For the last few years, companies have been remarkably patient with AI. They have tested copilots, summarized meetings, generated emails and asked chatbots questions about their own data. Some of it has been genuinely useful. Some of it has been a very expensive way to save somebody 15 minutes. Either way, we are getting to the point where executives want to know what all of this is actually doing for the business.
That is what makes some recent numbers from Outreach interesting. The company reported that consumption of its AI credits grew 12x during the first half of 2026, while AI annual recurring revenue increased 480% year over year in the second quarter of its fiscal year. Engagement with Kaia, its AI meeting and conversation intelligence product, grew 40%. Those are big numbers, but the more important part is how people are using the technology. Revenue teams are starting to move past asking AI for information and letting it handle pieces of the work.
The Gap Between Knowing And Doing
Sales has never really had an information problem. Managers know deals are getting stuck. Reps know they should follow up with prospects. Revenue leaders know accounts go quiet and opportunities disappear because somebody missed a signal or simply got busy. Anyone who has worked around a sales team has seen this happen a thousand times.
The problem is usually what happens next. Knowing a deal needs attention is different from somebody actually doing something about it, especially when every rep has dozens of other things competing for their time. This is where agentic AI starts to make more practical sense.
Outreach has been building agents that research prospects, personalize outreach, prepare sellers for meetings and spot changes in deals. Its Omni agent lets users ask questions across accounts, opportunities, prospects and conversations, then move into an action like drafting an email. Agent Studio lets companies build workflows around things sales teams already do, including following up with inbound leads, going back to closed-lost opportunities and flagging deals that have stalled.
None of that sounds as futuristic as some of the AI demos we have watched over the last few years. That might be the point. The value is in catching something that would have been missed and doing something about it while there is still time.
SolarWinds, for example, used an Outreach win-back agent that produced a 45% reply rate, reactivated more than 100 accounts and reopened $200,000 in pipeline. Resi has logged more than 1.4 million Kaia recordings and reported a 35% win rate among its mid-market account executives. Those examples are more useful than another estimate about how many hours AI might save because they connect the technology to things companies already measure.
AI Is Moving Into The Workflow
A lot of workplace AI still feels like an extra stop. Open a chatbot. Ask a question. Copy the answer. Go back to the software where you were doing the actual work. It can be helpful, but it is still another tab.
The next version is starting to look different. Outreach has been building connections through Model Context Protocol, or MCP, that allow AI systems to access revenue data and take permitted actions inside sales workflows. Its MCP tools can work with prospects, sequences and records, which makes it possible for intelligence gathered in one place to turn into action somewhere else.
That is probably closer to how most employees will eventually experience AI. They will not spend their days thinking about which model they are using. It will just be part of the software and processes already around them, handling the repetitive stuff that tends to fall through the cracks.
That does not mean handing the entire sales process to a machine. Buyers still want to talk to people who understand their business. Managers still need judgment. Sellers still need to know when to stop following the script and have an actual conversation. AI is simply getting better at handling everything surrounding those moments.
Usage Might Be The Metric That Matters
The AI race has spent a lot of time focused on capability. Which model is smartest? Which company has the newest agent? Which product can generate the most impressive demo? Those questions matter, but actual usage may tell us more about where this market is headed.
A 12x increase in AI consumption suggests people are coming back and using these tools as part of their regular work. That is a much harder thing to manufacture than excitement around a product launch.
Revenue teams are also a good place to watch this happen because there is nowhere for the results to hide for very long. Eventually, better execution has to show up somewhere in pipeline, conversion rates, win rates or revenue. If it does not, the experiment gets harder to justify.
For years, companies have been asking what AI can tell their employees. We may finally be getting to the more useful part of this story. What happens when AI starts helping them get the work done?
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