Why financial planning is becoming a strategic leadership discipline

Since planning supports various management needs, the idea of a single, integrated planning process becomes challenging to sustain. In theory, a unified planning model linking sales, production, and logistics planning (along with the resulting financial planning) is compelling. However, organizational complexity often undermines this ideal:

• The overall management objectives differ significantly:
  Some processes aim to establish commitment within the organization, such as performance management or incentive targets. Others seek to generate an unbiased “best guess”, for example through rolling forecasts. Still others focus on strategic prioritization and capital allocation.
• Planning horizons vary widely:
  Planning horizons vary widely: From short-term operational planning (e.g., production and staffing) to long-term financial investment and capital-allocation planning.
• Required levels of detail differ:
  Operational production planning may need to go down to batch/SKU level in various production lines, while sales forecasts may be aggregated by customer or product family. Long-term investment and allocation planning is typically established at a higher, case-based level containing selected key figures (internal rate of return, return on investment, etc.).

Due to these factors, a single integrated model is rarely realistic. Establishing such a model would require a potentially complex planning framework, and ongoing maintenance would be challenged by changes such as reorganizations, acquisitions, divestments, and shifting responsibilities.

A more practical solution is a set of purpose-specific planning processes, each designed to meet its own management objective. The finance function’s role will then be to ensure coherence and coordination (not to be mistaken for reconciliation) across models and processes where necessary.

The finance organization’s value in driving planning and budgeting should be measured against the above criteria, as they reflect the effectiveness of the company’s planning processes.

Traditional finance functions often prioritize accurate reporting of historical results - ensuring the numbers are correct and providing detailed explanations for deviations from the budget. While necessary, the forward-looking value of analyzing past deviations is limited.

Real value creation begins with scenario modeling and simulation. By modeling possible future scenarios for the company’s earnings generation, organizations gain data-driven insights into opportunities and risks. This enables informed responses to questions such as:

• What happens if raw material prices rise by 10%?
• What is the impact of losing customer X?
• How do we make the most of a 10% market growth?
• What is the effect of opening a new factory in country Y?

When modelling scenarios, we not only analyze the effects of changes in external parameters but also examine and model internal production structures, linking external dynamics to internal impacts.

By utilizing an appropriate time horizon in our models - which varies by industry - companies can extend their effective decision window and gain real manoeuvring room to address challenges and leverage opportunities.

How detailed do the models need to be, and on which theoretical foundation should they be built? There is no unique answer to these questions; however, it is essential to start systematic simulations of future scenarios for the company’s potential window of opportunity and learn from experience.

Simple simulations in tools like Excel may serve as a starting point, focusing on key drivers. Over time, these simple models can evolve into more advanced simulation frameworks, enabling the use of statistical and machine-learning models to adapt market data combined with driver-based planning to model internal operational structures.

The key is relevance over detail.

A recurring challenge for many companies is the lack of systematic links between analysis on one hand, and derived decisions and actions on the other. Many organizations invest substantial resources in integrated financial planning models, only to fail at embedding them in decision-making processes. In the worst cases, decision processes occur parallel to model predictions, which are then reduced to mere nice-to suggestions.

Why does this happen? We believe that companies do not place sufficient emphasis on designing explicit management processes that align the various components of the planning system, both in terms of processes and data, into a coherent management framework. This framework must be presented and addressed in the relevant decision-making forums authorized to make decisions. For example, how do changes in the sales forecast influence operational plans and the latest budget? Who is authorized to make decisions and adjust direction? In an increasingly uncertain world, it is crucial for companies to maintain full control of their management processes and ensure that integrated management decisions can be communicated to the organization clearly, quickly, and systematically.

Senior leadership plays a decisive role by emphasizing the necessity of robust and effective management processes, committing to these processes, and avoiding ad hoc, parallel decision-making.

There is also a need for stronger finance competencies to translate data into actionable recommendations for management. Too often, business partnering remains focused on controlling rather than creating forward-looking business value.

Forecasts and plans are valuable for supporting the company’s various management needs, as described above.

However, companies are often tempted to link budgets or forecasts to incentive schemes, such as bonus models. This creates undesirable effects, as it automatically introduces bias into forecasts and plans - for instance, in the form of ‘sandbagging’.

How often have we seen the commercial organization maintain deliberately low expectations for achievable sales growth in the budget, simply because it will directly impact the sales team’s bonus program?

Therefore, incentive structures should be decoupled from the company’s financial planning processes, including budgets and forecasts. Instead, bonuses can be tied to relative or absolute measures such as market growth or market share - for example, benchmarked against competitors.

The rapid development of technologies for budgeting, planning, forecasting, reporting, and simulation - from integrated financial planning systems to AI in finance - is unmistakable. To benefit from these tools, companies must prioritize efficiency and speed: the demand for faster, higher-quality analysis leaves no room for poor data quality or time-consuming, non-value-adding manual processes.

Dedicated planning processes, statistical forecasting models, and detailed driver-based models all rely on one critical prerequisite: the availability of relevant data of sufficiently high quality in near real time.

Many well-designed planning models have been discarded and rendered useless due to insufficiently reliable input data - whether master data or transactional data. This undermines trust in results and makes them ineffective as decision-making support. Before deploying new models, companies must analyse data availability and quality – this is especially also important before considering applying AI-based solutions.

Finance plays a central role in this work, and it should be properly recognized and prioritized.

Paradoxically, despite growing needs, we observe a very slow and often suboptimal adoption of these technologies - most recently reflected in the cautious rollout of AI. Many pilot projects in finance exist, but only a few progress to broad implementation across the organization.

We believe that a key reason for the lack of effective adoption is the conservative approach to competencies in many finance departments. The majority of employees in finance come from traditional accounting, finance, or audit backgrounds. As a result, there is strong expertise in these areas, while fields such as statistical methods, data modeling, machine learning, and data governance are only sparsely represented. Our assertion is that finance’s ability to bridge the gap - both in terms of data with the IT department and in terms of business understanding with the wider organization - is, in many cases, insufficient.

Finance must therefore redefine its role and critically evaluate the competencies needed to evolve beyond traditional deliverables.

Currently, most finance functions lack the skills to drive AI strategically or to lead AI programs at the enterprise level. This applies to both GenAI and Agentic AI, as well as the still limited use of traditional machine learning.

In a world generating vast amounts of internal and external data, many companies still utilize only a fraction of the information available for planning and management.

The real question is no longer whether finance can create more value through planning, but whether organizations are willing to embed planning at the core of how decisions are made.

Summary 

This article explores how finance’s planning responsibilities can unlock significant value when strengthened and better integrated with the business. Growing demands around data, modelling and skills are reshaping finance’s role and enabling closer collaboration with decision-makers. With focused efforts in the planning domain, CFOs can materially increase their overall value contribution. However, planning must be embedded across the organization rather than run as a standalone finance exercise. Ultimately, each CFO must define the right priorities and roadmap in close alignment with the business-partnering organization.