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Multi-project optimization with AI
Executive Summary
Today, companies and public organizations are not faced with a data problem, but with a decision problem Decision problem. Strategic portfolios consist of dozens to hundreds of projects running in parallel - each with their own budgets each with their own budgets, dependencies, risks, regulatory requirements and impact targets. Traditional planning and controlling approaches (Excel, scenario planning, linear prioritization) reach their structural limits here, as the decision space grows exponentially.
AI-based multi-project optimization addresses precisely this deficit: it does not calculate individual projects better, but optimizes the entire portfolio as a system.
The core problem
With each additional project, the possible combinations of start times, budget allocations, dependencies and conflicting goals multiply, Dependencies and conflicting objectives. Above a certain size, it is mathematically impossible to evaluate all alternatives manually or or rule-based evaluation. Decisions are then inevitably based on heuristics, empirical values or political enforceability Enforceability - not on global optimality.
The AI approach
Modern decision AI models multi-project portfolios as high-dimensional optimization problems. Instead of estimating scenarios, billions of possible portfolio combinations are analyzed algorithmically. The goal is not "one good project", but the best overall decision under real-life constraints such as budget limits, Resource scarcity, risk appetite, ESG objectives or strategic impact.
As a result, KI:
- which combination of projects achieves the highest overall ROI,
- which seemingly attractive projects worsen the portfolio overall,
- where small budget or sequence changes have a massive impact,
- and which decisions remain robust in the face of uncertainty.
Strategic added value for C-level and boards
Multi-project optimization with AI shifts decision-making from opinion to calculation. For board members, CFOs and public decision-makers, this means
- Transparent, comprehensible decisions instead of gut feeling
- Measurable impact and ROI optimization at portfolio level
- Reduction of systematic wrong decisions and opportunity costs
- Ability to make decisions even in the face of extreme complexity
Conclusion
AI does not replace leadership - it replaces uncertainty with predictability. In a world of growing project landscapes multi-project optimization with AI is not a technological extra, but a strategic imperative. Companies that start today, To calculate decisions instead of estimating them will gain a sustainable structural advantage.
In the context of platforms such as mAInthink GmbH and its StratePlan optimization system, this becomes clear: The future of project and portfolio management is not faster - it is optimal.
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.