With venture funding tightening, fundraising timelines extending and revenue visibility becoming less predictable, the margin for error is smaller than it has been in years. Growth still matters, but so does burn discipline, scenario planning and the ability to make confident decisions with imperfect information.
In this session from our Scaleup CFO Webinar, Aarish Shah and Joe Newbold discuss cash flow forecasting in a constrained capital environment, how to build investor-ready models without overcomplicating them, and how finance teams should approach planning when forecasts are almost guaranteed to be wrong.
Watch the conversation (10 mins) here:
1. Plan on the assumption that capital is harder to raise, because it is
VCs have raised materially less capital, and that reduction flows directly downstream. While large platform funds and very early-stage funds remain active, mid-market funds are under pressure, particularly those that haven’t yet demonstrated returns to LPs. For founders and CFOs, this changes the default planning assumption.
“The first point I would make is, can you get to a point where you don’t need to raise, or that you can raise on your own terms?”
The priority question becomes: can the business reach a position where it doesn’t need to raise at all, or can raise on its own terms? Profitability, revenue quality and unit economics matter more than ever. Capital, if raised, should be clearly framed as fuel for growth, not a lifeline.
2. Assume fundraising will take longer than you think
What once took three months can now take six to nine. Even strong businesses should assume extended timelines unless they are unequivocally a “hot deal”.
“Hot deals are still getting funded, but just assume you’re not the hot deal.”
A recurring mistake is entering a fundraise with four months of runway. Internally, teams should forecast on the basis that:
- Revenue will underperform expectations
- Timelines will slip
- You have less time than planned
A prudent internal assumption is that revenue forecasts may be 30–50% off. External messaging can remain optimistic, but internal planning must be conservative.
3. Separate investor models from operational reality
“Make it simple. Make it malleable. And particularly in these times, make it realistic.”
Investor models and operational cash flows serve different purposes, and trying to combine them usually breaks both.
An effective investor model should be:
- Simple – easy to understand and interrogate
- Malleable – inputs clearly drive outputs
- Realistic – ambitious but credible
It should illustrate where the business is going and roughly when cash runs out, not attempt to mirror daily operational complexity. Over-engineering payment timings, supplier terms or granular operational flows makes the model harder to trust and easier to challenge.
4. Over-complication is the fastest way to lose credibility
Hard-coded models, excessive revenue streams and unnecessary complexity increase the risk of errors and undermine confidence. Investors are used to very simple models, often simpler than founders expect.
“If you have one revenue stream but are planning four, just keep one revenue stream.”
Best practice:
- Keep assumptions on one page
- Three core financial statements on the next
- Break out obvious drivers (headcount, key costs)
- Avoid hard-coding wherever possible
If multiple revenue streams are planned but not yet proven, modelling a single blended stream is often more credible than presenting speculative detail.
5. Forecasting is about navigation, not perfection
“It’s like Google Maps, if it showed you everything around you, you wouldn’t be able to use it.”
A financial model is a map, not a mirror of reality. If it tried to reflect 100% of real-world complexity, it would be unusable. Its value lies in giving leadership a clear sense of direction and trade-offs.
The goal is not accuracy at all costs, but useful insight:
- Where do we run out of cash?
- What levers can we pull?
- How sensitive are we to revenue changes?
6. Contingency and scenario planning matter more than precision
For internal cash flow forecasting, the most important question is not “what’s the budget?” but “what happens if we miss it?”
“The question is: if revenue is A, B or C, what are the actions we take?”
Strong teams define clear trigger points:
- If revenue is at level A, B or C, what actions follow?
- At what point do hiring plans change?
- When are contractors reduced?
- What costs are discretionary versus fixed?
Budgets for 2026 will be wrong quickly. What matters is having pre-agreed responses when they are.
7. Avoid over-integrating systems into your model
Integrating CRMs, payroll systems or live data feeds into financial models often creates false confidence. CRM data in particular is only as good as sales discipline, and inaccuracies compound over time.
A more effective approach is:
- Take a realistic snapshot of current data
- Build simple, explicit assumptions
- Review and re-forecast on a rolling basis (e.g. quarterly)
Static starting points make change visible and intentional, rather than accidental.
8. Revenue forecasting deserves the majority of attention
Headcount and costs are largely within management control. Revenue is not. As a result, most forecasting effort should be spent pressure-testing pipeline assumptions, conversion rates and timing.
“I’d spend 90–95% of my time making sure the revenue pipeline is modelled properly, because everything else flows from that.”
Ultimately, cash flow forecasting succeeds or fails on how honestly revenue is modelled. Everything else flows from that.
About our panel
Aarish Shah is an experienced CFO, thought leader and founder of EmergeOne, working closely with founders, CFOs and investors across high-growth and investor-backed businesses. With deep experience navigating the operational and strategic realities of scaling finance functions, Aarish brings a pragmatic perspective on where AI genuinely adds value — and where it currently falls short.
Joe Newbold is an experienced CFO and finance operator who has spent years inside fast-growth businesses, building and refining finance, FP&A and operational workflows. Known for his hands-on approach, Joe has actively tested many of the tools currently being promoted across the AI landscape, giving him a grounded view on what works in practice, what requires caution, and what remains aspirational.
