👋 Hey, I’m David Stepania, and welcome to an exclusive 🔒 subscriber-only edition 🔒 of Homebase AI’s weekly newsletter. Every week, I share actionable insights, strategies, and real-world stories from founders and leaders who are transforming businesses with AI.

Join our community as we explore how to unlock AI’s untapped potential, tackle real business problems, and accelerate your company’s growth.

AI as a Knowledge Amplifier, Not a Replacement

Executive Summary

AI tools are everywhere, but the biggest challenge isn’t access—it’s adoption. Companies invest in AI solutions expecting seamless automation, only to realize that employees struggle to integrate these tools into daily workflows. According to Snow Lee, CEO of RunBear, the key to successful AI implementation isn’t just powerful technology—it’s making AI intuitive enough for non-technical teams to use effortlessly.

In this case study, we explore how RunBear is eliminating friction in AI adoption, allowing companies to deploy AI teammates in Slack and other workplace tools in just 10 minutes—without technical expertise. Based on our exclusive interview with Snow Lee, this deep dive highlights the challenges of AI adoption, the critical role of workflow integration, and why enterprises must rethink how they implement AI.

Watch the full interview below! 👇

The Problem: Why AI Adoption Fails

Non-Technical Users Are Overwhelmed

AI implementation is often marketed as seamless, yet many organizations struggle to get real value from AI tools. The main issue? Non-technical teams don’t adopt them.

Despite the promise of AI-driven efficiency, employees often revert to old habits—scrolling through Slack channels, searching manually for files, or waiting on coworkers for answers. The result? Underutilized AI investments and frustrated teams.

Overcomplicated AI Solutions

Many enterprise AI solutions are designed for developers, not business teams. They require custom integrations, coding expertise, and ongoing maintenance—barriers that discourage widespread adoption. Snow Lee recognized this flaw early on:

RunBear pivoted its strategy, shifting from a developer-first tool to a no-code AI assistant designed specifically for business teams.

The Solution: AI That Works Within Existing Workflows

RunBear solves these challenges by embedding AI directly into existing communication platforms like Slack, Google Drive, and Notion—where employees already work. This approach eliminates the friction of learning new systems and ensures AI adoption happens naturally.

• Instant AI Teammates:

Employees can create AI agents within Slack in 10 minutes—no technical skills required.

• Adaptive AI:

RunBear’s AI assistants learn from existing conversations and documents, responding instantly to common questions.

• Cross-Team Collaboration:

AI agents can collaborate with each other, ensuring IT, HR, and finance teams all benefit from shared AI knowledge.

One standout use case? LaserAway, a leading medical aesthetics company, deployed RunBear to support communication between its medical and customer support teams. By using AI to answer repetitive questions, they saved 180+ hours per week—freeing up human agents for higher-value tasks.

The Future: AI as a Knowledge Amplifier, Not a Replacement

The fear of AI replacing jobs has long dominated conversations, but RunBear proves that AI’s real value is augmentation, not replacement. AI assistants help teams move faster, make better decisions, and reduce cognitive overload—without removing human oversight.

For enterprises still struggling with AI adoption, the key takeaway is clear: AI should seamlessly integrate into existing workflows, not demand drastic behavioral changes.