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Decision tool AI: Why modern companies need to calculate decisions - not estimate them
Introduction: The real problem of modern companies
Companies today have more data, reports and dashboards than ever before. ERP systems, FP&A tools, BI platforms and classic AI applications deliver high-quality forecasts, scenarios and key figures.
And yet a central problem remains unsolved:
The crucial question is not "What is happening?" - but "What should we decide?"
This is precisely where a modern AI-based decision tool comes in.
Data ≠ Decision
Traditional systems do an excellent job:
- They describe reality
- They analyze the past
- They predict the future
What they do not do structurally:
- The calculation of optimal decisions in complex decision spaces
As soon as decisions affect several projects, budgets, dependencies, risks and strategic goals at the same time, human intuition - just like linear software logic - reaches its limits.
When the decision space explodes
The problem becomes apparent even in seemingly straightforward situations:
- 10-15 projects
- Fixed investment budget
- Risk and ESG requirements
- Time dependencies
- Strategic priorities
The number of possible project combinations grows exponentially (2ⁿ). From around 7 projects, the decision space is no longer intuitively manageable.
Consequence:
Management decisions are not optimal - but plausible, politically or historically influenced.
Why traditional AI is not enough
Many companies are already using AI. However, their use is usually fragmented:
- Forecasting models for sales, costs or risks
- Classifications and scores
- Partial optimization of individual processes
The problem:
Forecasting is not a decision.
Classic AI answers the question:
What could happen?
A real decision tool answers:
What is the best decision under all conditions?
What a real AI decision tool must do
A modern AI-based decision tool differs fundamentally from analysis or forecasting software:
1. Holistic optimization
- Considers all projects simultaneously
- Takes dependencies, budgets, risks and goals into account
- Optimizes not partial aspects, but the entire decision space
2. Mathematical optimality
- Decisions are calculated, not estimated
- Results are reproducible, auditable and explainable
- No black box recommendations, but robust decision logic
3. Results suitable for management
- Clear decision options instead of a flood of scenarios
- Transparency for the board & supervisory board
- Traceability for compliance and governance
Concrete benefits for companies
The use of a genuine AI decision-making tool leads to measurable effects in practice:
- +20-60% ROI improvement through better project combinations
- Avoidance of misallocations in the millions
- Reduction of strategic risks
- Massive time savings in decision-making processes
- Fewer political discussions - more facts
Or to put it another way:
Decisions are faster, safer and more economical.
Fields of application for AI decision-making tools
The benefits unfold wherever decisions are expensive:
- Strategic investment planning
- Project & infrastructure portfolios
- Industrial, energy, real estate and financial projects
- Major public programs and budget decisions
The greater the complexity and financial volume, the greater the leverage.
Conclusion: The most expensive decision is the wrong one
In a world of exponential complexity, experience alone is no longer enough. Nor are reports, dashboards or forecasts enough.
The most expensive decision is the one that was not optimally calculated.
Companies that systematically calculate their decisions in future will gain a structural competitive advantage Competitive advantage - not through better data, but through better decisions.
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.