Positioning data and AI as a driver of investment conviction
Data & AI Due Diligence
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.
As part of a live investment process, the private equity client sought to validate a digital marketplace’s core value drivers — specifically the strength of its network effects and the feasibility of AI-led pricing and recommendation capabilities.
Management presented a compelling growth narrative underpinned by data and AI. However, there was limited independent validation of the underlying data quality, structure, and readiness required to deliver on these claims.
DataDiligence was engaged to conduct a rapid, focused data and AI diligence to assess whether the opportunity was real, scalable, and investable.
The work was delivered over a seven-day sprint, combining structured data analysis, unstructured document assessment, and targeted feasibility evaluation.
The review examined:
Quality, completeness, and reliability of core transactional datasets
Extractability and usability of unstructured documents for automation
Relationship structures across users, projects, and transactions to validate network effects
Technical feasibility of AI-driven pricing and recommendation models
WHAT WE FOUND
The analysis provided a nuanced view — confirming elements of strength while surfacing critical constraints:
Inflated headline metrics: Significant duplication in transactional data overstated activity levels and required normalisation to establish a reliable baseline
Gaps in data richness: Missing attributes limited segmentation and model effectiveness, although the underlying schema was directionally sound
Strong unstructured data potential: ~85% of key documents were digitally extractable, creating a clear pathway for automation and enrichment — albeit requiring domain-specific taxonomy development
Early-stage network maturity: Relationship analysis confirmed credible network structures, but with uneven density — concentrated regional clusters rather than fully scaled network effects
AI capability not yet production-ready: Existing AI experiments were fragmented, externally dependent, and lacked the robustness required for scalable deployment
WHAT THIS MEANS FOR THE BUSINESS
Clear, data-backed understanding of the true strength of the platform’s data assets and network effects
Realistic view of what is achievable with AI — and what investment is required to get there
Identification of priority data improvements needed to unlock pricing and recommendation use cases
Structured pathway from fragmented experimentation to scalable AI capability
OUTCOME
DataDiligence aligned both investor and management on a realistic, evidence-based view of the opportunity — bridging the gap between ambition and execution.
We defined a practical, high-level AI roadmap, outlining:
Required data enhancements
Capability build (data, engineering, modelling)
Sequencing of initiatives to deliver measurable value
This enabled the investor to move forward with:
A clear view of readiness and feasibility
A quantified and de-risked investment case
Confidence in the pathway from data to value creation
The investor proceeded with the acquisition, supported by a robust understanding of both the upside potential and the delivery requirements.