About RM Equity Partners

RM Equity Partners is the investment arm of a 100+ year family business, specializing in acquiring and operating profitable asset-light businesses across Europe. With a deployment capacity of €250M and a portfolio of 25 brands employing over 600 digital experts, RMEP positions itself as "street fighters, not pencil pushers" - valuing results over processes and common sense over sophistication.

The challenge: bringing AI transformation at portfolio scale

By late 2024, seeing AI as the biggest technological shift in decades, RMEP decided to act on it. As a fund they faced a unique challenge: enabling change not only at fund level, but across the entire portfolio.

Portfolio companies live quarter to quarter, focused on hitting objectives and operational excellence. Optimizing for tomorrow, but leaving them little room to explore what is coming with AI. Without outside pressure, they won't prioritize AI transformation today.

Recognizing this challenge, RMEP took the initiative to bring AI transformation capabilities to all their portfolio companies.

Alternatives falling short

RMEP evaluated several conventional approaches to AI enablement, but each had critical limitations:

• Online courses: Couldn't create the necessary momentum for shared learning and cross-company collaboration

• Consultants: Would deliver recommendations but leave no lasting capability behind once their engagements ended

• Internal development: Would take too long to build the necessary expertise and curriculum from scratch, missing the competitive window

What RMEP needed

RMEP started looking for a program that could train operators across multiple companies simultaneously, create a cross-company builder community with shared technical language and tools, build real production solutions that delivered immediate value, and transfer skills permanently to portfolio company teams - over a short period of time.

The solution needed to work across different industries, company sizes, and technical maturity levels within the portfolio.

The program

9x adapted the AI-First Program to work across RMEP's entire portfolio of companies. The program, started in August, span across 5 months, in three phases:

• AI Literacy

• AI Automation

• Hackathon

Phase 1: AI Literacy (Online)

The first phase deployed 9x's AI Operator course across RMEP's entire portfolio, reaching 600 participants across 25 companies. This self-paced foundation training built baseline AI fluency across the organization, covering:

• How AI works - understanding large language models, capabilities, and limitations

• Prompt engineering - techniques for effective AI interaction and output quality

• Responsible AI use - data privacy, security considerations, and ethical guidelines

• Identifying automation opportunities - spotting high-impact use cases within daily workflows

This phase served a dual purpose: broad enablement and talent identification. As participants completed modules and engaged with the material, 9x tracked progress both at the portfolio-wide level and at individual company level. This comprehensive tracking system provided:

• Centralized fund-level reporting - RMEP received consolidated dashboards showing participation rates, completion metrics, and engagement patterns across the entire portfolio

• Individual venture reporting - Each portfolio company received detailed analytics on their team's progress, identifying participants who demonstrated the highest engagement in the course

This tracking helped RMEP find the most engaged operators from Phase 1. The data showed who was learning quickly and participating actively.

Phase 2: AI Automation (Online Cohort)

80 operators from across the portfolio underwent intensive technical training through 9x's AI Builder program. The cohort began with an in-person kickoff to establish connections across companies, then continued with the four modules of the AI Builder:

• Module 1: Automations: Covered how to structure workflows, understand automation logic, and build first simple automations.

• Module 2: Data & Databases: Focused on transforming data, designing database structures, and creating relationships between different data tables.

• Module 3: APIs & Webhooks: Taught connecting different tools and services, sending and receiving data between systems, and building integrated workflows.

• Module 4: AI Automations: Explored incorporating AI capabilities into workflows, building AI agents, and creating intelligent automation systems.

The format combined asynchronous courses (3-5 hours) with 2 live workshops each week as well as office hours for technical support.

Following the completion of the AI Builder course, participants were invited to apply for the hackathon. To ensure teams arrived ready to build, 9x organized support sessions to help participants formalize their use cases and prepare for the intensive building phase.

Phase 3: Hackathon (Onsite)

Before the hackathon, participants went through another round of selection. Those who attended had fully completed the AI Builder course and already scoped a use case approved by 9x.

For three days, participants gathered at 9x's Berlin offices to build their own projects. The 9x team provided hands-on support throughout—troubleshooting technical issues, offering guidance on implementation approaches, and brainstorming ways to evolve each project.

On the final day of the hackathon, a 3-hour demo session brought everyone together. Each team presented their automation project to the group, showcasing what they had built and sharing insights with the entire portfolio.

Outcomes

The program transformed AI from concept to capability across RMEP's portfolio: training 600+ operators, building 25+ production automations in 3 days, and establishing a cross-company community of builders who share technical skills, tools, and a common language for solving operational challenges.

From theory to shipping automations to production

The program used a consistent curriculum and foundational principles for all participants, but each team applied these to their own operational challenges: building company-specific automations across different industries, business models, and functional areas that went directly into production, not throwaway demos.

Pascal's use case: Local Content scraper
Pascal built a scraping cockpit to automate local news aggregation from 2000 Austrian municipalities.

Lena's use case: Social Network Post Automation
Lena developed an automation system for creating and scheduling social media posts, reducing manual effort while maintaining consistent brand presence across platforms.

Mariia's use case: Automatic AI Summaries
Mariia created an AI-driven summarization tool that automatically processes documents and content, delivering concise insights to support operational efficiency.

Skills transfer, not dependency

The program didn’t just deliver automations - it transferred the capability to build them to the portfolio. Participants returned to their companies equipped to automate workflows not just for themselves, but for their entire teams. They become internal force multipliers, creating leverage across their organizations by building solutions that streamline operations for colleagues, eliminate repetitive tasks, and free up capacity for higher-value work. Whether these operators remain with their current portfolio companies or move to new roles, they carry these capabilities with them, continuously multiplying the program's long-term value across the broader ecosystem.

Cross-portfolio builder community established

Beyond delivering individual automation solutions, the program successfully established a thriving cross-portfolio community of AI builders that spans RMEP's entire network of companies- bringing together participants who now share not only a common technical language and standardized toolset, but also an active network and collaborative framework for identifying, discussing, and solving operational challenges together across organizational boundaries.

Read RMEP full story