Basis, Clay, and Exa Labs have integrated AI agents into their workflows, reducing onboarding time by 75% and saving an hour of daily triage. These startups demonstrate how AI can transform enterprise operations.
TL;DR
- Basis, Clay, and Exa Labs have successfully integrated AI agents into their workflows, significantly improving efficiency.
- These startups show how AI can be used to automate and optimize enterprise operations.
- The integration of AI agents allows for more consistent, repeatable, and measurable workflows.
What happened
Basis, a startup building AI agents for accounting firms, has reduced first-day onboarding from two hours to 30 minutes using AI agents. The agents provide immediate access to company-specific onboarding skills and complete integration setup in the background.
Clay, a company building a self-learning revenue engine for go-to-market teams, uses AI agents to keep deal context current and prioritize daily actions. This workflow saves roughly an hour of inbox triage each night.
Exa Labs, which builds web search infrastructure for AI agents, has automated the process of identifying and implementing high-priority integration opportunities. AI agents monitor repositories, gather context, create pull requests, and prepare weekly updates.
Why it matters
These startups demonstrate how AI agents can be integrated into various enterprise workflows to improve efficiency and consistency.
The use of AI agents allows for more measurable and improveable workflows, which is crucial for enterprise leaders looking to scale AI usage.
The integration of AI agents also leaves room for experimentation and improvement, which is essential for startups and enterprises alike.
Key facts
- Basis reduced onboarding time from two hours to 30 minutes using AI agents.
- Clay's AI agents save roughly an hour of inbox triage each night.
- Exa Labs' AI agents automate the process of identifying and implementing high-priority integration opportunities.
- AI agents can be used to teach stable processes, provide persistent context, and carry opportunities into tested action.
- The integration of AI agents allows for more consistent, repeatable, and measurable workflows.
- Enterprise leaders need to give employees room to test consequential workflows and turn the strongest experiments into repeatable practice.
Context
OpenAI's latest Enterprise Signals report shows that frontier firms, those with the top 10% of AI usage, generate 8.3× as many output tokens per active user as typical firms. This widening gap points to a deeper operating shift where leading firms connect agents to company context and tools, delegate more substantive work, and make successful workflows easier to repeat.
The integration of AI agents into enterprise workflows is a growing trend, with startups like Basis, Clay, and Exa Labs leading the way.
As AI usage continues to increase, enterprise leaders need to focus on turning depth into work people can trust, measure, and improve.