To accelerate high-value sales, Palantir hosts free, multi-day workshops for CEOs and CIOs of potential clients. By demonstrating the software's capabilities on the client's own data, they provide a powerful proof-of-concept that bypasses skepticism and dramatically shortens the enterprise sales cycle.

To overcome the cold start problem in a network effects business, especially in a conservative industry like finance, a powerful strategy is to create a coalition or consortium model. By giving early adopters ownership and governance rights, you align incentives, build trust, and transform would-be competitors into enthusiastic evangelists for the new network.

The SaaS-era advice to "do one thing well" is outdated and risky in the current AI climate. The best defense against rapid displacement by competitors or platform shifts is to build a multi-product bundle. This strategy creates a wider surface area within a customer's workflow, increasing stickiness and defensibility.

Early enterprise customers won't invest time to become proficient with a complex data tool. Founders must join their meetings, operate the software for them, and surface insights to demonstrate value. This manual "data monkey" role is crucial for driving initial adoption.

For data-intensive AI products, an initial consulting project can solve the cold-start problem. 7 Learnings' first client paid for consulting and allowed data usage to develop a separate SaaS product, as the problem was too complex for them to solve alone.

Hospitals face immense internal friction (compliance, IT security) to approve any new vendor. Once a startup is in, it's far easier to upsell them a broader suite of solutions than for the hospital to onboard a separate point solution. Product expansion becomes a powerful sales and retention strategy.

Large enterprises don't buy point solutions; they invest in a long-term platform vision. To succeed, build an extensible platform from day one, but lead with a specific, high-value use case as the entry point. This foundational architecture cannot be retrofitted later.

Point-solution SaaS products are at a massive disadvantage in the age of AI because they lack the broad, integrated dataset needed to power effective features. Bundled platforms that 'own the mine' of data are best positioned to win, as AI can perform magic when it has access to a rich, semantic data layer.

Instead of pursuing complex, open-ended consulting projects, partners can scale more effectively by creating productized, "turnkey AI" offerings for specific business units like legal or marketing. This approach lowers the adoption barrier for customers by delivering predictable results for a defined use case, making it easier to sell into departments or smaller businesses.