AI value creation across the whole portfolio.
A program that anchors AI Transformation along the deal lifecycle, from due diligence to exit.
The situation
Many funds have an AI thesis in the value creation plan, but no repeatable implementation across several portfolio companies. Every company starts from zero, learnings get lost, and the operating partner has no way to compare which company is actually making progress.
We bring the building blocks we offer individually for one company (due diligence, the 100-day plan, AI Transformation, Fractional CTO) into a program that works across several portfolio companies: a comparable assessment framework, reusable use cases, and a shared view for the operating partner on progress.
Every portfolio company gets its own tailored plan. The operating partner gets an overview that's comparable across the entire portfolio.
Deliverables
Portfolio assessment
A comparable AI readiness assessment across several portfolio companies, using a consistent framework.
Prioritization across the portfolio
An assessment of which company offers the largest and fastest AI lever.
Reusable use cases
Use cases piloted in one company and reused in similar portfolio companies.
Support per company
On-the-ground implementation in each portfolio company, adapted to its maturity and resources.
Progress overview for the operating partner
A regular, comparable status report across all companies involved.
Our approach
- 01
Review the portfolio
We assess with the operating partner which portfolio companies qualify for an AI program.
- 02
Prioritize the first company
We start with the company offering the largest or fastest-realizable lever.
- 03
Develop and implement use cases
We develop and implement AI use cases in the first company.
- 04
Transfer to further companies
We check which use cases transfer to other portfolio companies and adapt them.
- 05
Consolidate progress
We report to the operating partner regularly on the status across the whole program.
Typical clients
| Situation | Our approach |
|---|---|
| A value creation plan with an AI thesis but no implementation program | A repeatable program instead of one-off projects per company |
| Several portfolio companies with a similar business model | Reusing use cases saves time and cost |
| The operating partner needs a comparable overview | A consistent assessment framework across the whole portfolio |
Our approach: A repeatable program instead of one-off projects per company
Our approach: Reusing use cases saves time and cost
Our approach: A consistent assessment framework across the whole portfolio
Outside our scope
We don't roll out a standard solution across every portfolio company without checking its individual starting point. Every company has its own data, systems, and teams, so the program adapts use cases accordingly instead of copying them unchanged.
Frequently asked questions
| Ausgangslage | Umsetzung | Ergebnis | |
|---|---|---|---|
| EdTech company, PE-financed | PE-financed EdTech company, technical picture unclear before the investment. | Tech DD, greenfield architecture, AI agents in production, AI-assisted engineering organization. | The company runs AI in day-to-day operations, not just in pilot projects. |
| EdTech company, content production | Content production ran step by step by hand and depended on a few people. | n8n pipeline with LLM steps for drafting, structuring, and formatting, with human sign-off. | The content team works on quality, not on mechanical intermediate steps. |
| EdTech company, engineering organization | Engineering team used AI coding tools individually, without a shared workflow. | Toolchain, adapted workflow, and quality gates for AI-assisted development. | The team works agentically as the default, with controls that fit the new way of working. |
Ausgangslage
PE-financed EdTech company, technical picture unclear before the investment.
Umsetzung
Tech DD, greenfield architecture, AI agents in production, AI-assisted engineering organization.
Ergebnis
The company runs AI in day-to-day operations, not just in pilot projects.
EdTech company, content production
Ausgangslage
Content production ran step by step by hand and depended on a few people.
Umsetzung
n8n pipeline with LLM steps for drafting, structuring, and formatting, with human sign-off.
Ergebnis
The content team works on quality, not on mechanical intermediate steps.
EdTech company, engineering organization
Ausgangslage
Engineering team used AI coding tools individually, without a shared workflow.
Umsetzung
Toolchain, adapted workflow, and quality gates for AI-assisted development.
Ergebnis
The team works agentically as the default, with controls that fit the new way of working.
Initial call: 30 minutes, concrete.
We discuss what an AI value creation program could look like for your portfolio.