Lattice Brings Performance Data Into AI Workflows With MCP and Leverage Insights
The News
At its annual Lattiverse conference in San Francisco, Lattice unveiled two new product lines that aim to close a gap most HR technology vendors have so far ignored: the distance between the AI tools employees use and the performance data managers need to evaluate whether that use is actually working.
Lattice MCP (Model Context Protocol) is the company's implementation of the open MCP standard, connecting performance records — reviews, 1:1 notes, feedback, goals, and progress updates — as live context inside AI tools including Claude, ChatGPT, Glean, and Slack. Sarah Franklin, Lattice's CEO, told the conference that this means managers no longer have to reconstruct context from memory or hunt through systems when preparing for performance conversations.
Alongside MCP, Lattice introduced AI Leverage Insights, a workforce intelligence capability that links AI usage data to performance outcomes. Rather than measuring token counts — a volume metric Franklin called 'a quantity signal, not a quality signal' — the feature surfaces whether employees using AI are producing higher-quality work, as rated by manager feedback and output signals. 'For the first time, people leaders can answer not just how much AI is being used, but whether it's delivering value,' Franklin said.
Why It Matters
For talent acquisition leaders, Lattice's announcement matters because it signals a shift in how the C-suite will evaluate HR technology investments. If Lattice — a performance management platform — can link AI usage to outcomes, expect CFOs and CHROs to ask the same question of their ATS, CRM, and assessment tool vendors. The era of buying AI features on faith is ending; vendors will need to prove that their AI tools are producing measurable improvements in quality of hire, time to competence, or recruiter productivity.
It also raises the bar for internal conversations. When a TA director goes to the board to request budget for an AI-powered sourcing tool, the first question may no longer be 'how many candidates will it surface?' but 'how will we know if it's working?' That is a fundamentally different procurement conversation, and TA leaders who prepare for it now will have an advantage.
Taking Action
All action is situated in your corporate context. These are starting points, not prescriptions — choose what fits.
Connect
Who to talk with in your enterprise
Your head of people analytics — ask them whether your organisation currently measures AI adoption against any quality signal, or only usage volume. Lattice's AI Leverage Insights provides a reference model for what a quality-signal approach looks like.
Your HR technology procurement lead — review your current ATS and assessment vendor contracts. Do any of them offer outcome-linked metrics for their AI features, or are you buying on trust alone?
Your CHRO — brief them on the Lattice announcement ahead of any upcoming budget conversations. C-suite awareness of outcome-based AI measurement is rising fast, and your team should be the one framing the question, not responding to it.
Inspect
What to take a closer look at
Audit which of your current HR technology vendors offer any form of outcome-based AI measurement — not just feature lists but evidence that their AI tools improve quality of hire, time to fill, or recruiter efficiency. Specific tool to look at: your ATS's analytics or reporting module — check whether it surfaces any AI-specific metrics beyond adoption rates or usage hours.
Reflect
What to think about more deeply
The most interesting question isn't whether AI tools improve hiring outcomes — it's whether we have the measurement infrastructure to know the answer in the first place. If 76% of recruiters say they expect to replace their primary recruiting platform within two years (Employ, 2025), what does that say about how little confidence we have in the data those platforms currently provide?