Goldman Sachs AI Disruptive Technology Symposium 2026 in London – Why Decision Optimization Is Becoming a Competitive Advantage

At the invitation of Goldman Sachs in London, I had the opportunity to attend the Goldman Sachs Disruptive Technology Symposium 2026. In numerous discussions with investors, technology companies, and decision-makers from various industries, a clear common thread emerged: It is not “more projects” that drive growth—but rather the mathematically optimal selection, sequencing, and combination of investment initiatives.

Why StratePlan Belongs in the Context of Disruptive Technologies

For us, the invitation to the Goldman Sachs Disruptive Technology Symposium underscores a crucial point: Disruption arises not only from new AI models or new technologies—but also from how companies make decisions.

This is exactly where StratePlan comes in. The mathematical optimization of CAPEX decisions transforms a process that, in many companies to this day, remains heavily influenced by individual evaluations, prioritization lists, budget rounds, and repeated recalculations.

Instead of evaluating individual investments one after another, StratePlan treats the entire CAPEX list as a mathematical decision-making problem and calculates which combination of investments generates the highest total value under the company’s real-world constraints.

The fact that this approach is being discussed at a symposium on disruptive technologies demonstrates just how significantly the focus is shifting: from AI as an analytical tool to AI and mathematical optimization as the foundation for better business decisions.

A recurring theme in many discussions

It was particularly striking how often a structural problem was confirmed: companies have long since stopped thinking solely in terms of short-term budgets and now plan in 3-, 4-, 5-, or 10-year cycles. This is precisely what creates a fundamental challenge: There is no single “right” investment decision, but rather dozens to hundreds of possible projects—all subject to budget, capacity, risk, and strategic constraints.

The crucial question is therefore no longer: Is Project A good? But rather: Which combination of all possible projects generates the maximum ROI under real-world constraints?

This is precisely where mathematical optimization comes into play.

As the number of projects, time periods, and constraints increases, the number of possible decision combinations rises dramatically. What still seems manageable in small portfolios through experience, Excel models, or traditional prioritization methods quickly evolves into a combinatorial decision-making problem.

Dr. Georg Schlesinger, a mathematician and member of the Schrödinger Institute at the University of Vienna, sums up the significance of this approach in the context of StratePlan:

“StratePlan addresses a mathematically fascinating topic: as the number of investment opportunities and constraints increases, complexity grows very rapidly. Mathematical optimization makes this complexity manageable and enables us to systematically derive better decisions from a multitude of possible combinations.”

Dr. Georg Schlesinger
Mathematician and member of the Schrödinger Institute, University of Vienna

In my view, this is precisely where one of the key shifts in corporate management lies: complexity does not need to be reduced in order to make decisions. It can be made mathematically manageable.

StratePlan addresses precisely this issue. Instead of merely evaluating projects individually and then sorting them by priority, the investment portfolio is viewed as a cohesive mathematical decision-making problem.

The impact begins with the starting point: the global optimum

Those who view their investment landscape as a complete system and calculate the optimal initial portfolio position based on it do not start from a random or historically evolved prioritization, but rather from a mathematically optimized portfolio.

This initial position fundamentally changes strategic management because every subsequent decision builds upon an already optimized portfolio.

The difference is significant: Traditional investment decisions often ask which projects appear attractive on their own. Mathematical optimization, on the other hand, asks which combination of all available projects generates the highest total value under the given constraints.

The Optimization Cycle Over Several Years

This effect is particularly pronounced over multi-year planning horizons. Annual re-optimization of the project portfolio creates a continuous strategic cycle:

1. All investment projects are modeled collectively as a portfolio.
2. The combination with the highest mathematical value is implemented.
3. Unallocated liquidity is retained and carried forward to the next fiscal year.
4. New projects, changes in the operating environment, and additional available budget are incorporated into the next calculation.
5. The entire portfolio is mathematically optimized once again.

Over time, this creates a system of continuous portfolio optimization. Capital, impact, and strategic clarity reinforce one another.

Over 3, 4, 5, or 10 years, this creates a cycle of optimization, budget growth, and strategic impact—and thus a structural competitive advantage.

Important: Projects do not disappear—they are better positioned

One point that has been confirmed in many discussions: In an optimized investment system, projects are not necessarily “discarded.”

They can be reprioritized, postponed, or strategically repositioned so that they make the greatest possible contribution to the overall portfolio at the optimal time and under the right constraints.

This also changes the discussion among management and in the boardroom: Individual projects no longer compete against each other in isolation. Instead, it becomes clear which combination generates the highest enterprise value at any given time.

From the Existing CAPEX List to the Optimal Investment Portfolio

No new data infrastructure is required for this optimization. The starting point for StratePlan is the company’s existing CAPEX list.

The investment projects contained therein are fed into a mathematical optimization model along with their relevant parameters—such as investment volume, expected return, duration, priorities, and dependencies—as well as company-specific constraints.

The key question is then no longer:

Which individual CAPEX project is the best?

But rather:

Which combination of existing CAPEX projects generates the highest total value given the existing budgets, time frames, and constraints?

StratePlan makes the underlying combinatorial complexity mathematically manageable and calculates the highest-value portfolio composition from the existing investment projects.

This shifts the focus from the isolated evaluation of individual projects to the optimization of the entire CAPEX portfolio.

This is crucial: A company may have very good individual projects and still allocate capital inefficiently if the overall impact of their combination is not considered.

This is precisely where StratePlan’s strength lies: Companies don’t have to reinvent their investment planning. We optimize what already exists.

Conclusion: The quality of decision-making becomes a competitive advantage

The Goldman Sachs Disruptive Technology Symposium made it clear to me once again that the decisive competitive advantage of the coming years will not arise solely from new technologies, but from systematically better decisions in capital allocation.

The more complex companies, investment programs, and strategic interdependencies become, the more important it is to not only understand this complexity but also to master it mathematically.

Those who mathematically optimize their investment portfolios not only maximize the value of individual projects—they also optimize the allocation of capital across the entire company and over several years.

“Every company has the right to maximize profit.”

This does not mean short-term profit maximization, but rather the best possible use of capital, resources, and time—based on a decision-making logic that does justice to the real combinatorial nature of investments.

Complexity is not the problem here. The crucial question is whether we have the mathematics to make it manageable.