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Blog main article:
Decision-making AI - why strategic decisions need to be calculated today
For decades, strategic decision-making was characterized by experience, intuition, Committee logic and Excel scenarios. This approach works as long as as long as the number of possible alternatives remains manageable.
In modern organizations - companies and municipalities alike - this is no longer the case. Investments, projects and measures compete for budget, resources, time and political or economic attention, Resources, time and political or economic attention. Decisions are no longer individual decisions. They are portfolio decisions.
The core problem of modern decision-making
As soon as there are several projects to choose from at the same time there is no linear decision-making process, but an exponentially growing decision space.
With N projects, there are not N decisions, but 2N possible combinations. This space can no longer be fully grasped by humans - regardless of regardless of experience, specialist knowledge or decision-making routines.
Guess or calculate?
This is exactly where AI-supported decision-making comes in. Not as a "consulting tool" or forecasting aid, but as a mathematical system, that explicitly calculates the entire decision space.
AI decision-making: from gut feeling to mathematical optimality
Modern decision-making AI does not consider individual projects in isolation, but evaluates every possible combination along defined target parameters: Impact, cost, risk, sustainability, resilience or strategic priorities.
StratePlan calculates the entire decision space and uses this to find
The one project combination that generates the maximum overall benefit.
The decisive factor: AI does not make decisions "on instinct". It systematically evaluates every permissible combination and identifies the global optimum - not a local one.
Why human decision-making processes fail here
Boards, committees and traditional decision-making processes inevitably operate within an extremely small section of the actual decision-making space. The majority of all better combinations remain invisible.
A size comparison that makes the problem tangible
A size comparison:
our Milky Way and a corporate decision space with "only" 50 projects
of 1.125 quadrillion possible project combinations
Ex-ante decision making instead of subsequent corrections
The decisive paradigm shift brought about by AI consists of optimizing decisions ex ante - before money is committed, projects are launched or political decisions are made.
Errors are no longer managed, but systematically avoided. Impact is not claimed, but calculated.
FAQ - Decision-making with AI
What does decision-making with AI actually mean?
Decision-making with AI describes the use of mathematical and algorithmic systems to systematically calculate complex decision spaces. In contrast to classic approaches AI not only evaluates individual options, but also all relevant combinations of measures and identifies the best solution within the defined objectives and restrictions.
How does AI decision-making differ from Excel or traditional analyses?
Excel and classic scenario analyses usually only compare a few manually selected variants. AI, on the other hand, can model and evaluate the entire decision space (2n combinations). This reveals solutions that are inevitably overlooked in workshops, committees or spreadsheets.
Is AI replacing the decision-maker?
No. AI does not replace responsibility, mandate or leadership. It provides transparency and calculated basis for decision-making. The decision remains with the human being - but on the basis of a resilient, comprehensible evaluation instead of gut feeling or limited variant testing.
For which decisions is decision-making AI particularly suitable?
AI is particularly suitable for portfolio and prioritization decisions with many competing projects, objectives and constraints - e.g. investment programs, budget allocation, project prioritization, resource conflicts or packages of measures with impact targets.
What does "ex ante" decision-making mean?
Ex ante means optimizing decisions before implementation - before the budget is committed, projects are launched or political or business paths are defined. This reduces costly readjustments and increases the impact per euro invested.
How reliable are the results of AI-supported decision-making?
Reliability depends on clearly defined target values, clean data and correct modeling. The advantage: results are reproducible, verifiable and transparent. Sensitivity analyses can show how robust a recommendation is against uncertainties.
Is decision-making AI only useful for large companies?
No. Organizations with limited budgets in particular benefit because AI helps to focus resources where the greatest impact is achieved. The decisive factor is not the size, but the complexity of the decision and the number of competing options.
Why is a decision made without calculation?
Because the decision space grows exponentially for many projects and people can only examine a small section. What is not calculated remains invisible - and the final combination selected is then often the result of a limited choice of variants instead of the mathematically best possible overall effect.
What is the most important conclusion for decision-makers?
AI-supported decision-making is an answer to modern complexity. It makes the decision space visible and makes it possible to identify the best combination of measures. Those who decide without AI are not automatically wrong - but inevitably incomplete.
Conclusion for decision-makers
AI in decision-making does not replace responsibility and no political or entrepreneurial mandate. However, it replaces uncertainty, flying blind and chance by mathematically sound transparency.
Anyone who makes complex decisions today without AI is not making the wrong decision - but inevitably incomplete.
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.