Building an AI-Native Company: Lessons from Ramp's Approach to AI Adoption
AI conversations often center on the latest models, benchmark scores, or breakthrough capabilities. But during a recent Enrich member session, Jay Sobel, Data Platform Engineer at Ramp, shared a different perspective: the companies creating the most value from AI aren't necessarily using better models—they're building better systems around them.
Rather than treating AI as a standalone assistant, Ramp has embedded it throughout everyday workflows, enabling employees across the organization to solve problems faster, automate repetitive work, and build their own internal tools. The session offered a practical look at what it takes to become an AI-native organization, and why success depends as much on culture and context as it does on technology.
Here are some key takeaways from the discussion. For the full transcript, log into our member portal.
1. Design AI around workflows, not one universal assistant
Ramp didn't build a single chatbot and expect it to solve every problem. Instead, the company created specialized AI tools for specific jobs, including coding agents, desktop assistants, data research tools, internal application builders, and AI-powered dashboards.
The lesson: AI adoption improves when tools are purpose-built for the work employees already do.
2. Empower non-engineers to solve technical problems
One of the most compelling examples involved a designer identifying a UI bug. Rather than filing a ticket and waiting for engineering capacity, the designer submitted the issue to an AI coding agent, which generated a pull request in roughly 14 minutes. Engineers still reviewed the code before merging, but AI dramatically reduced the time between identifying and addressing the problem.
The biggest productivity gains often come from enabling more people—not just engineers—to contribute.
3. Automate repetitive requests so experts can focus on higher-value work
Ramp's internal data agent answers natural-language questions that would otherwise consume analyst time. Employees can retrieve customer information, business metrics, or operational data without relying on the data team for routine requests.
This allows analysts to spend more time on strategic analysis instead of repetitive lookups.
4. Context is becoming the real competitive advantage
Frontier AI models are increasingly accessible to everyone. According to Jay, what separates successful implementations isn't which model an organization chooses, it's the quality of the context provided.
Well-organized documentation, structured knowledge bases, clean data, and clear business processes make AI significantly more useful than constantly switching between models.
For many organizations, investing in internal knowledge may deliver greater returns than chasing the newest AI release.
5. Remove friction to increase adoption
Ramp focused less on adding features and more on making AI impossible to ignore.
Its desktop AI assistant comes pre-installed on employee machines with company systems already connected. Employees don't need to configure integrations or decide which tool to use before getting started.
The easier AI is to access, the more likely it becomes part of everyday work.
6. Encourage experimentation, even when many projects won't survive
Through its internal platform, Rampify, employees can quickly build dashboards, automations, workflows, and lightweight applications.
Many of these tools eventually become obsolete, but that's viewed as a worthwhile investment. The objective isn't perfection, it's rapid learning.
Creating a culture where experimentation is inexpensive often leads to better long-term innovation.
Final notes:
Perhaps the biggest lesson from the conversation wasn't about AI at all—it was about organizational design.
The companies seeing the greatest impact aren't waiting for a perfect model or a fully autonomous assistant. They're reducing friction, creating better context, empowering employees to experiment, and embedding AI into everyday workflows one use case at a time.
As AI capabilities continue to evolve, those practical habits may prove to be the most durable competitive advantage.
For the complete summary of takeaways from this event, visit our membership events page on Notion.
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