Use the continuous improvement loop of Autoresearch not for novel research, but to grind through operational finance tasks like invoice matching and expense reporting. This can be sold as a service or software with a clear ROI: "we cut your AP expense time in half."

The true power of AI agents lies in full-cycle automation. An agent can be built to scrape customer pain points for ad ideas, generate creative, publish campaigns via API, analyze live performance data, and then automatically reallocate budget by disabling underperformers and scaling winners.

Use Autoresearch to automate experimentation at a massive scale. This allows an agency to offer a compelling value proposition: running hundreds of tests for the same price as competitors who only run a few, leading to faster optimization and better results.

Joe Lonsdale advises established SaaS companies to go on offense with AI. Instead of merely defending their core product, they should build AI agents on top of their platforms to automate customer workflows. This creates new, high-margin revenue streams by helping customers reduce headcount and increase efficiency.

The business model is shifting from selling software to selling outcomes. Instead of creating a tool and inviting users, create pre-trained agents that perform valuable work. Then, invite companies to a workspace where this 'team' of AI employees is ready to start delivering value immediately.

Simply adding a generative AI co-pilot is now table stakes for SaaS companies. The founder argues the next evolution is 'agentic AI' — systems that don't just provide insights but autonomously perform tasks and make decisions for the user, like qualifying and actioning a sales lead.

Package pre-configured Autoresearch loops to solve a single, painful problem for a specific niche, like an Amazon listing optimizer or an email tuner for realtors. Sell it as a simple, automated monthly subscription service.

OpenAI's partnership with ServiceNow isn't about building a competing product; it's about embedding its "agentic" AI directly into established platforms. This strategy focuses on becoming the core intelligence layer for existing enterprise systems, allowing AI to act as an automated teammate within familiar workflows.

Instead of being a standalone feature, LLMs provide the most value when subtly integrated into existing workflows. YouTube's AI summaries or its ability to extract a parts list from a DIY video are examples of enhancing the user experience without being disruptive.