Private equity · Private equity value creation
The First 100 Days: Turning VCP Hypotheses Into Evidenced Operational Value
The value-creation plan is a hypothesis. The first 100 days should turn the highest-value hypotheses into operational evidence before they become a backlog of transformation or AI initiatives.
Most first-100-day plans are not short of initiatives.
They are short of evidence about exactly where the operating economics are leaking — and which intervention will genuinely move them.
A VCP might say improve productivity, reduce cost-to-serve, integrate an acquisition, release capacity, improve service or use AI to create operating leverage.
All reasonable ambitions.
But each is still a hypothesis until it can be connected to a real workflow, a baseline and a credible mechanism for value creation.
VCP hypothesis ≠ evidenced operational value pool.
That gap is where first-100-day activity can become busy without becoming economically precise.
What’s normal
The usual sequence is understandable.
The deal closes. Leadership mobilises. Functions produce initiatives. Technology teams gather AI and automation use cases. Transformation creates a roadmap. Benefits are attached to projects so they can be prioritised.
The problem is the direction of travel.
The organisation has moved from strategic ambition to intervention before it has fully established the operational evidence underneath it.
So the first 100 days can quickly produce a substantial change portfolio without answering a more basic question:
Which few workflows contain the value we actually need to recover?
Why it fails
A business case built around a proposed solution can tell you whether that solution appears attractive.
It does not automatically tell you whether it is the best intervention for the underlying problem.
AI may be technically feasible. Automation may remove manual steps. A new platform may improve workflow orchestration.
But the root cause might be upstream input quality, unnecessary controls, avoidable process variation, duplicated work after an acquisition or a hand-off that should not exist.
If you select the intervention first, every problem starts to look like a use case for the intervention you already selected.
That creates three common risks:
- theoretical benefits rather than evidenced benefits;
- local optimisation that pushes work somewhere else;
- technology applied to work that should have been prevented, simplified or standardised first.
What I do differently
I work backwards from the economics.
Not “where can we deploy AI?” but:
Where is operational value leaking? What is causing it? What is the credible value pool? What is the smallest intervention that can recover it?
That normally means taking a high-priority VCP hypothesis and moving it through progressively stronger evidence:
VCP lever → workflow → baseline → root cause → £ value pool → intervention → verified benefit.
AI and automation still matter. They just arrive at the right point in the decision.
Sometimes the answer is AI. Sometimes it is workflow automation or integration. Sometimes it is standardisation, upstream prevention or removing an activity altogether.
The method is intervention-neutral because the economic objective is more important than the technology category.
Diagnostic
What this looks like in practice
For one material VCP hypothesis, I would want an Operating Partner, Portfolio Director or PortCo COO to be able to answer:
- Which workflow is the economic hypothesis actually referring to?
- What is the current baseline — volume, unit cost, handling, waiting, rework, capacity or service level?
- Where is the value leaking rather than simply where is work manual?
- What evidence suggests the root cause rather than the visible symptom?
- How much of the theoretical opportunity is realistically addressable?
- What management action converts a process improvement or capacity release into an EBITDA, service or risk outcome?
That is enough to distinguish a genuine value pool from an attractive transformation idea.
private-equity · Leania
See how Leania approaches this workflowEvidence from the work
The principle is not theoretical.
In a high-volume operation, I found that a small number of processes were driving the majority of one team’s workload. The visible pain was downstream processing capacity.
The more valuable intervention sat upstream.
A small change to the upstream interaction — roughly two additional minutes of handling, with an estimated annual operating cost of around £5,000 — could prevent failures that were creating downstream work. The business case identified approximately £150,000 of savings potential, with an estimated payback of around two months.
That is the distinction I want in a first-100-day value conversation.
The obvious target was the team carrying the workload. The economic cause sat somewhere else.
If we had begun with “what can we automate in the downstream team?”, we could have produced a plausible automation backlog while missing the better intervention.
The questions I would put against a 100-day plan
For each operational value-creation initiative:
What is the baseline we expect to move?
Where is the value actually leaking?
Do we have evidence of cause, or only a visible symptom?
Would we still choose this intervention if AI or automation were not available?
Is the benefit cashable, capacity, revenue, service or risk — and who owns the action that realises it?
What would we need to see in 10–15 business days to decide whether this deserves further investment?
The objective is not to slow down the 100-day plan.
It is to stop speed creating false certainty.
The point
The first 100 days should not be a competition to create the longest transformation backlog.
They should turn the most important VCP hypotheses into a small number of evidenced operational value pools.
Once you know the baseline, the cause and the economics, the technology decision becomes easier — and the value-creation story becomes much more defensible from entry through to exit.