AI Calculates High-ROI Use Cases—Why Business Success Isn't Just a Gut Feeling

Executive Summary: High ROI Is Achieved Through Mathematical Portfolio Optimization

How much additional economic potential lies within your existing investment projects, digitalization initiatives, and strategic use cases—without having to increase your budget?

Companies invest substantial sums in digitalization, automation, artificial intelligence, production facilities, process optimization, and strategic transformation.

Nearly every one of these investments is evaluated using economic metrics. Return on investment (ROI), payback period, capital requirements, and expected value contribution are standard tools of professional corporate management.

Yet one crucial question often remains unanswered:

Which combination of all planned use cases achieves the highest overall economic contribution within the available budget?

In practice, use cases are often evaluated individually, prioritized in workshops, or ranked based on their expected return.

The result may be plausible. However, it does not necessarily represent the best overall portfolio from an economic standpoint.

This is exactly where StratePlan comes in.

The technology combines mathematical optimization methods from operations research with AI-driven approaches to systematically analyze large combinatorial decision spaces.

The goal is not to identify the most attractive individual use case, but to calculate the economically optimal combination of all available investment alternatives.

1. The Misconception Behind Traditional ROI Calculations

Return on investment (ROI) is one of the most important metrics for evaluating economic measures.

It describes the ratio between the achieved or expected economic outcome and the capital invested to achieve it.

A simplified ROI calculation is as follows:

ROI = (Return − Investment Costs) / Investment Costs × 100

The exact definition may vary depending on the company and the specific use case.

This metric helps assess the economic viability of individual investments.

But this is precisely where a frequently underestimated problem arises:

A use case with a high individual ROI may not necessarily be part of the most economically sound overall portfolio.

The reason lies in the interactions between different initiatives.

Use cases compete for investment budgets, personnel resources, technical capacity, and available time.

Selecting one project can prevent several other projects from being implemented together.

That is why it is not enough to rank investments solely based on their individual ROI.

The key management question is: Which combination of our use cases generates the highest overall economic contribution with the available capital?

2. High ROI is a portfolio decision—not a competition among individual projects

A simple example illustrates the difference between the highest individual return and the economically best combination of projects.

A company has an investment budget of 10 million euros.

There are three use cases to choose from:

Use Case A has the highest individual ROI at 100%.

If only this use case is implemented, the net value added is 4 million euros with a capital investment of 4 million euros.

If, on the other hand, Use Cases B and C are implemented together, the entire investment budget of 10 million euros is utilized.

The combined net value contribution amounts to 8 million euros.

The portfolio consisting of B and C thus generates twice the absolute net value contribution, even though both projects individually have a lower ROI.

However, the portfolio ROI for B and C is 80%, which is lower than the individual ROI of A.

This example illustrates an important distinction:

Maximum portfolio ROI and maximum absolute net value contribution are not the same objective function.

Management must determine which objective to optimize before performing the calculation.

For strategic capital allocation, maximizing the total net value contribution within a limited budget can be particularly relevant.

3. The Invisible Decision Space Behind High-ROI Use Cases

As soon as multiple use cases are available for selection at the same time, a combinatorial decision space emerges.

In a simplified binary model, each use case can either be implemented or not implemented.

With N independently selectable use cases, there are theoretically 2^N possible combinations.

• 7 use cases: 2^7 = 128 combinations
• 10 use cases: 2^10 = 1,024 combinations
• 20 use cases: 2^20 = 1,048,576 combinations
• 30 use cases: 2^30 = 1,073,741,824 combinations
• 40 use cases: 2^40 = 1,099,511,627,776 combinations
• 50 use cases: 2^50 = 1,125,899,906,842,624 combinations
• 100 use cases: 2^100 = approximately 1.27 × 10^30 combinations

A portfolio with 50 use cases thus has a theoretical decision space of approximately 1.126 quadrillion possible combinations.

Not all of these combinations are feasible due to budget constraints, resource limitations, or project dependencies.

Nevertheless, this order of magnitude illustrates why traditional workshops and manual prioritization methods can capture only a limited subset of the possible decisions.

The challenge is not in evaluating 50 use cases. It lies in determining the economically optimal permissible portfolio from the possible combinations of these 50 use cases.

4. Why an ROI Ranking Does Not Automatically Lead to the Best Result

In traditional investment processes, use cases are often prioritized based on their expected return on investment.

The projects with the highest ROI are at the top of the list and are implemented first.

This approach is simple and straightforward.

However, it does not automatically account for the optimal allocation of available investment capital.

Three structural problems arise particularly frequently in this context.

First: Budget Competition

A single use case can consume a significant portion of the investment budget.

As a result, several other economically attractive measures can no longer be implemented.

Second: Project dependencies

Individual use cases may be interdependent.

For example, AI-based production optimization can only be meaningfully implemented once a specific data infrastructure has been established.

An isolated ROI assessment does not automatically account for such interdependencies.

Third: Resource constraints

Even if sufficient capital is available, personnel or technical capacities may limit the simultaneous implementation of multiple measures.

The economically optimal portfolio must therefore not only be financially viable but also organizationally feasible.

An ROI ranking evaluates individual projects. Mathematical portfolio optimization examines their best possible combination given the actual constraints.

5. Calculate high-ROI use cases ex ante rather than evaluating them ex post

In many companies, the economic success of investments is not comprehensively measured until after they have been implemented.

The controlling department then examines whether the planned cost savings, revenue increases, or efficiency gains have actually materialized.

This ex-post analysis remains indispensable.

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

Mathematical ex-ante optimization, therefore, begins even before the investment decision is made.

It examines which combination of planned measures, within the available budget, achieves the highest expected overall economic contribution.

Relevant factors can be taken into account for this purpose:

• Capital expenditures (CAPEX)
• Ongoing operating expenses (OPEX)
• Expected revenue
• Planned cost savings
• Project dependencies
• Resource and capacity constraints
• Strategic Priorities
• Risk and time constraints

This information is used to create a mathematical optimization model.

The result is a calculated project combination that has been optimized according to the defined objective function and constraints.

Economic leverage is thus generated before capital is committed—not only upon the subsequent evaluation of the results.

6. How StratePlan Mathematically Optimizes High-ROI Use Cases

StratePlan was developed to analyze complex investment and project portfolios using mathematical optimization methods.

The technology combines approaches from operations research with AI-driven methods to efficiently identify high-performing project combinations.

For a simple economic optimization model, three pieces of information per use case are initially sufficient:

1. Project ID: Unique identifier for the use case
2. Expenses: Planned expenditures or investments
3. Income: Expected revenue or monetized economic returns

The data can be provided, for example, via an existing Excel spreadsheet.

In addition, the available investment budget is defined.

StratePlan examines possible combinations of use cases and calculates a project portfolio based on the selected objective function.

With “Maximize Net Contribution,” the focus is on maximizing the total expected net value contribution.

The portfolio ROI can also be reported as an economic metric.

Advanced models can account for additional constraints and evaluation parameters.

Depending on the problem structure and the optimization method used, a provably optimal or an approximately optimal solution can be determined.

StratePlan thus does not merely provide a ranking of use cases, but rather a mathematically calculated combination of measures within the available budget.

7. Live in the Board Meeting: Which use cases generate the highest economic contribution?

The particular strategic benefit arises when portfolio optimization is directly integrated into management decisions.

Let’s imagine a company that has identified 80 digitalization, automation, and AI use cases.

The total of all planned investments exceeds the available budget.

Management must decide which initiatives should be implemented.

StratePlan first calculates an optimized combination of projects based on existing economic assumptions.

Various scenarios can then be examined during the board meeting.

Scenario A: Maximize Net Contribution

The CFO asks:

“Which combination of our use cases achieves the highest expected net value contribution with the existing investment budget?”

StratePlan calculates a corresponding project combination.

Management receives an overview of the selected measures, the required capital investment, and the expected total economic contribution.

Scenario B: Reduce the Investment Budget

The CEO asks:

“Which use cases should we implement if we have to reduce our budget by 20%?”

The budget constraint is adjusted.

StratePlan recalculates the portfolio.

Management can see which measures will be eliminated and how the expected net value contribution changes.

Scenario C: Give greater weight to AI and automation projects

Another board member asks:

“What changes if we strategically prioritize AI and automation projects more highly?”

The corresponding weighting parameters are adjusted.

The portfolio is recalculated and compared with the previous scenarios.

Scenario D: Additional Investment Capital

The CFO wants to know:

“What additional economic contribution could we achieve with an additional 5 million euros in investment budget?”

The budget is increased, and a new optimization is performed.

This allows the modeled additional value contribution to be compared with the additional capital investment.

Change parameters. Recalculate the portfolio. Compare economic impacts. Make more informed decisions.

This transforms a traditional use-case list into a dynamic foundation for strategic investment decisions.

8. Why Excel, experience, and workshops alone aren’t enough

Excel is a powerful tool for profitability analyses, business cases, and investment planning.

Mathematical optimization methods can also be applied using appropriate solvers.

The challenge arises when dealing with large combinatorial decision spaces involving numerous binary variables and complex constraints.

A simple Excel prioritization does not automatically map the possible project combinations mathematically.

Workshops and management committees can also discuss only a limited number of scenarios.

This does not mean that human experience is worthless.

On the contrary: The quality of economic assumptions, the definition of strategic goals, and the assessment of risks require sound management expertise.

However, human experience alone cannot fully explore exponentially large decision spaces.

The combination of management experience, reliable project data, and mathematical optimization enables a more well-founded investment decision than isolated prioritization.

9. Sensitivity Analysis: How robust are our high-ROI use cases?

Investment decisions are based on assumptions about future economic developments.

Expected revenues may turn out to be lower. Project costs may rise. Resources may become scarcer. Strategic priorities may change.

Therefore, mathematical portfolio optimization should not consider only a single scenario.

By making targeted changes to the input parameters, different economic developments can be simulated.

Typical questions include:

• Which use cases remain attractive even with a smaller investment budget?
• Which projects will be excluded from the portfolio if costs rise?
• How does the net value contribution change when revenue expectations are lower?
• What combination of projects emerges when strategic weightings change?
• Which measures remain part of the portfolio under various scenarios?

Such a sensitivity analysis reveals how stable a calculated investment decision remains under changed assumptions.

A high ROI therefore means not only an attractive expected return but also an understanding of the conditions under which the economic results can be achieved.

10. Opportunity Costs: The Economic Value of Unselected Use Cases

Every investment decision ties up capital and resources.

If you finance a use case, you cannot simultaneously use that budget for other initiatives.

This results in opportunity costs.

In traditional prioritization methods, these costs often remain partially hidden.

A company may select several economically attractive projects and still leave a significant amount of additional value untapped.

Mathematical portfolio optimization makes it possible to compare the originally planned portfolio with an optimized combination of projects.

The following metrics can be compared:

• Total investment requirement
• Expected net value contribution
• Portfolio ROI
• Selected and non-selected use cases
• Remaining budget flexibility
• Economic Differences Between Alternative Portfolios

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

The greatest leverage often lies not in investing more capital, but in allocating existing capital to the economically superior combination of measures.

11. Case Study: Klybeck Site in Basel – Approximately 63% Higher Return on Investment

The StratePlan case study of the Klybeck site in Basel illustrates the potential that mathematical portfolio optimization can unlock.

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

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 changing the combination of existing investment alternatives while maintaining the same budget.

These project-specific results do not constitute a general guarantee of return.

The key finding: A better combination of existing investment alternatives can enable a significantly higher overall economic contribution—without an additional investment budget.

12. In which areas is high-ROI optimization particularly relevant?

Mathematical portfolio optimization is relevant wherever numerous investment opportunities compete for limited financial or operational resources.

AI and Digitalization Portfolios

Companies often identify numerous potential AI applications.

The challenge lies in selecting those use cases that generate the highest total economic contribution within the available budget.

Manufacturing and Production

Automation initiatives, machinery investments, capacity expansions, and process optimizations compete for the same CAPEX funds.

Mathematical portfolio optimization can help identify suitable combinations of investments.

IT Modernization

Cloud migration, ERP projects, cybersecurity, infrastructure modernization, and software development often must be implemented within limited IT budgets.

Project dependencies and resource constraints play a particularly important role in this context.

Energy and Sustainability

Energy efficiency measures, building renovations, on-site power generation, and decarbonization projects can be optimized collectively based on economic and strategic criteria.

Real Estate

Renovations, project developments, modernizations, and location investments can be considered within a shared capital budget.

Municipalities and Public Budgets

Cities, municipalities, and public institutions also face the challenge of financing numerous investment projects with limited budgetary resources.

In addition to economic metrics, technical considerations and social impact goals can also be incorporated into the optimization process.

The common thread among all use cases is the question of which combination of competing measures achieves the best-defined overall result given the existing constraints.

13. Conclusion: A High ROI Results from the Right Combination

A high return on investment is not a matter of chance.

It results from economically sound assumptions, clear objectives, and the appropriate allocation of limited resources.

The traditional individual evaluation of use cases remains important.

However, it does not automatically answer the question of which combination of all planned measures results in the economically best overall portfolio.

As the number of use cases increases, the theoretical decision space grows exponentially.

Even with just 50 projects, there are more than a quadrillion possible combinations.

This complexity requires specialized mathematical optimization methods.

StratePlan enables the systematic analysis of investment portfolios, taking into account economic objectives, available budgets, and defined constraints.

This enables companies to calculate alternative project combinations, compare different scenarios, and identify untapped economic potential.

The crucial question is not: Which use case has the highest individual ROI?

Rather: Which combination of our use cases generates the highest overall economic contribution with the available capital?

This is precisely where the transition from traditional use-case prioritization to mathematical portfolio optimization begins.

StratePlan – Same projects. Same budget. Optimal combination. Better results.

FAQ – Calculating High-ROI Use Cases with AI

What exactly does “calculating high-ROI use cases with AI” mean?

It means not evaluating planned investment and optimization measures solely on an individual basis, but rather analyzing their economic impact as a combined portfolio.

This process takes into account investment costs, expected returns, available budgets, and, where applicable, other constraints within a mathematical optimization model.

The goal is to identify a combination of projects that is particularly economically efficient.

Why isn’t a traditional ROI calculation per use case sufficient?

Because a high individual ROI does not automatically lead to the highest overall economic contribution.

Use cases compete for budget and resources. Furthermore, project dependencies and time constraints can influence their joint feasibility.

Mathematical portfolio optimization takes these interdependencies into account within a unified decision-making model.

How is StratePlan different from a traditional Excel prioritization?

Traditional Excel prioritization often ranks projects by ROI, cost-effectiveness, or strategic importance.

StratePlan, on the other hand, examines possible project combinations using specialized mathematical optimization methods.

This allows for the identification of portfolios that might otherwise be overlooked when relying solely on a ranking approach.

Excel itself can also solve optimization problems with suitable Solver add-ins. The actual differences in performance depend on the problem size, model structure, and the method used.

What data is needed for an initial calculation?

For a simple economic optimization model, three pieces of information are initially required for each use case:

• Project ID
• Expenses – planned expenditures
• Income – expected revenue or economic returns

In addition, the available investment budget is specified.

For more complex models, additional parameters such as project dependencies, resources, risks, and time constraints can be included.

Can StratePlan calculate the maximum portfolio ROI?

The economic objective must be clearly defined before the calculation.

Maximizing the absolute net value contribution and maximizing the portfolio ROI are different mathematical problems.

In the “Maximize Net Contribution” scenario, the total expected net value contribution of the portfolio is considered the target.

The portfolio ROI can be reported additionally and used to compare different scenarios.

How does mathematical optimization handle uncertainty?

Uncertain assumptions can be examined through different scenarios and sensitivity analyses.

For example, investment costs, revenue expectations, or available budgets can be adjusted, and the resulting portfolios can be recalculated.

For explicitly probabilistic risk optimization, additional mathematical models and corresponding input data are required.

Is the use case with the highest ROI automatically the best investment?

No.

A use case can generate a high individual return and yet tie up capital that would enable a higher absolute net value contribution in a different combination of projects.

Therefore, the economic objectives and the available constraints must be considered together.

For which companies is high-ROI portfolio optimization particularly relevant?

It is particularly relevant for companies with numerous competing investment projects and limited CAPEX budgets.

Typical areas of application include manufacturing, production, digitalization, IT, energy, real estate, and strategic transformation programs.

Public institutions can also use mathematical portfolio optimization when different projects must be selected under shared budget constraints.

How quickly can an initial portfolio optimization be performed?

If the necessary project data is already available, an initial calculation can be prepared with just a few input parameters.

The actual computation time depends on the number of projects, the problem structure, the constraints, and the optimization method used.

For more complex portfolios, preparing and validating the input data may take longer than the mathematical calculation itself.

Can I simulate different investment budgets?

Yes. Different budget limits can be modeled as alternative scenarios.

This allows you to examine which combination of projects is selected given a lower or higher investment budget and how the expected total economic contribution changes.

Does AI replace management’s decision?

No.

Management defines the strategic goals, economic assumptions, and constraints.

Based on this, StratePlan calculates an optimized combination of projects.

The final decision and responsibility remain with the relevant managers.

What is the most important difference between prioritization and portfolio optimization?

Prioritization ranks individual projects.

Portfolio optimization examines which combination of these projects achieves the best overall result under the defined constraints.

Therefore, the best ranking is not automatically the best investment portfolio.

How much more economic value can be derived from your use cases?

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

Start with your own project data and compare mathematically optimized investment scenarios.

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