The Rise of Knowledge Systems

Over the past 20 years, I’ve watched software evolve from glorified digital spreadsheets into dynamic engines of business logic. We’ve moved through waves—each one a step forward. First came data-entry systems like early Salesforce and SAP. Then orchestration platforms like Marketo, HubSpot, and Workato, automating the if-this-then-that logic of multistep workflows.

Each era moved us closer to software that could do more. But here’s the truth: despite all the progress, software hasn’t made us smarter—it’s just made us faster at clicking through systems. We’ve optimized process. We haven’t touched thinking.

Now, we’re entering a fourth wave. And this one’s different.

It’s the rise of knowledge systems—a new software layer that doesn’t just store or process data, but understands context, retrieves insight, and activates knowledge in the moments that matter. Knowledge systems are AI-native infrastructure built to do what previous generations of software couldn’t: make sense of unstructured, tribal, often messy information, and turn it into precise, timely action.

This isn’t hype. It’s already happening.

Morgan Stanley built an internal GPT-4 assistant for its 16,000 financial advisors to query decades of proprietary research—resulting in 98% adoption and better-informed client conversations. Accenture deployed a company-wide AI assistant trained on its institutional IP to help consultants find proposals, project examples, and benchmark data in seconds. These aren’t “chatbots”—they’re contextual intelligence layers, woven into daily workflows, returning ROI traditional software couldn’t dream of.

This shift is why we at Upfront have been going deep on knowledge systems. Some companies we’ve backed under this thesis:

• Writer: An enterprise-grade platform to deploy and manage AI agents trained on your company’s data—grounded, secure, and audit-friendly. This isn’t a “write me a blog post” tool; it’s an agentic foundation layer for enterprise AI use.
• 1Up: Turns scattered product docs, security policies, and Slack threads into a sales-ready AI that can handle RFPs, technical questions, and compliance requirements with the fluency of a top sales engineer—at scale.
• Endgame: Think of it as your memory extension for GTM. Endgame connects your CRM, email, call recordings, and product usage data into a living knowledge graph that helps your team prep for meetings like they’ve been selling to the account for months.
• Sandbar: A personal knowledge system born out of wearables and context capture coming soon!

What ties these together is this belief: in a world drowning in content and data, context is the new compute.

It’s not about having more dashboards. It’s about surfacing the right insight at the right time—inside the tools people already use—so they can act faster and think better.

In this paper I’ll be breaking down this opportunity where we will cover:

• What Are Knowledge Systems and Why Do They Matter?
• How Modern Knowledge Systems Transform Work
• The Taxonomy of Knowledge Systems: Vertical, Departmental, Tribal, Personal
• Common Patterns and Infrastructure of Knowledge Systems
• Productizing Knowledge Systems: Interfaces and User Experience
• Investment Implications: The Case for AI-Native Knowledge Platforms

If the last era of software was about automating tasks, this next one is about amplifying thought. And that changes everything.

As someone who’s been both an operator and an investor, I’ve seen how rare it is to find software that can scale thinking. That’s why I believe knowledge systems aren’t just a new feature—they’re the next platform layer. The new system of intelligence.