Lattice Now Tells You Who's Really Using AI (vs. Who's Just Talking to It)
Every enterprise has an AI adoption dashboard. Most of them measure the wrong thing.
Token counts, login frequency, prompt volume — these tell you who is using AI tools. They say nothing about who is using them well. That gap between activity and impact is where billions of dollars in AI investment quietly disappear.
Lattice thinks it can close that gap. At Lattiverse 2026, held June 10 in San Francisco before more than 2,000 HR and people leaders, CEO Sarah Franklin unveiled AI Leverage Insights — a workforce intelligence capability that connects AI usage data directly to employee performance outcomes. It is, by Lattice's account, the first people platform to link AI usage quality to measurable business results.
The Problem With Token Counts
Most AI adoption tracking amounts to a vanity metric. Companies know how many seats are active and how many prompts are flowing, but they cannot answer the question that matters: is any of this making people better at their jobs?
"Token counts are a quantity signal, not a quality signal," Franklin said at the keynote. "For the first time, people leaders can answer not just 'how much is AI being used?' but 'are we getting value?'"
That distinction — quantity versus quality — is the design principle behind AI Leverage Insights.
What AI Leverage Insights Actually Measures
Rather than tracking raw usage volume, AI Leverage Insights surfaces performance data, manager feedback, and output signals alongside AI tool adoption metrics. The system is designed to help HR leaders and managers distinguish employees who are using AI to produce higher-quality work from those generating volume without creating value.
The practical implication: a people team can identify which departments, teams, or roles are converting AI access into better outcomes — and which need coaching, better tooling, or different workflows. It shifts the AI adoption conversation from "are people logging in?" to "is this investment paying off?"
AI Leverage Insights is expected to roll out later in 2026.
Lattice MCP: Bringing Performance Context Into AI Tools
The second major announcement — and arguably the more technically significant one — is Lattice MCP, the first performance management platform to implement the open Model Context Protocol standard.
MCP is a protocol that lets AI assistants pull structured context from external systems. With Lattice MCP, managers can access reviews, 1:1 notes, feedback, goals, and progress updates directly inside the AI tools they already use — including Claude, ChatGPT, Glean, and Slack.
The idea is straightforward: if a manager is preparing for a performance conversation and asks their AI assistant for help, that assistant now has access to real employee data rather than working from a generic prompt. Sophie Hurcombe, Lattice's Chief People and Operations Officer, framed the stakes directly: "When managers have real context at their fingertips, reviews stop feeling like a compliance exercise and start feeling like the meaningful conversations they were always meant to be."
Lattice MCP is available in early access for North American customers now.
Three More Features Worth Noting
Lattiverse 2026 also introduced:
AI Agent in 1:1s — A coaching agent that sits inside one-on-one meetings, pulling relevant goals, recent feedback, and prior conversation notes to generate suggested discussion points. Most managers walk into 1:1s underprepared because assembling context from multiple systems takes longer than the meeting itself. This agent eliminates that prep tax, keeping conversations focused on progress and blockers rather than status updates.
Evidence-Based Review Drafts — Instead of a blank text box at review time, managers get a pre-populated draft built from documented goals, peer feedback, project outcomes, and 1:1 notes accumulated throughout the cycle. The pain point is recency bias — managers defaulting to the last two weeks because they cannot recall six months of context. Evidence-based drafts ground every evaluation in a longitudinal record, producing fairer reviews with less effort.
Voice Modality — Managers can now speak to the Lattice AI Agent conversationally rather than typing prompts, enabling coaching interactions that feel like talking to a colleague rather than querying a database. This matters because many frontline managers operate where typing is impractical — warehouse floors, retail locations, clinical settings. Voice access lowers the adoption barrier for the managers who need coaching tools the most.
These features collectively signal Lattice's bet that AI in HR should be deeply embedded in existing workflows rather than bolted on as a separate tool.
What This Means for HR Leaders
For HR leaders, the urgency is straightforward: organizations that figure out how to measure AI's actual impact on work — not just its adoption — will be better positioned to demonstrate ROI and course-correct before tool spend becomes a sunk cost.
For CHROs and people leaders, the immediate takeaway is operational. If your current AI metrics only measure activity, you are flying blind on ROI. Lattice is making a product bet that linking usage to performance data is the next frontier in workforce intelligence. Whether or not you use Lattice, the framework matters: measure impact, not inputs.
Three things to do with this information now:
Audit your AI adoption dashboard this week. Pull up whatever metrics your team tracks for AI tool usage. If every metric is an input measure — logins, prompts, tokens, sessions — you have a measurement gap. Identify at least one output metric you can tie to AI usage: cycle time on deliverables, quality scores, or manager-rated performance improvement.
Ask your AI tool vendors one direct question: "Can you show me the correlation between usage and outcomes?" Most cannot. That gap is your leverage point in renewal negotiations. If a vendor only reports seat utilization, they are selling you an activity tracker, not a workforce intelligence tool.
Build a simple AI impact audit for one team. Pick a department with high AI adoption and compare its performance metrics against a similar team with lower adoption. You do not need a sophisticated platform to start; you need the habit of asking whether usage is translating into results. Lattice is productizing that question, but the discipline of asking it is free.
From Post-Hire Intelligence to Pre-Hire Screening
Lattice's AI Leverage Insights tackles the post-hire side of the equation — understanding who uses AI effectively once they are in the role. But there is an upstream question that hiring teams are increasingly asking: can you evaluate AI collaboration skills before someone joins?
This is where chat-native screening tools add value. OVI's ATS uses two purpose-built AI agents — Sora for sourcing and Milo for structured audio-chat screening — to probe how candidates think about and work with AI tools during the hiring process itself. At $99/month on the Starter plan, it gives recruiting teams a way to assess AI fluency as a practical skill rather than a resume keyword. Together, platforms like Lattice and OVI represent the emerging stack for organizations serious about building AI-capable teams: screen for it at entry, measure it continuously after.
Lattice's AI Leverage Insights and MCP were announced at Lattiverse 2026 on June 10, 2026, in San Francisco.