AI Decision Optimization Online Service

Executive Summary

Organizations today rarely fail due to a lack of data, a lack of expertise or unclear goals. They fail because of decisions in highly complex decision-making spaces. The more projects, strategies, budgets, risks and constraints that have to be considered at the same time, the greater the gap between human decision-making ability and objectively optimal decisions.

The Decision Optimization Online Service closes precisely this gap. It transforms decision-making from a discursive, experience-based process into a mathematically based, standardized online service, that fully calculates complex decisions - instead of simplifying or estimating them.

At the center is StratePlan: a ready-to-use decision and optimization infrastructure, that makes decision optimization available as an online tool as a service.

To the decision optimization online service

1. The real problem: decisions under complexity

Modern management decisions are rarely binary. They include:

• several options for action at the same time
• limited budgets and resources
• Dependencies between decisions
• different time horizons
• Risks, uncertainties and volatility
• strategic, regulatory and ESG constraints

Each additional option multiplies the decision space. Even with just a few variables, there are thousands, in realistic decision-making situations quickly Millions to billions of possible decision options.

This space is structurally unmanageable for human intuition.

2. Why traditional decision-making systematically fails

2.1 Gut feeling, experience and committee logic

They dominate in practice:

• experience-based management decisions
• Discussions in committees and steering committees
• Scenarios with a greatly reduced number of variants

These approaches are inevitably subjective. They only consider a minimal section of the actual decision-making space.

What feels right, is almost always not mathematically optimal.

2.2 The error of simplification

In order to be able to make decisions, complexity is often reduced.

Mathematically, this leads to systematic wrong decisions. Complexity must be calculated - not simplified.

The consequences:

• hidden opportunity costs
• suboptimal budget and resource allocation
• robust decisions are overlooked
• long-term loss of value

3. Decision optimization: from judgment to calculation

Decision optimization does not mean

• more data
• more reports
• more meetings

But more: the objective calculation of the best decision under all relevant constraints.

The focus thus shifts from human judgment to mathematically optimized decision-making logic.

4. StratePlan: Decisions as a mathematical system

StratePlan was developed to translate complex decision problems into formalized optimization models.

Among other things, it calculates

• which option should be chosen
• which combination of decisions is optimal
• how budgets, resources and time can be used optimally
• which decisions must be excluded despite plausibility

StratePlan does not replace managers. It supplements them with a level of decision-making logic, that human cognition is structurally unable to provide.

5. Decision optimization as an online service

The Decision Optimization Online Service makes this capability for the first time standardized, scalable and immediately usable.

The process is deliberately simple:

• structured data upload
• algorithmic optimization
• clear decision recommendation

Instead of workshops and discussions:

Upload → calculation → optimal decision.

6. Data upload: minimalist and secure

The service works with reduced data and only processes structured values.

Supported formats:

• .xlsx
• .xml
• .json

Typical inputs:

• Options or decision variables
• Costs, benefits, effects
• Restrictions, budgets, dependencies

Texts, narratives or subjective evaluations are deliberately not processed.

7. Data protection through reduction

The Decision Optimization Online Service follows the principle of structural data minimization.

• no sensitive content
• no strategic texts
• no conclusions about internal discussions

Optionally, data can be shared with customer-specific multipliers can be abstracted or scaled.

8. Mathematical optimization instead of intuition

Complex decisions belong to the class of NP-hard optimization problems.

Characteristic:

• exponentially growing decision space
• not solvable by linear models
• unsuitable for manual scenarios

The service combines:

• exact optimization methods
• heuristic search algorithms
• hybrid AI approaches

The goal is not theoretical perfection but a practically optimal decision with maximum real effect.

9. Measurable added value

Real applications show that

• up to 60% higher ROI for the same budget
• more robust decisions under uncertainty
• Reduction of wrong decisions
• transparent decision-making logic

10. Who is the service suitable for?

The Decision Optimization Online Service is aimed at organizations with:

• complex investment or strategy decisions
• limited budgets
• high decision-making responsibility
• high uncertainty and conflicting goals

11. Supplementation instead of replacement

The service does not replace existing systems such as

• ERP
• BI or controlling systems
• Management committees

It supplements them with a mathematically based decision-making level, which has been missing until now.

12. Conclusion: Decisions are the real bottleneck

It is not data, processes or tools that limit organizations, but the quality of decisions.

The Decision Optimization Online Service makes this quality for the first time calculable, reproducible and scalable.

Not as a method.
Not as advice.
But as a ready-to-use online service for well-founded decisions in complex systems

To the Decision Optimization Online Service: Calculate complex decisions online and make optimal decisions objectively