy giants.
Terra Security chose to sell its AI pentesting solution directly to end customers rather than licensing it to existing pentesting firms. This strategy provides direct product feedback, builds brand equity, and creates market pressure on incumbents, forcing them to adapt or be replaced.
AI-native companies find more success selling to new businesses or those hitting an inflection point (e.g., outgrowing QuickBooks). Trying to convince established companies to switch from deeply embedded systems like NetSuite is a much harder 'brownfield' battle with a higher cost of acquisition.
Incumbents face the innovator's dilemma; they can't afford to scrap existing infrastructure for AI. Startups can build "AI-native" from a clean sheet, creating a fundamental advantage that legacy players can't replicate by just bolting on features.
A major market opportunity exists when one side of an industry (e.g., insurance companies) adopts new technology like AI faster than its counterpart (e.g., hospitals). Startups can succeed by building tools that close this technology gap, effectively 'arming the rebels' and leveling the playing field.