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What AI Support Actually Looks Like When It Works
Most AI support deployments fail at the knowledge layer, not the model. This session shows the architecture that fixes it, from three teams that built it.
Hosted by 
Daniel Kimber · Danielle Wilson
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What this session covers
95% of enterprise AI pilots fail to deliver measurable ROI, according to MIT’s2025 State of AI in Business Report.
But the teams getting real results, with lower ticket volume, higher deflection, and agents that actually hold up under pressure, aren't running better models. They built a better knowledge layer.
In this session, Brainfish CEO Daniel Kimber breaks down how three organizations made AI support actually work. See what they changed, how they structured the system behind it, and why they landed in the 5% that delivered measurable results.
WHAT WE'LL COVER:
- The MCP era: how Claude, Cursor, and VS Code Copilot can now search, draft, audit, and update your knowledge layer, not just cite from it
- The knowledge layer: what it looked like before, what they built, and why it changed everything the agent could do
- The connections that mattered: scattered docs, support history, and product context, unified into something an agent could actually use
- What they kept, what they cut, and what had to be rebuilt from scratch
- The results: deflection rates, accuracy benchmarks, and what the team can now handle that was impossible before
- Open Q&A: architecture, tool selection, build vs. buy, what to fix first, plus a live MCP setup walkthrough
WHO THIS SESSION IS FOR:Heads of Support. CX leaders. Anyone who said yes to the AI investment and now has to make it work. If your agent is live and underperforming, or you're deciding what to build before you deploy, this session is where to start.
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