In AI acquisitions, a startup's underlying technology is less important than its "workflow proximity." Atlassian's AI head advises buyers to assess how deeply a tool is integrated into a user's fundamental daily tasks. A tool central to a core workflow is far more valuable and defensible than a specialized, peripheral one.
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Before launch, product leaders must ask if their AI offering is a true product or just a feature. Slapping an AI label on a tool that automates a minor part of a larger workflow is a gimmick. It will fail unless it solves a core, high-friction problem for the customer in its entirety.
The term "AI-native" is misleading. A successful platform's foundation is a robust sales workflow and complex data integration, which constitute about 70% of the system. The AI or Large Language Model component is a critical, but smaller, 30% layer on top of that operational core.
Simply offering the latest model is no longer a competitive advantage. True value is created in the system built around the model—the system prompts, tools, and overall scaffolding. This 'harness' is what optimizes a model's performance for specific tasks and delivers a superior user experience.
Traditional SaaS switching costs were based on painful data migrations, which LLMs may now automate. The new moat for AI companies is creating deep, customized integrations into a customer's unique operational workflows. This is achieved through long, hands-on pilot periods that make the AI solution indispensable and hard to replace.
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.
The idea of a "one-person unicorn" is flawed. Atlassian's Sharif Mansour argues these individuals still need to architect complex AI workflows, becoming their own bottleneck. More importantly, to be a unicorn, they must avoid generic "AI slop" by injecting unique taste and process, a human-intensive task that works against solo scalability.
AI capabilities offer strong differentiation against human alternatives. However, this is not a sustainable moat against competitors who can use the same AI models. Lasting defensibility still comes from traditional moats like workflow integration and network effects.
Most successful SaaS companies weren't built on new core tech, but by packaging existing tech (like databases or CRMs) into solutions for specific industries. AI is no different. The opportunity lies in unbundling a general tool like ChatGPT and rebundling its capabilities into vertical-specific products.
To maximize AI's impact, don't just find isolated use cases for content or demand gen teams. Instead, map a core process like a campaign workflow and apply AI to augment each stage, from strategy and creation to localization and measurement. AI is workflow-native, not function-native.
Contrary to early narratives, a proprietary dataset is not the primary moat for AI applications. True, lasting defensibility is built by deeply integrating into an industry's ecosystem—connecting different stakeholders, leveraging strategic partnerships, and using funding velocity to build the broadest product suite.
