Calculate KPIs Using AI Ex Ante

We don’t just measure KPIs—we calculate the best possible decision beforehand

What if board members, CEOs, and CFOs didn’t have to wait until after an investment decision to determine whether their KPIs had been met, but could instead calculate—even before the decision is made—which combination of investments promises the best chance of achieving their goals?

This is precisely where a fundamental paradigm shift in corporate management begins.

For decades, Key Performance Indicators (KPIs) were considered central management tools: measure, compare, report, and adjust.

Revenue, EBIT, ROI, return on investment, capacity utilization, liquidity, and sustainability metrics form the basis for numerous management decisions.

But in a world of growing complexity, limited investment budgets, and an exponential increase in decision-making options, ex post measurement alone is no longer sufficient.

The problem is not that KPIs are wrong.

The problem is that they often only reveal how successful a decision that has already been made was—but not whether a different decision would have led to a better overall outcome.

For board members, CEOs, CFOs, and public policymakers, this raises a new strategic question:

Which combination of planned investments, projects, and measures maximizes our economic or strategic targets within the available resources?

With StratePlan, this question becomes a mathematical optimization problem.

KPIs remain relevant in this context. However, they are not viewed exclusively in retrospect; rather, they can be incorporated into portfolio optimization as targets and constraints even before a decision is made.

1. The Limits of Traditional KPI Management

KPIs create transparency. They enable comparisons, highlight trends, and support both operational and strategic corporate management.

However, traditional KPI management is often organized retrospectively.

A company invests in new production facilities, IT systems, locations, or product developments. Months or years later, defined metrics are used to assess whether the expected results have been achieved.

The crucial question often remains unanswered:

Would a different combination of the same planned investments—within the same budget—have yielded a better overall result?

This is precisely where the limitations of an exclusively metric-oriented approach lie.

Individual business units can successfully improve their respective KPIs without this automatically leading to optimal management of the company as a whole.

• Sales maximizes revenue and market share.
• Controlling focuses on costs and return on investment.
• Production optimizes capacity and utilization.
• IT prioritizes digitalization and system availability.
• Sustainability management pursues emissions and ESG goals.

Each of these objectives can be valid.

However, if they are pursued in isolation, conflicting goals may arise.

For example, a project with a particularly high individual return may tie up so much investment capital that several other projects with a higher overall economic contribution can no longer be financed.

Optimizing individual KPIs therefore does not necessarily lead to the optimization of the entire investment portfolio.

2. The Key Fallacy: Optimizing Individual Projects Instead of the Overall Portfolio

In many companies, investment decisions are made based on individual business cases.

Each project is evaluated for its economic viability, strategic importance, and risks.

The projects are then prioritized and approved based on the available budget.

This sounds logical, but it has a mathematical flaw.

The most economically attractive individual project is not necessarily part of the most economically optimal combination of projects.

A simplified example

A company has a CAPEX budget of 10 million euros and is considering three investment projects.

Project A has the highest single net value contribution.

If Project A is selected, 3 million euros of the budget will remain. This is not enough for either of the other two projects.

The total net value contribution is 4 million euros.

If, on the other hand, Projects B and C are implemented together, the entire budget of 10 million euros will be used.

The combined net value contribution amounts to 6 million euros.

In this example, the optimized portfolio thus achieves a 50% higher net value contribution—with the same CAPEX budget.

This example illustrates the basic principle of mathematical portfolio optimization: It is not the best individual investment that matters, but rather the best combination of all available investment alternatives.

3. Why Strategic Decision Spaces Grow Exponentially

The challenge becomes significantly more complex as the number of planned projects increases.

In a simplified binary decision model, each project can either be implemented or not implemented.

Thus, with N independently selectable projects, there are^{theoretically 2^N} possible project combinations.

• 10 projects: 1,024 combinations
• 20 projects: 1,048,576 combinations
• 30 projects: 1,073,741,824 combinations
• 40 projects: 1,099,511,627,776 combinations
• 50 projects: 1,125,899,906,842,624 combinations
• 100 projects: approximately 1.27 ×^10³⁰ combinations

A company with 50 planned investment projects is therefore not merely considering 50 decision alternatives.

It operates within a theoretical decision space of more than 1.125 quadrillion possible project combinations.

Not every one of these combinations is permissible due to budget, resource, or project dependencies. Nevertheless, the mathematical complexity remains considerable.

A board of directors cannot fully grasp this decision space through discussion or intuition.

Even traditional prioritization lists or manually developed Excel scenarios typically represent only a small subset of the possible combinations.

The central challenge, therefore, is to determine the economically or strategically best permissible combination from an exponentially large number of possible project combinations.

4. Measure ex post or optimize ex ante?

The difference between traditional KPI-based management and mathematical ex ante optimization lies in the timing and nature of decision support.

Ex post: What were the effects of our decisions?

Traditional KPI analysis examines the results of measures that have already been implemented.

Typical questions include:

• What ROI have our investments generated?
• How has our EBIT margin evolved?
• Which projects exceeded their budgets?
• What was our return on investment?
• Which strategic goals were achieved?

This information is essential for controlling, reporting, and corporate management.

However, it does not automatically answer whether a different combination of investments would have been more successful.

Ex ante: What decision should we make?

Ex-ante optimization takes place before the investment decision is made.

It evaluates planned projects based on their expected economic outcomes and available resources.

Typical questions include:

• Which combination of projects maximizes our total net value contribution?
• Which investments should be implemented within our CAPEX budget?
• Which combination of projects achieves the highest level of defined goal attainment?
• How do the results change with a lower budget?
• Which projects are competing for the same resources?
• Which investments should be considered if strategic priorities change?

Ex-post KPIs show what has been achieved. Ex-ante optimization examines which decision yields the best expected outcome before it is implemented.

Both approaches complement each other.

Retrospective measurement remains necessary to compare actual results with the original assumptions and to improve future decisions.

5. KPIs Become Target Values for Mathematical Optimization

StratePlan does not replace KPIs.

Rather, relevant metrics can be incorporated into the mathematical modeling of strategic decisions from the outset.

These include, for example:

• Net Contribution: Expected net economic value contribution
• Portfolio ROI: Expected return on investment for the entire portfolio
• CAPEX: Available investment budget
• OPEX: Ongoing operating expenses
• Risk: Defined risk limits or risk assessments
• Resources: Personnel, capacity, and technical availability
• Impact: Strategic, social, or environmental impact

Depending on the optimization model, these variables are taken into account as an objective function, weighting parameters, or constraints.

It is important not to offset different target variables against one another in an uncontrolled manner.

Economic, environmental, and societal objectives require transparent evaluation criteria and, where necessary, explicitly defined weightings.

Mathematical optimization then determines which combination of projects best fulfills the defined objectives within the given constraints.

The result is not merely another KPI analysis, but a concretely calculated investment portfolio.

6. How StratePlan Calculates Investment Decisions

Getting started with mathematical portfolio optimization can be relatively straightforward.

For a basic economic optimization model, StratePlan first requires three pieces of information per project:

1. Project ID: Unique project identifier
2. Expenses: Planned expenditures or investments
3. Income: Expected revenue or economic returns

This information can be sourced, for example, from existing Excel spreadsheets, investment plans, or business cases.

Based on the defined objective function and the available budget, StratePlan evaluates possible project combinations.

Advanced models can account for additional parameters:

• Project dependencies
• Technical or strategic weightings
• Multi-year investment budgets
• Resource constraints
• Time constraints
• Risk and sustainability criteria

StratePlan combines mathematical optimization methods from operations research with AI-based approaches to efficiently analyze large combinatorial decision spaces.

The calculation delivers an optimized project combination based on the input data and model parameters.

Depending on the problem and the method used, the result may be a provably optimal or an approximately optimal solution.

The key innovation lies in not only evaluating the economic viability of individual projects but also optimizing their combined impact within a shared investment budget.

7. Live in the Board Meeting: What happens if we change our KPIs?

The particular strategic benefit arises when mathematical portfolio optimization is directly integrated into management’s decision-making processes.

Let’s imagine a board meeting at an industrial company.

On the agenda are 100 planned CAPEX projects, whose total funding requirements significantly exceed the available investment budget.

StratePlan first calculates an optimized combination of projects based on the defined objectives and constraints.

Now the strategic discussion begins.

Scenario A: Maximize Net Contribution

The CFO asks:

“Which combination of projects will generate the highest expected net value contribution with our existing CAPEX budget?”

StratePlan calculates a corresponding portfolio.

Management sees which projects are selected, how much budget is required, and what expected net value contribution the overall portfolio will generate.

Scenario B: Reduce the CAPEX Budget

The CEO asks:

“What happens if we have to reduce our investment budget by 15%?”

The budget constraint is adjusted.

StratePlan recalculates the portfolio.

The board can then examine which projects will be eliminated, how the expected value contribution changes, and which investments will remain part of the portfolio.

Scenario C: Give Greater Weight to Strategic Projects

A board member asks:

“How does our investment portfolio change if we prioritize digitalization and energy efficiency more heavily?”

The corresponding weighting parameters are adjusted.

After recalculating, the results can be compared with the previous scenarios.

Scenario D: Additional Investment Funds

The CFO wants to know:

“What additional economic contribution could we achieve with an additional 20 million euros in CAPEX?”

The budget is increased, and the portfolio is optimized again.

This allows management to compare the modeled additional value contribution with the required capital investment.

Change parameters. Recalculate. Compare scenarios. Make more informed decisions.

This transforms a static investment report into a dynamic basis for decision-making for the entire management team.

8. Sensitivity Analysis: How robust is our investment strategy?

A single optimization calculation initially provides a result based on the defined assumptions.

The analysis becomes particularly valuable from a strategic perspective when different parameters are systematically varied.

A sensitivity analysis examines, for example:

• Which projects remain selected under different CAPEX budgets?
• How does the net value contribution change as project costs rise?
• Which investments are particularly sensitive to changes in revenue expectations?
• Which projects are excluded when risk weighting is increased?
• Which strategic priorities have the greatest impact on the portfolio composition?

This reveals which investment decisions within the model are particularly robust.

Other projects may only be selected under certain economic or strategic assumptions.

Ex-ante optimization thus not only provides a calculated combination of projects but also enables the systematic examination of alternative decision-making conditions.

9. Opportunity Costs: The Economic Value of Unselected Alternatives

Every investment decision entails opportunity costs.

If capital is allocated to a specific project, it is no longer available for other investments.

This is particularly true when CAPEX budgets are limited.

A company may select economically attractive individual projects and yet fail to realize a significant additional value contribution if the combination of these projects is not optimal.

Mathematical portfolio optimization makes such differences between the alternatives under consideration quantifiable.

A comparison between the originally planned investment portfolio and an optimized portfolio can reveal:

• How high the expected net value contribution of both portfolios is
• Which projects are selected differently
• How much CAPEX is required in each case
• Which economic opportunities in the original portfolio remain untapped

The difference between the modeled results illustrates the economic improvement potential under the underlying assumptions.

The true value of ex-ante optimization therefore lies not in improved KPIs, but in the ability to identify better investment combinations before capital is committed.

10. Case Study: Klybeck Site in Basel

The StratePlan case study of the Klybeck site in Basel demonstrates just how significant the economic difference can be between an existing plan and a mathematically optimized portfolio.

As part of the portfolio optimization for Central Real Estate AG and Swiss Life AG, various investment and project combinations were mathematically analyzed.

The modeled result:

• Original return on investment: 7.0%
• Optimized return on investment: 11.4%
• Relative return improvement: approximately 63%
• Additional modeled economic value contribution: approximately 30 million CHF

The improvement was modeled by altering the combination of existing investment alternatives while maintaining the same budget.

These figures illustrate the potential of mathematical portfolio optimization. They do not constitute a general guarantee of returns for other projects or companies.

The key insight: It is not necessarily more investment capital, but rather a better combination of investments that can increase the overall economic contribution.

11. Why Human Experience Alone Is Not Enough

Experienced board members, CFOs, and investment managers possess extensive expertise and a deep understanding of their companies.

This expertise remains indispensable.

However, even many years of experience cannot fully encompass exponentially large decision spaces.

Even with just seven binary project decisions, there are 128 possible combinations.

With 20 projects, there are more than one million.

With 50 projects, there are more than a quadrillion.

Human strength lies in setting strategic goals, understanding economic relationships, and assessing the quality of assumptions.

The strength of mathematical optimization lies in systematically examining large numbers of possible combinations.

The combination of management experience, reliable technical data, and mathematical optimization creates a more sound basis for decision-making than any of these components alone.

12. From Business Intelligence to Decision Intelligence

Business Intelligence primarily answers questions about a company’s economic performance.

What happened? Which key metrics have changed? Where are there deviations from the plan?

Decision Intelligence expands this perspective to include systematic support for concrete decisions.

What alternative courses of action are available? What constraints apply? Which combination maximizes the defined objectives?

StratePlan positions itself in this field as a mathematical system for portfolio and decision optimization.

It adds an additional layer to existing ERP, controlling, and business intelligence systems:

The calculation of optimized decisions prior to their implementation.

A company does not necessarily have to replace its existing IT systems to do this.

The necessary project data can initially be provided via simple spreadsheets. Depending on requirements, advanced integrations are possible via suitable interfaces.

This makes mathematical decision optimization accessible even to companies that have traditionally relied primarily on classic investment lists and Excel models.

13. Conclusion: KPIs Remain Important—But the Decision Comes First

KPIs will continue to be an indispensable component of professional corporate management.

They enable the monitoring of economic developments, the tracking of strategic goals, and the evaluation of actual results achieved.

However, on their own, they do not answer the crucial question of whether a company has allocated its available investment capital to its projects in the best possible way.

This is precisely where mathematical ex-ante optimization comes into play.

StratePlan analyzes possible investment combinations while taking into account economic goals, limited budgets, and defined constraints.

The results can be calculated, modified, and compared across different scenarios before an investment decision is made.

The future of strategic corporate management therefore lies not in simply measuring KPIs faster, but in mathematically optimizing the decisions that influence these KPIs in advance.

This changes the central management question.

Instead of asking exclusively:

“How successful were our investments?”

the additional question is:

“Which combination of our planned investments would yield the highest overall economic contribution under the given conditions?”

The answer to this is not a traditional reporting task.

It is a task of mathematical portfolio optimization.

StratePlan—Optimize decisions ex ante. Measure KPIs ex post. Use capital more intelligently.

How much more economic value can you unlock from your CAPEX budget?

Calculate which combination of your planned investment projects, within your available budget, can achieve the highest expected net value contribution.

Same projects. Same budget. Optimized combination. Better results.

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