Lanai Launches OS to Tame Enterprise AI Chaos, Close Accountability Gap

SAN FRANCISCO, CA – April 16, 2026 – As artificial intelligence rapidly moves from isolated experiments to an integral part of daily operations, a critical “accountability gap” has emerged, leaving business leaders struggling to track usage, measure value, and manage risk. Addressing this challenge, enterprise AI accountability company Lanai today announced the general availability of its AI @ Work Operating System, a platform designed to bring order to the burgeoning chaos of corporate AI.

Lanai’s system promises to be the first of its kind to discover every AI workflow across an organization—whether it was bought, built, or is being used in the shadows—and connect that activity to measurable business outcomes. The goal is to provide a single source of truth that helps leaders decide which AI investments to scale and which to cut, transforming AI management from a reactive guessing game into a strategic discipline.

“AI has moved from experimentation into day-to-day operations, but most enterprises still cannot clearly see where it is being used, what it is improving, or where risk is building,” said Lexi Reese, co-founder and CEO of Lanai, in the company's announcement. “Our goal at Lanai is to make enterprise AI accountable by organizing every AI and agent interaction and making it transparent, measurable, and actionable.”

The Pervasive Problem of 'Shadow AI'

The urgency for such a solution is underscored by the widespread and often invisible proliferation of AI tools within large companies. Eager to boost productivity, employees are independently adopting a vast array of AI assistants, copilots, and embedded features, a phenomenon known as “shadow AI.” While often well-intentioned, this unsanctioned usage creates significant blind spots for IT and security leaders.

Industry analysts warn that this untracked activity exposes organizations to substantial risks, including the leakage of sensitive intellectual property into public AI models, regulatory compliance violations, and increased vulnerability to security threats. Research suggests that as much as 80% of AI activity within an enterprise can be “off the grid,” leaving leaders with an incomplete and often misleading picture of their AI landscape. This creates a scenario where business results may improve, but the underlying drivers remain a mystery.

This challenge was articulated by one executive in the company's materials. “Our SDR pipeline conversion is up. I don't know if that's one person who figured something out with AI — or everyone,” stated the COO of a 4,000-person technology company. “It's like a science experiment where too many variables changed at once. Lanai isolates the variables.”

A New Operating System for AI Accountability

Lanai's AI @ Work Operating System is engineered to provide this clarity without impeding employee productivity. The platform's technical foundation is built on edge-based detection, utilizing a lightweight browser extension, an endpoint agent, and hooks into downloaded tools. This allows it to capture AI activity across sanctioned and unsanctioned tools alike, crucially without relying on vendor-specific integrations that can limit visibility.

This edge-based approach is also central to the company's privacy-first design. By processing data directly on the user's device, the system can identify patterns and workflows without reading the specific content of prompts or routing sensitive corporate data through a third-party cloud. This focus on pattern detection over content monitoring aims to balance the need for organizational visibility with respect for employee privacy.

Key features of the platform include:

• Comprehensive Discovery: Automatically detects and inventories AI activity across all assistants, agents, and embedded tools, including shadow AI.
• Workflow-Level Measurement: Moves beyond tracking tool usage to measure how specific AI-powered workflows are being adopted across different teams.
• Queryable Data Graph: Connects AI usage metrics to an organization's existing systems of record, such as Salesforce, GitHub, and Zendesk, allowing leaders to ask natural language questions about AI's impact on business performance.

Moving Beyond Usage to Quantifiable Business Impact

A core differentiator of the Lanai platform is its focus on translating AI activity into quantifiable business value. Instead of simply logging which tools are used, the system is designed to measure the “capacity gained” at the workflow level. It calculates how much time AI saves by comparing the duration of an AI-assisted task to the estimated human effort it would have otherwise required. This provides leaders with a clear metric for understanding where AI is creating the most significant efficiency gains.

According to Lanai, its work with early Fortune 500 customers has already demonstrated tangible results. The platform has helped organizations identify specific, high-impact use cases, such as a sales renewal preparation workflow that saved representatives 4.5 hours per instance, and has shown how engineering teams can achieve 1.4x leverage with AI tools compared to 1.1x in sales. In other instances, the visibility provided by the platform has reportedly enabled clients to consolidate redundant AI tool subscriptions, eliminate hundreds of thousands of dollars in wasted licensing fees, and strategically scale beneficial workflows that were previously hidden within pockets of the organization.

By connecting AI adoption directly to metrics like pipeline velocity, SLA attainment, and engineering throughput, the system empowers executives to make data-driven decisions about where to invest their resources for maximum return. This shifts the conversation from the hype of AI to the hard numbers of its strategic value.

Backed by Industry Veterans and Capital

Lanai enters the market with a formidable leadership team and significant financial backing, signaling strong investor confidence in its mission. CEO Lexi Reese brings a wealth of experience from her time as COO of Gusto and as a longtime leader at Google, where she was deeply involved in its machine learning-driven ads platform. She is joined by CTO Rajesh Raman, an infrastructure veteran from Google, Meta, and Splunk, and CPO Mohit Mehta, who brings product leadership experience from Splunk and Nvidia.

The company is backed by a syndicate of notable venture capital firms, including Lux Capital, Juxtapose, BAG Ventures, f7ventures, and BenchStrength. This combination of seasoned leadership and robust funding positions Lanai as a serious contender in the emerging and critical market for AI governance and accountability.

Lanai's AI @ Work Operating System is now generally available and can be deployed in as little as one day through standard enterprise MDM and SSO solutions, offering a fast path for organizations to finally get a handle on their AI performance.