PRIVATE EQUITY · AI VALUE CREATION
AI value creation is more than a thesis about a model.
We assess technical risk, data maturity and automation potential — and show how to translate it into first-100-days execution. We look at the target and the portfolio companies through the eyes of an operator-architect, not just a developer.
WHAT USUALLY BLOCKS IT
Where the AI thesis diverges from operational reality.
The AI upside is hazy
AI shows up in the investment thesis but isn’t tied to a process, a flow or a decision — it’s hard to tell real leverage from narrative.
Technical risk is unclear
Standard due diligence shows what can break. What’s missing is the layer of where technology, data and automation maturity support the thesis, and where they undermine it.
Portfolio companies are stuck in manual work
Proposals, tenders, CRM/ERP data and customer service consume teams’ time — without governance and without a clear entry point for the first automation.
The board needs recommendations it can read
The deal team needs conclusions the board can read — with red flags, value levers and a first-100-days map — not a technical report for engineers.
WHAT WE DO
An operator-architect’s lens on the target and the portfolio.
We work from the investment thesis through architecture and execution — answering the questions that genuinely weigh on the decision.
- AI/IT due diligence — technical risk, data maturity and automation potential before and after the decision.
- Mapping AI opportunities across the portfolio — where AI lowers cost, raises throughput, improves customer service or builds recurring revenue.
- Automation-readiness assessment — which workflows are ready for a pilot and which need data and process foundations first.
- Technical-risk review — the data and integration foundations that could block the thesis.
- A first-100-days map — what happens in the first days, weeks and quarter after closing, ordered by value and feasibility.
OFFER
Three entry points — matched to the moment in the deal.
AI/IT due diligence
An assessment of AI readiness and automation maturity, technical risk and a data-architecture review where access allows — with a first-100-days value-creation map. A result the board can read, as an appendix to the memo or a summary for the board.
Assess risk before the dealAI value audit
An organized map of AI opportunities tied to business processes — with an assessment of value, risk and feasibility and a recommendation for the first pilot. For moments when a lot is happening around AI in the company, but nothing is tied to an operational outcome.
Map the highest-value opportunitiesAI Operating Model
A practical AI operating model for a portfolio company — how to identify, approve, build, oversee and improve AI workflows. With data- and risk classification, a build/buy/partner decision model and a governance cadence, so AI adoption is repeatable rather than one-off.
Build a repeatable AI modelEVIDENCE
Where our opinion comes from.
We combine due diligence in private equity, operational experience at COO level and the practice of an AI architect — three perspectives rarely found in one person.
- Fund-side experience: earlier work in AI/IT due-diligence and portfolio advisory.
- COO-level operational background — wiring AI and automation into processes that are already running.
- A sovereign AI architecture for an insurer — designed for on-prem deployment, with regulatory-corpus ingestion and auditable evaluation; an investment thesis shaped for the executive sponsor and the portfolio board.
- A compliance-assessment workbench for a digital regulator — designed end-to-end: evidence ingestion, classification against regulatory frameworks, gap and contradiction detection in a single auditable workspace.
- An AI-enablement strategy for an advisory partner — an executive deck, workflow evaluation, a KPI structure and pilot-ready workflows packaged for reuse across multiple client engagements.
- Evidence of operating leverage from proposal automation (RFP) and CRM/ERP workflows in companies with extensive product catalogs.
Many examples are anonymized due to client confidentiality. We describe proposal- or thesis-stage work as “designed,” not “deployed.”
FIRST MEETING
We start the conversation with specifics, not slides.
Thesis
The investment thesis or the portfolio value-creation plan.
Bottlenecks
Current operational bottlenecks and manual work.
Data
Technology and data maturity — what is usable, what is missing.
Risk
Technical risk and existing activity around AI — what is already happening, where the red flags are.
Value levers
Value-creation targets and the workflows ready for leverage first.
Timeline
The scope and access for due diligence and the decision timeline.
Knowledge base
For funds and portfolio companies
The guides investment and operating teams read most often.
LET’S START
Assess AI and technical risk before you sign the deal.
Bring an investment thesis or a portfolio company with manual work that’s holding back growth — we’ll show where AI creates value, where it introduces risk and how to translate it into an actionable map.