Startups run on speed. Lasting companies run on structure.

Scenario

The CEO wants to expand the existing product into a new client market. The data points to a profitable market and the company product can be customised to fit. The CEO says: make it happen.

My job as the right hand is to turn the ambition into something concrete, costed, resourced and de-risked, and to come back to the board with three things in plain numbers: what we can do, what we can’t, and what we’ve mitigated. Every step below is run on data, which is what a board pays for.

Phase 1 — Translate the vision into data

I identify the CEO’s vision: what market, what size, what region, who is the ideal client profile, what they are willing to pay, and how the service should be presented to them.

Then I define success metrics up front: what does good look like, by when, and how it’s measured. A number and a picture.

Phase 2 — Capacity, P&L and whether we can take this on

I turn the current landscape into numbers. I model the requirements of the new initiative (onboarding volume, support load, delivery hours) against the team’s current utilisation.

Model current P&L

I identify and analyse the impact on EBITDA explicitly, analysing if revenue is growing faster than cost. I build these numbers together with Finance, so what I present to the board and what the CFO presents line up.

Control built in: the incoming pipeline should never exceed our capacity to deliver service; we never degrade service for the clients we already have. The warning signs and metrics of what this looks like are identified, communicated and mitigated against.

Phase 3 — Systems, vendors and whether the machine can support it

A new initiative often brings new demands the current setup wasn’t built for. I assess the capability of the existing systems and quantify this against the CEO’s vision.

Control built in: reversibility is a framework I apply everywhere — I don’t lock the business into an expensive commitment before the initiative has demonstrated it will pay.

Phase 4 — Cross-functional engagement, and what happens when something fails

I take the direction to the heads of Technology, Operations and Sales, and we pressure-test it against their own data. Can each function carry this inside its current resource, and if not, what’s the gap.

For each function we agree two sets of KPIs: what success looks like, and what failure looks like, both in numbers, so everyone can see “on track” and “off track” without debate. Over-communication is key here.

Phase 5 — Report to the board, led by data

I come back to the C-suite with what’s been done, what can be done, what can’t (and why), and what’s been mitigated. Every claim carries a number and a way to track it, and every risk comes with how we’d know it’s materialising and what the response plan is.

The output is a credible business case and a practical implementation plan, with the trade-offs made explicit so the decision is an informed one. All of it tied back to the CEO’s original vision.

The lenses I apply

• Data first, always.
• Translate vision into a target with numbers.
• Build, borrow, or defer on current resources and systems, scored by cost, time-to-value and reversibility.
• Lines of defence. For every failure mode: who catches it, and who catches it if they miss.
• Bad news first, aligned to the thesis. The board hears what can’t be done and what’s been mitigated alongside the upside.

My pragmatic visionary approach: I connect the functions, turn an ambition into a costed and de-risked plan the board can actually decide on, and then make it run.

Scenario

A candle brand is building out its range for the Christmas quarter, the season where most CPG brands make the majority of their sales. Ahead of launch, a premium brand might choose to screen-print and foil-stamp its glass vessels across several new scents/SKUs at once, before any of them has proven it will sell.

Operational error

Every SKU sits somewhere between fully recoverable and irrecoverable. A plain candle vessel with a stick-on label is recoverable: if the SKU doesn’t sell, you peel the label and the glass still has reuseable value. The moment you permanently print that vessel, a recoverable SKU has become a financial risk; it can’t be discounted into something useful and it can’t be reused.

Focusing on the icing instead of the cake

Many CPG founders unknowingly ignore the foundations of a business which are the tiers I pointed out. So while everyone’s asking the marketing question — “will this SKU sell?” — the operational one gets missed: what happens to the capital if it doesn’t? The brand has locked money into a form it hasn’t validated with data. This operational risk compounds in two ways: every extra SKU is another irreversible risk, so the risk grows with the size of the range; and because the sales pattern is seasonal, if you get it wrong you have to wait a whole year to put it right.

The channel decides how bad it gets

Sell direct, and a failed line stays contained: full margin, controlled volume, you can make a few hundred and stop. Put that same line into retail and the damage multiplies — retailers buy at roughly half your price, charge listing and fill fees for every product in every store, and pay you weeks later. One failed line becomes a write-off, a fees bill and trapped cash all at once. The route to market is itself a reversibility decision: prove a line selling direct before you commit it to a shelf you can’t walk back from.

4 ways to stay protected

• Stay reversible until a line earns it. Labels on plain or translucent vessels, not permanent print, so an unproven SKU keeps its resale value.
• Fill partially. Buy the vessels, fill a fraction. If it lands, fill the rest. If it doesn’t, the empties are still worth something.
• Tool one detail, not the whole thing. A custom lid on a stock vessel buys a distinctive look for a fraction of the cost, and almost none of the risk.
• Cash cycle conversion strategies, which I can further expand on in a consultation depending on the structure of your business.

The lens I apply

This applies to every CPG brand — swap “scent” for “SKU” and it fits skincare, drinks, food, anything. One of my diagnostic lenses is simply: how reversible is this? I weigh how permanent a decision is against how much data actually backs it. As a right hand, I’m always asking “what does this cost us if we’re wrong, and can we get it back?”