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Calculating high-ROI use cases with AI - why economic success is not a gut feeling
Organizations talk constantly about ROI. In practice, however, precisely those decisions that have the greatest impact on returns are surprisingly rarely calculated systematically.
Investments, digitalization projects, transformation programs, or efficiency initiatives are often evaluated individually, prioritized in workshops, or negotiated politically. The result is not an optimal ROI – but an accepted compromise.
Calculate a high-ROI use case online now
The misconception behind classic ROI calculations
A single use case can show a high ROI and still be part of an overall suboptimal decision. Economic impact does not arise in isolation, but from the interaction of multiple initiatives.
High-ROI decisions are therefore always portfolio decisions.
The invisible decision space behind use cases
As soon as multiple use cases are considered simultaneously, decisions are no longer isolated, but form an exponentially growing decision space.
With N use cases, there are 2N possible combinations of investments, savings, risks, and impacts. This exact space remains invisible in classic ROI approaches.
High ROI: guess or calculate?
Anyone who compares only a handful of variants is effectively deciding at random within a gigantic decision space. The truly best ROI is usually found where no one is looking.
From the best use case to the best combination
A high-ROI use case is not the one with the highest individual return, but the one that, in combination with other initiatives, generates the greatest overall benefit.
StratePlan calculates the entire decision space and derives from it:
The one project combination that generates the maximum overall benefit.
This is exactly where classic ROI logic diverges from true decision intelligence. AI does not calculate the best individual ROI, but the globally optimal ROI across all permissible combinations.
Why Excel, experience, and workshops are not enough
Humans – regardless of expertise – can only grasp a very small fraction of exponential decision spaces.
Committees therefore optimize not ROI, but the comprehensibility of decisions. This is human – but not economically optimal.
A scale comparison for high-ROI decisions
A scale comparison:
our Milky Way and a decision space with “only” 50 use cases
of 1.125 quadrillion possible combinations
High-ROI decisions must be made ex ante
The greatest economic leverage does not come from course correction, but from ex-ante optimization. Before capital is committed, projects are launched, or resources are allocated.
High ROI is not the result of good justification – but of good calculation.
Conclusion for decision-makers
Anyone who wants to identify high-ROI use cases must not compare individual initiatives. They must make the entire decision space visible and have it calculated.
Decisions made without AI are not necessarily wrong – but they ignore a large share of the possible ROI.
FAQ – Calculating High-ROI Use Cases with AI
What does “calculating a high-ROI use case” actually mean?
It means not only evaluating potential initiatives (use cases) individually, but systematically quantifying their economic impact: benefits, costs, risks, dependencies, and time dynamics. The goal is not “a plausible business case,” but a robust, comparable ROI basis for prioritized decisions.
Why is a classic ROI calculation per use case often insufficient?
Because ROI rarely arises in isolation. Use cases compete for budget, resources, and attention – and influence each other (synergies, cannibalization, sequencing effects). A purely individual view can lead to a suboptimal overall decision.
What does AI do differently in high-ROI calculations compared to Excel?
Excel typically compares only a few manually selected variants. AI can model the decision space as a portfolio and evaluate a very large number of combinations (2n possible portfolios for n use cases). This makes ROI-optimal combinations visible that do not appear in classic workshops or spreadsheets.
What input data is required for a robust ROI calculation?
Typically: investment (CapEx/OpEx), expected effects (revenue, cost reduction, quality, lead time), time dynamics (ramp-up), risks/uncertainties, constraints (budget, personnel, capacities), as well as dependencies and sequencing. The clearer the objectives and constraints, the more robust the results.
How does AI handle uncertainty?
Uncertainty can be modeled as ranges, scenarios, or probability assumptions. From this, robust recommendations can be derived, for example via sensitivity analyses: which use-case combination remains economically viable even under deviations?
Is “high ROI” only the highest return – or the best overall portfolio?
In an executive context, high ROI is primarily a portfolio objective: the combination that generates the maximum overall benefit within defined constraints. A use case with a high individual return may still be inferior in the portfolio if it blocks resources or prevents better combinations.
Which areas are particularly suited to AI-based high-ROI calculations?
Typical areas include digitalization portfolios, automation, supply chain, production, sales/marketing, IT modernization, energy/ESG initiatives, and public investment programs. Anywhere many competing initiatives must be prioritized simultaneously.
How quickly can actionable results be achieved?
Often in two steps: first, a structured model with the most relevant use cases and constraints (for clear prioritization), followed by a deeper analysis of the top candidates (for investment decisions and implementation planning).
Does AI replace management decision-making?
No. AI provides transparency, comparability, and the calculated best combination under defined objectives. The decision remains with management – but based on measurable impact rather than gut feeling.
What is the key takeaway?
Anyone seeking to identify high-ROI use cases should not rely solely on individual calculations, but should calculate the entire decision space as a portfolio. Economic success then arises from calculation – not from gut feeling.
Dr. Igor Kadoshchuk is a computer scientist, algorithm architect, and one of the leading minds behind mAInthink's optimization and decision-making algorithms. As scientific director of the StratePlan™ and DeepAnT platforms, he combines in-depth mathematical research with practical applications in project portfolio optimization, business, finance, and public administration.
He holds a PhD in computer science from the renowned Moscow Institute of Physics and Technology (MIPT), where he also taught as a professor of computer engineering and mathematics. He has decades of experience developing highly complex mathematical models for project portfolio optimization and financial systems, investment planning, and strategic decision-making. His professional career includes leading positions such as Head of IT at Gazprombank and Director of Project Management at TransTeleCom.
Dr. Kadoshchuk writes on the mAInthink AI Blog. Kadoshchuk on:
- Algorithmic strategy optimization
- New methods for calculating ROI and impact
- Project portfolio optimization beyond traditional tools
- The limits of human decision-making – and how AI overcomes them
His aim: to calculate strategy, not estimate it.
His contributions combine scientific precision with clear, understandable language – always with the goal of making complex decision-making spaces transparent, manageable, and measurable.