Why Most Income Statements Don’t Enable Decision-Making (And Why This Is Not a Tooling Issue)

An opinion piece by Mehdi Ben Salah, co-founder of Beyond Plans

In many organizations, the forecast income statement (P&L) plays a central role in steering committees and strategic trade-offs. Yet it is rarely used as a true decision-making tool.

The issue lies neither in the quality of the figures nor in the level of sophistication of the tools. It lies in how the exercise is designed: as an aggregated outcome, produced at the end of the process, describing a trajectory without making its economic mechanisms explicit.

Under these conditions, adjusting assumptions often amounts to moving high-level levers without clearly understanding what actually drives changes in performance. Decisions become intuitive, late, and difficult to justify.

As long as value creation drivers — volumes, prices, mix, productivity, cost structures — are not explicitly formalized, decision-making remains constrained. At best, the trajectory is observed; at worst, it is justified after the fact.

Reframing financial analysis as an explicit, recalculable, and shared economic model is a prerequisite for any scenario-based planning, rolling forecast, or modern FP&A framework.

This article offers a perspective on why so many financial decisions remain fragile — and under what conditions an income statement can once again become a genuine arbitration tool.

1. A Financial Deliverable… or an Economic Model

In many organizations, financial forecasting is built as a summary deliverable. Assumptions are produced elsewhere, consolidated, adjusted, and then aggregated into a document that freezes a trajectory.

In this logic, the exercise primarily serves reporting requirements. It allows comparison between an expected outcome and a budget or explains high-level variances, but it is not designed to be questioned, manipulated, or recalculated continuously.

By contrast, decision-useful analysis relies on an explicit economic model. The goal is no longer to align revenue and cost lines, but to formalize cause-and-effect relationships between operational assumptions and financial performance.

The difference is structural:

• in a descriptive framework, numbers are central;

• in an explanatory framework, assumptions structure the discussion.

2. Driver-Based Planning as a Minimum Condition for Decision-Making

Making decisions requires understanding what drives performance. A forecast built on high-level assumptions — growth rates, cost envelopes, margin targets — does not allow organizations to identify the true levers of value creation.

A driver-based approach rebuilds financial analysis from its fundamental economic drivers: volumes, prices, mix, cost structures, and allocation rules. The result becomes a consequence, not an abstract target.

This shift profoundly changes the nature of discussions. Debates move away from global adjustments and focus instead on concrete, actionable levers.

Micro case – Arbitrating growth versus margin

Consider a classic situation: an executive committee must choose between accelerating commercial growth or preserving short-term margins.

In a descriptive framework, the discussion compares two aggregated financial trajectories. The debate remains binary and largely intuitive.

In practice, these limitations lead to late decisions, suboptimal investments, recurring tensions between finance and business teams, or abrupt year-end adjustments. The cost is not only financial; it is also organizational and strategic.

In an explanatory framework, the discussion changes fundamentally. The impact of growth is broken down into its components: incremental volumes, required commercial effort, pricing pressure, induced variable costs, and operational capacity.

The trade-off becomes structured, traceable, and fully owned.

Transition — When Tooling Becomes an Accelerator (and a Governance Question)

Once financial analysis is designed as an explicit and recalculable economic model, the role of tooling changes. The challenge is no longer to produce faster, but to make reasoning usable across the organization.

In this context, a planning platform such as Anaplan allows the model to evolve over time: versioned assumptions, comparable scenarios, rapid recalculations, and enhanced collaboration.

However, this acceleration raises a critical question: who can modify which drivers, at what time, and under what rules?

Without a clear framework, speed weakens decision-making instead of strengthening it.

Beyond Plans supports precisely this articulation between economic modeling, tooling, and governance — ensuring that technical sophistication genuinely serves decision quality.

When Uncertainty Becomes the Norm, Decision Quality Becomes a Competitive Advantage

Market volatility, geopolitical instability, inflationary pressure, and rapid disruption of business models mean assumptions become obsolete faster than decision cycles.

In this context, the challenge is no longer to produce a “correct” forecast, but to revise decisions quickly based on explicit and governed assumptions.

Artificial intelligence accelerates analysis and multiplies scenarios, but it does not fix a flawed economic model.

Without clear drivers, it industrializes the illusion of control. In other words:

AI does not correct a poor economic model. It executes it faster.

Conclusion — Governing Means Arbitrating

At executive committee level, the issue is not numerical accuracy, but the ability of figures to inform decision-making.

Whether we like it or not, the way an income statement is constructed shapes decisions. It does not merely reflect them.

A poorly designed income statement does not prevent decisions.

It shifts them toward intuition, influence, or power dynamics — outside a rational framework.

The challenge, therefore, is not to produce a better financial document, but to create the conditions for collective decision-making based on mechanisms that are understood, governed, and owned.