The ROI story is there, but it's buried
Ada has genuinely impressive numbers. We're talking 200% ROI, 83% autonomous resolution rates, 2X agent productivity gains—the kind of metrics that make finance teams sit up and pay attention. They've powered over 5 billion customer interactions across 350+ businesses in eight different industries. That's real scale, not demo-day optimism.
But here's what's interesting: all that proof is scattered across case studies and marketing pages. If you're a product manager trying to justify an AI agent investment to your CFO, you're stuck copying and pasting metrics from Ipsy's 943% ROI or Simba Sleep's £600K monthly revenue unlock into a deck, hoping your situation is similar enough for the comparison to stick.
What Ada could do—and this would be genuinely helpful—is build an interactive ROI calculator. Not some generic "enter your ticket volume" thing, but something that actually ingests your industry vertical, current resolution rate, channel mix, and shows you projections based on companies that look like yours. The data density is already there from those 350+ customers. Right now, prospects are doing mental math with other companies' case studies. Give them a planning tool they can hand to stakeholders with confidence.
Knowledge bases matter more than anyone admits
Here's something that came up repeatedly in the research: your AI agent is only as good as your knowledge base structure. Ada's platform is doing sophisticated reasoning, but if your help content doesn't have proper HTML header hierarchies (H1, H2, H3 tags) or if your information architecture is a mess, the AI struggles to retrieve the right information.
The challenge is that most teams only discover these structural issues after deployment, when their resolution rates plateau or agents start surfacing wrong answers. By then, you've lost momentum and trust.
Ada could get ahead of this with a knowledge base health diagnostic that runs before launch—something that scans your existing content and flags missing header structures, overlapping categories that confuse the AI, content gaps based on ticket patterns, and stale articles that haven't been updated since your last product launch. Prioritize the findings by impact on automated resolution rate, and suddenly teams can fix root causes systematically instead of playing whack-a-mole with content updates.
AI coaching shouldn't require AI expertise
The most successful Ada customers spend 2-3 hours weekly coaching their AI agents. That's not much time, but most organizations don't know where to focus that effort. The research was clear: AI agents require ongoing feedback to improve beyond baseline performance, but without structured guidance, teams either over-coach unnecessary behaviors or miss high-leverage improvements entirely.
What would help: a weekly coaching dashboard that automatically surfaces the top five optimization opportunities based on actual conversation patterns. Each one should show estimated resolution rate impact, number of affected conversations, and a guided coaching flow. If your AI is misinterpreting return policy questions, the dashboard surfaces that pattern and walks you through adding clarifying examples—no PhD required.
This matters because only 24% of customer service professionals currently use generative AI, but the expectation is shifting fast. Making AI management accessible to people without specialized expertise accelerates adoption and ensures the teams who invest time see measurable improvement.
What stood out
Ada's unified reasoning engine is legitimately differentiated—coaching and improvements in one channel (chat, email, voice) cascade automatically to all the others. That eliminates the siloed AI deployments we usually see and multiplies impact across the entire operation.
The business case is solid. The platform works at enterprise scale. The opportunity now is making that evidence actionable at the exact moments when prospects and customers need it most. We used Mimir to pull this analysis together, and what's clear is that Ada has the proof points—they just need to meet people where the decisions actually happen.