Case studies & use cases

Many portfolio companies begin their journey with fragmented systems, inconsistent reporting and limited visibility.

The first step is typically establishing a single source of truth — creating the data infrastructure required for reliable analytics and future AI initiatives.

As portfolio companies grow through acquisition, new entities often introduce additional systems, data structures and reporting complexity.

With strong data foundations in place, new businesses can be integrated into the existing data platform quickly, enabling group-wide visibility without requiring disruptive ERP consolidation.

Once reliable data foundations exist, companies can begin applying AI and machine learning to improve operational processes.

Targeted automation projects allow businesses to reduce manual workload, improve consistency and unlock efficiency gains in high-volume operational activities.

As organisations gain confidence in AI-driven automation, the next step is enabling internal teams to develop and scale their own solutions.

By embedding practical skills and frameworks inside the organisation, companies can continue identifying and implementing new automation opportunities without relying entirely on external specialists.

As the investment period matures, portfolio companies increasingly need to demonstrate the strength of their technology, data and AI capabilities to prospective buyers.

Conducting exit readiness diligence 9–18 months before a sale process allows leadership teams to identify and close gaps, strengthen the investment narrative and prepare clear documentation that supports investor diligence and valuation discussions.

It also allows management teams to anticipate buyer diligence questions and strengthen the investment narrative before the sale process begins.

As investors increasingly evaluate data and AI as core value drivers, the ability to validate data quality, architecture, and delivery feasibility is critical to investment decisions.

Data and AI due diligence provides a structured, evidence-based view of a company’s data assets, technical foundations, and AI readiness — moving beyond management narrative to assess what is truly scalable and value-accretive.

By examining both structured and unstructured data, underlying data models, and the feasibility of priority use cases, this approach identifies gaps, risks, and unrealised opportunities early in the investment process.

Conducted ahead of or alongside commercial and technical diligence, it enables investors to quantify upside, de-risk execution, and define a clear roadmap from current state to measurable value creation.