Is your forecast still holding or already lying?

By February, most plans still appear coherent. The numbers add up, dashboards show no obvious red flags, and the year-end plan seems under control. This phase reassures management and, often, the board. The forecast isn't necessarily wrong, but it's frequently built to confirm an existing direction rather than truly stress-test it through multiple scenarios. Early variances get absorbed, normalized, explained away as temporary. Initial assumptions remain standing, even as the context has already begun to shift.

For scale-up leaders with investors in the cap table, the real risk isn't getting a forecast wrong—it's continuing to make strategic decisions, allocate resources, and defend a narrative to the board based on numbers that create an illusion of control.

When a forecast "always works out," the question isn't how accurate it is, but "what isn't it showing?"

We're in the month when the plan has started producing effects, but not enough to reveal where it's actually taking the company in terms of value, risk, and strategic optionality. Year-end decisions are now operational, teams are executing, initial actuals are flowing into reports—and precisely because of this, a sense of control begins to take hold.

It's an ambiguous phase where the numbers don't signal emergencies, but they don't tell the full story either. This is where the limitation of a linear approach becomes evident: observing what's happening is no longer sufficient, because the question isn't whether the plan is working today, but whether it will continue to hold and support strategic choices when certain variables start moving in unexpected ways.

In February, the issue isn't correcting the numbers—it's asking:

• Are the assumptions underlying our growth, burn, and milestones still solid?
• Are we detecting weak signals that could impact Enterprise Value in the coming quarters?
• Are we exploring alternatives now, or postponing until they become forced?

This is when scenarios and simulations become central—not as theoretical exercises, but as governance tools. Simulating means shifting attention from what is happening to what could happen if one or more drivers begin behaving differently than expected. It means stopping asking whether the numbers are coherent and starting to ask how robust the system is against realistic variations.

At this point in the year, these questions are still manageable because options remain open. Decisions aren't irreversible, constraints aren't fully crystallized, and the cost of a correction is still contained. But leveraging this window requires moving from a "monitoring" mindset to a "simulation" mindset.

The point now isn't verifying whether results align with budget—it's understanding whether the model driving decisions remains reliable in a context that's already begun to move.
The useful KPIs at this stage don't measure performance; they detect where the plan might stop supporting value creation in the medium term. This calls for a hierarchy.

Here are some that must be considered.

1. Signals of decision quality

The first level of analysis concerns the ability to correctly read what's happening. It's not enough to know if our forecast "holds"—we need to know if it's reacting to the right signals. It becomes critical to observe:

• Coherence between initial assumptions and early actual data on key business drivers
• The presence of small, repeated variances—more significant than isolated errors
• The number of assumptions that continue to remain active without being revalidated

These signals don't indicate error—they indicate inertia. They reveal whether the plan is evolving with the context or simply adjusting numbers to stay consistent with itself.

2. Signals of anticipated financial stress

Is future value under pressure before the P&L is? Tensions emerge first in cash flow, not in the income statement. This is why it's useful to watch:

• Evolution of actual runway versus planned runway, observing not just the level but the velocity at which it's declining
• Effective burn rate compared to what's implicit in the plan, especially to understand if costs are front-running expected benefits
• Operating cash flow trends over time, rather than its value in any single month

These indicators reveal whether execution is eroding room to maneuver that, over time, directly impacts Enterprise Value and the ability to sustain the plan in front of the board.

3. Signals of execution risk

Early in the year, the most underestimated risk is operational. Many plans work only if a sequence of events unfolds without friction, and the right KPIs measure precisely this fragility.
In particular, it's useful to observe:

• The gap between assumed and actual collection timing, because even modest DSO variations can have significant cumulative effects
• The level of revenue and cash concentration on a few clients or on milestones not yet under control
• The plan's dependency on future events not directly governable—such as deal closures, contract renewals, or expected funding rounds

These KPIs don't indicate something is going wrong—they show how exposed the plan is to variables not under control.

A B2B SaaS scale-up with €12M in ARR and 15 months of runway can be perfectly on track with its February budget while simultaneously having no visibility into which path is truly maximizing Enterprise Value.

The plan calls for 35% annual growth, 110% Net Revenue Retention, and new customer acquisition concentrated in the mid-market segment. Monthly burn is under control, CAC Payback is within expected bounds, and the first few weeks confirm the trajectory. Everything appears to be working.

But management has no way of knowing what would happen if:

• Trial-to-paid conversion rates degraded by 15% due to shifting market conditions
• Average close times on enterprise deals lengthened by 25 days
• A competitor began aggressively targeting the mid-market with more aggressive pricing
• Reference interest rates continued making access to capital more expensive
• Retention in certain verticals began showing signs of instability not yet visible in aggregate data

Each of these variables, taken individually, can be absorbed. But which combination, if occurring simultaneously, would compromise Enterprise Value rather than merely slow growth? And more importantly, which decisions made today—on go-to-market, pricing, cost structure, capital allocation—would reduce exposure to these scenarios without sacrificing growth opportunities?

Without simulating alternative scenarios, management continues operating as if the plan were the only possible trajectory. With simulations, it becomes clear which levers are truly under control, which depend on external factors, and which combinations of strategic choices build more value even when the context moves unexpectedly.

The question, then, isn't "are we meeting budget?" but "have we evaluated whether the decisions we're making today maximize Enterprise Value relative to possible alternatives, considering even what we can't control?"

Scenario simulations serve exactly this purpose: creating a structured space to explore alternatives, test critical decisions, and measure the impact of choices before they become urgent.

This is why we partner with CFOs and leadership teams in guided simulation sessions, built on real data and the decision levers most relevant to each scale-up's specific context, with expert support to help interpret weak signals and translate them into informed decisions—while room to maneuver still exists.

Contact our team to understand how to manage your company's decisions and not compromise its growth. Book a free assessment