Client: Private equity firm with multiple portfolio companies
The situation
Data was fragmented across portfolio companies. AI initiatives had started but weren’t operationalised. There was duplicate investment, inconsistent reporting and slow value realisation. Each portfolio company had different levels of data maturity, making it difficult to compare performance or identify synergies.
What we did
Stage 1
Portfolio Baseline We assessed data maturity and AI readiness across the portfolio, identifying common gaps, platform fragmentation and governance inconsistencies. This gave the investment team a clear view of where each company stood.
Stage 2
Standardised Foundations We established shared reference architectures, governance frameworks and data quality standards. This created a common foundation that could be applied across the portfolio without forcing uniformity where it wasn’t needed.
Stage 3
Pilot and Prove Value We identified high-impact use cases that could demonstrate value quickly. We ran pilots across selected portfolio companies, validated outcomes and built the business case for broader rollout.
Stage 4
Scale and Industrialise We rolled out proven solutions across the portfolio, embedding governance and capability as we went. We established a repeatable operating model that could be applied to future acquisitions.
The outcome
- Faster value realisation across portfolio
- Reduced delivery risk and duplication
- Comparable KPIs across companies
- Stronger governance and auditability
- Scalable operating model for future acquisitions
Investor outcomes
- Clear visibility of data and AI maturity across portfolio
- Standardised foundations that accelerate integration of new acquisitions
- Proven playbook for value creation through data and AI
Form
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