Mendo Raises €12M to Fix Enterprise AI's Biggest Problem: Getting Employees to Actually Use It

Enterprises have poured billions into generative AI, but a persistent problem haunts these investments: getting employees to use the tools effectively. Many organizations deploy cutting-edge AI only to see adoption stall, leaving them with costly experiments that never deliver measurable business outcomes. A Paris-based startup founded in 2021 by Quentin Amaudry and Alexandre Pinon believes it has cracked this challenge by focusing not on the models, but on the human layer between the tools and the workforce .

Mendo, which helps large enterprises embed AI into daily workflows, has just secured a €12 million Series A round led by Ventech and Educapital, bringing its total funding to €15.5 million . The round, which also saw participation from Tomcat and OVNI, follows a €3.5 million seed closed in October 2024 and a €700,000 pre-seed in 2023 . The fresh capital will fuel an aggressive expansion plan: doubling the team to 100 employees, expanding the platform to support agentic AI, and scaling commercial operations across Europe .

Why enterprise AI adoption keeps getting stuck

The core problem Mendo addresses is not technology availability. Large organizations already have access to ChatGPT, Microsoft Copilot, Gemini, Claude, and internal AI tools. What they lack is a structured way to move beyond scattered, individual experimentation and turn these tools into a genuine productivity lever across the workforce .

Mendo positions itself as an "adoption layer" that embeds directly into the tools employees already use — including ChatGPT through its MendoGPT tool and Microsoft Excel, where the platform initially launched . Rather than pulling workers into a separate training portal, the platform guides usage in real time within the applications, surfacing relevant use cases by role and giving leadership clear visibility into where AI is creating value .

The goal is to answer the questions that keep CIOs up at night: where is AI actually being used? Which use cases generate measurable time savings? And how can effective practices be anchored across the entire organization?

Traction with major enterprises

Mendo's approach has already gained significant traction. The platform now supports more than 100,000 employees across more than 100 major organizations, including PwC, Novo Nordisk, and Crédit Agricole . The company says its tool is designed to be product-first, building an integrated adoption and training experience that helps teams unlock "real and measurable time savings with GenAI" .

The platform's core philosophy rests on three pillars: getting people to actually use generative AI to understand it, making that experience seamless within existing workflows, and focusing on long-term impact rather than short-term engagement metrics .

What the €12 million will fund

With the Series A closed, Mendo is pursuing three clear growth priorities :

Product development for agentic AI. The startup plans to expand beyond today's generative AI tools to support agentic AI — autonomous systems capable of taking actions on behalf of users. This positions Mendo to remain the adoption layer as enterprise AI evolves from chatbots to agents that can execute complex workflows.

Workforce expansion from 50 to 100. Doubling the team will give Mendo the capacity to serve a growing client base, accelerate engineering, and build out go-to-market capabilities across new territories.

Commercial scaling across Europe. While the startup has established a strong presence in France, the Series A is designed to test its approach in additional European markets, with sales and go-to-market efforts expanding accordingly .

The bigger picture for enterprise AI

Mendo's raise comes amid a broader wave of AI funding in France, where landmark rounds from companies like Mistral AI have positioned the country as a visible player in the global AI landscape . But while Mistral and its peers build the models, Mendo represents a growing category of startups tackling the less glamorous but arguably more critical problem: getting people to actually use the technology in ways that show up on the balance sheet.

The bet is that in an era when every enterprise has access to powerful AI tools, the winners won't be the companies with the best models, but the ones that solve the organizational challenge of widespread, effective adoption.