Abhay Parasnis, founder and CEO of Typeface and former CTO/CPO at Adobe, shares his expertise in AI strategy and enterprise sales. He explores how startups can create effective partnerships with tech giants like Microsoft and Google to enhance their market presence. Abhay discusses the importance of understanding enterprise needs and adapting to macro strategies when selling. He also emphasizes the role of consultative partnerships in AI implementation and the significance of starting small with custom data training for long-term differentiation.
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volunteer_activism ADVICE
Target Enterprise and Partner with Platforms
Target large enterprises for their budgets and pain points around content creation.
Partner with major platform players like Google or Microsoft for distribution and relationships.
volunteer_activism ADVICE
Partnering with Large Platforms
Understand a platform's strategic motivations and pinpoint their key bets, like Gemini for Google.
Show how your startup can showcase their platform's power through a compelling use case.
volunteer_activism ADVICE
Dual-Level Value Proposition
Understand both the macro strategy of large companies and individual sellers' KPIs.
Craft a value proposition that resonates at both levels, aligning with their goals.
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Abhay Parasnis is founder and CEO of Typeface, is former CTO/CPO at Adobe, and sits on the board of Dropbox and Schneider Electric. He joined Ben Casnocha, co-founder and partner at Village Global, for a live masterclass for Village Global founders.
Takeaways:
If you can break into the top tier of the enterprise market, it’s hard to dislodge you.
Big platforms like Microsoft, Google, and OpenAI are eager to prove their tech's power. If you position your startup as a prime showcase for their platform, you become a strategic asset.
When selling to enterprise, understand the company's macro strategy in the market, but also know what the on-the-ground person needs to close deals.
With the rise of agents and agentic automation, even the workflow layer isn’t safe. As these systems gain richer context, they’ll redefine automation in SaaS. Just like today’s platform vs. app debate, tomorrow’s battle will be legacy workflows vs. agents.
Success in AI isn’t just about top-tier products—it’s about being a consultative partner to help them with change management.
There’s a common fear about embracing a custom data training strategy — it’s expensive, complex, and feels like the domain of big players. But starting narrow and accumulating distinct data early is key. It drives real AI differentiation and builds critical internal muscle in engineering and product.
Value-based pricing means customers are less price-sensitive because it ties directly to their top-line business, unlike commodity pricing, which faces constant cost pressure. Instead of charging per word, image, or seat—models that AI disrupts—pricing should align with outcomes, ensuring long-term sustainability.
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