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Impact ROI


Impact ROI

Why the true return of StratePlan is not in IT, but in better decisions

Executive Summary

Traditional ROI calculations fall short when it comes to strategic decision-making systems. They measure efficiency gains, not impact gains. StratePlan's Impact ROI starts at a different point: the systematic reduction of wrong decisions in large decision-making areas. This is precisely where the greatest economic effects arise.

This article shows why Impact ROI is the only sensible evaluation logic for decision optimization - and how this ROI can be derived mathematically.

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1. The basic problem with classic ROI logic

Traditional ROI models answer questions such as:

  • How much time does a system save?
  • How many employees does it replace?
  • How much does it reduce operating costs?

This logic works for:

  • ERP automation
  • Process digitization
  • Reporting tools

It fails as soon as the economic lever is not efficiency, but in decision quality.

StratePlan does not automate work.
StratePlan changes decisions.

2. Impact ROI - the right perspective

Impact ROI does not measure productivity, but avoidable economic damage.

The central assumption is:

In complex decision spaces, the greatest losses are not caused by inefficiency, but through systematically suboptimal decisions.

The economic benefit results from

  • better allocation of capital
  • Avoidance of wrong priorities
  • Reduction of misalignments in budgets, portfolios and programs

3. The economic logic behind StratePlan

StratePlan is based on a clear economic assumption:

  • Decisions do not work in isolation
  • Effects are not linear
  • Combinations explode exponentially (2n problem)

Humans cannot fully think through these decision spaces.
StratePlan calculates them.

4. The impact ROI calculation approach (simplified)

The economic effect can be formally described as follows:

Impact = Σ (Vi ×Fi × ΔQ )

The following applies:

  • Vi = financial volume of a decision field
  • Fi = wrong decision rate without system
  • ΔQ = Quality improvement of the decision through StratePlan

Even small improvements in ΔQ generate massive effects for large volumes.

5. Example: Public budgets & corporate portfolios

Typical decision volumes:

  • Investment programs: Billions
  • Project portfolios: Hundreds of millions
  • Strategic allocations: multi-year

Conservatively assumed:

  • Mismanagement: 1-3 %
  • Decision improvement through StratePlan: 30-50 %

Result:

  • Double-digit to triple-digit millions in avoided misallocation losses
  • for investments in the low single-digit million range

6. Why Impact ROI is so high

StratePlan's ROI does not scale with the software, but with it:

  1. Decision volume
  2. Complexity of the decision space
  3. Systemic error rate of human planning

The larger and more complex the system, the higher the impact ROI.

This is why impact ROI is particularly high for

  • Governments & ministries
  • Corporations with many projects
  • Infrastructure, energy and industry portfolios

7. Differentiation: No classic IT ROI

Important:

  • StratePlan does not save minutes
  • StratePlan does not replace Excel reports
  • StratePlan does not automate meetings

StratePlan prevents wrong decisions before they are made.

The ROI is generated ex ante, not ex post.

8. Governance implication

Impact ROI means

  • Decisions become calculable
  • Effects can be simulated
  • Discussions become quantifiable

StratePlan shifts the decision-making process:

  • from intuition
  • to mathematical optimality

This is not an IT project, but a governance upgrade.

9. Conclusion

The greatest economic leverage of modern organizations does not lie in more data, but in better decisions.

Impact ROI makes this lever visible.

StratePlan is not a cost factor, but a decision multiplier - and that is precisely why its ROI lies where classic models can no longer explain it.

Determine impact ROI

Author: Dr. Igor Kadoshchuk CTO mAInthink

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.

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