Lanai Launches AI @ Work Operating System to Close the Enterprise AI Accountability Gap
Lanai, an enterprise AI accountability company, has announced the general availability of its AI @ Work Operating System—a first-of-its-kind platform designed to help organizations discover, measure, and optimize every AI workflow across their business.
As enterprises accelerate AI adoption, many struggle with visibility, accountability, and measurable ROI. Lanai’s new platform addresses these challenges by providing a centralized system to track AI performance, evaluate business impact, and guide smarter investment decisions.
Why Enterprises Need AI Accountability
Modern organizations running AI workflows often face critical issues such as:
- Rising token spend without clear ROI
- AI agents operating without oversight or governance
- Difficulty attributing business results to specific AI workflows
Lanai’s AI @ Work Operating System shifts the focus from simply tracking AI usage to actively managing AI performance. It connects AI-driven workflows—whether human-assisted, copilots, or autonomous agents—to key business metrics like:
- Pipeline velocity
- SLA attainment
- Engineering productivity
- Capacity gains
Unlike traditional tools that measure isolated productivity, Lanai delivers portfolio-level visibility across sanctioned tools, shadow AI, and autonomous systems.
Turning AI Activity into Measurable Business Impact
“AI has moved from experimentation into day-to-day operations, but most enterprises still lack visibility into how it’s being used or where value is being created,” said Lexi Reese, Co-founder and CEO of Lanai.
Lanai’s mission is to make enterprise AI transparent, measurable, and actionable, enabling leaders to:
- Identify high-performing AI workflows
- Scale successful initiatives with confidence
- Eliminate underperforming AI investments
Key Features of Lanai’s AI @ Work Operating System
1. Comprehensive AI Workflow Detection
Lanai uses edge-based detection through browser extensions, endpoint agents, and tool integrations to capture AI activity across all environments—including unsanctioned tools—without relying solely on vendor APIs.
2. Workflow-Level Capacity Insights
The platform measures time saved by AI by comparing tasks against human effort benchmarks, giving leaders a clear view of capacity gains and productivity improvements.
3. Workflow-Centric Adoption Metrics
Instead of tracking tool usage, Lanai analyzes how AI-powered workflows are adopted across teams, helping organizations pinpoint where AI is delivering the most value.
4. Queryable AI @ Work Graph
Lanai provides a powerful, queryable data layer that connects AI insights to systems of record like Salesforce, GitHub, and Zendesk. Leaders can access metrics such as adoption, impact, and capacity through natural language queries.
Proven Results Across Enterprises
Lanai is already working with Fortune 500 companies across industries such as SaaS, fintech, healthcare, and restaurant technology.
Key insights include:
- 65% of employees using AI fail to report actionable outcomes
- AI-driven renewal preparation saves 4.5 hours per sales rep at 36% adoption
- Engineering teams achieve 1.4x productivity gains, compared to 1.1x in sales
These insights help organizations understand:
- What’s driving performance
- Where efficiency gains are uneven
- Which areas need strategic focus
Real-World Impact
One enterprise COO shared:
“Our SDR pipeline conversion is up. I don’t know if it’s one ব্যক্তি using AI or everyone. It feels like too many variables changed at once. Lanai isolates those variables.”
Fast Deployment and Immediate Value
Lanai’s AI @ Work Operating System is now generally available and can be deployed in as little as one day using MDM and SSO integrations. This enables enterprises to quickly:
- Gain visibility into AI usage
- Measure real business impact
- Optimize AI performance at scale
Final Thoughts
As AI becomes deeply embedded in enterprise operations, accountability is no longer optional. Lanai’s AI @ Work Operating System provides the transparency and insights businesses need to move from experimentation to data-driven AI strategy and execution.
