AI in the First 100 Days of Portfolio Ownership
An evidence-led frame for AI in a private-equity owner’s first 100 days: which opportunities to pursue, which depend on data or ownership, and which should wait.
Direct answer
Direct answer: Private equity first 100 days
The first 100 days of ownership is when AI ideas are easiest to add to a value-creation plan and hardest to test. The useful question is not where AI could help, but which initiatives the company can own, has the data for, and can measure, and which should wait. I work with the owner’s operating team and company management to produce a company-level triage, its dependencies, and a staged plan that management can carry.
The question
“What AI questions belong in an early operating review, and which initiatives should wait for better data or ownership?”
Who it is for
Operating partners, portfolio operations teams, and value-creation leads at private-equity and other long-term owners, working with company management.
What you leave with
A company-level opportunity triage, its dependencies, and a staged operating plan.
- 01 A shortlist of AI opportunities tied to specific workflows, with the evidence for and against each
- 02 A dependency register: data, systems, people, and decisions each opportunity relies on
- 03 A staged plan separating what can start now, what needs a test first, and what should wait
- 04 Named management owners and the measures each stage will be judged on
How it runs
Format, method, and preparation
- Duration
- Management interviews and a document review, followed by a working session and a written plan
- Delivery
- Remote, with in-person sessions in the UK where useful
- Participants
- The operating partner or portfolio team, the CEO, and the relevant functional and technology leaders
Read the business
Review the operating model, systems, data, and existing technology plans before any AI idea is discussed.
Generate and test opportunities
List candidate uses tied to real workflows, and test each against data availability, ownership, risk, and measurability.
Map dependencies
Record what must be true first: data access, system changes, policy decisions, or a capable owner.
Stage the plan
Agree what starts in the first 100 days, what is tested first, and what is deliberately deferred.
Useful to have ready
- →Access to company management and relevant operating data or reports
- →The owner’s existing value-creation or operating plan
- →Agreement on which business areas are in scope
Evidence
What this draws on
Document and CRM workflows
Python and AWS systems for document extraction, classification, validation, routing, and LLM integration with CRM and operational workflows.
Regulated banking AI at Aveni
Architected enterprise banking AI with conduct-risk workflows, evidence generation, human review, escalation, evaluation, versioning, and release controls. Part of the Aveni team in the first FCA Supercharged Sandbox cohort; the FCA lists Aveni as an accepted firm, which is not an FCA endorsement or certification.
Inspect the source ↗Six companies founded, 2008–2024
Kwippy, Jaja.tv, Octo.ai, ExpressMOJO, Boom, and Neul Labs, spanning social software, interactive media, ML and analytics, logistics, blockchain infrastructure, and applied AI. Advisory and investment records are not counted as founded companies.
Read more →Scope and limits
What this is not
- —The 100-day plan is a planning frame. It does not promise value creation, cost reduction, or returns.
- —This is technical and operating decision support, not investment advice, valuation, or a substitute for financial or legal diligence.
- —Initiatives that depend on data or ownership the company does not yet have are recorded as dependencies, not committed to.
If the need is different
Common questions
Answers before you commission
Should AI be in a private-equity first-100-days plan?+
It should be in the review, but not automatically in the plan. Many AI opportunities depend on data quality, system access, or an owner the company does not yet have. Those belong in the plan as dependencies, not as committed initiatives.
How do you assess AI opportunities across different portfolio companies?+
With a common set of questions about workflow, data, ownership, risk, and measurability, applied to each company’s actual operations. The same framework produces different answers for different businesses.
Who owns the plan afterwards?+
Company management. Each stage has a named owner and a measure, so the plan does not depend on an outside adviser to continue.
Can you work across several portfolio companies?+
Yes. The same frame can be applied company by company, with an anonymised cross-portfolio summary for the operating team.
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