From artificial intelligence to economic value: Matthias Zwick’s experience
8 August 2026Integrating artificial intelligence into business processes does not simply mean identifying a promising technology. It means understanding where it can generate value, which costs it can reduce, which timelines it can shorten, and how it can contribute to a company’s financial performance.
This is one of the main perspectives developed by Matthias Zwick during the Executive Master in Artificial Intelligence for Business at Bologna Business School.
As Director, Strategic Partnerships & Ecosystem, Matthias works on strategic partnerships and ecosystems and now applies what he learned to partnership and product decisions related to agentic AI. During the Master, he developed an approach that starts from technological potential but always assesses it through the lens of a sustainable business case.
His experience reflects one of the elements that characterise education at Bologna Business School: the dialogue between disciplines, managerial skills, and practical experience, with the aim of addressing business transformation from complementary perspectives.
For Matthias, the most significant change generated by the Master was not technical, but economic.
“Before the program, I could discuss the capabilities of artificial intelligence in theoretical terms. The Master gave me a rigorous method for translating a model’s capabilities into a business case: understanding where a solution can genuinely reduce costs or process time, where the return-on-investment calculation does not hold up, and where the term ‘AI-powered’ is simply a label with no real impact on the P&L,” Matthias explains.
The program also gave him the language and frameworks needed to address AI monetisation as an explicit and independent topic within strategic discussions.
“It is not enough to consider the efficiency generated by a technology. You need to ask how a specific capability can translate into revenue, margins, or measurable results. This distinction separates an interesting pitch from an initiative that can secure funding. It is the perspective I now apply to every partnership or product decision associated with agentic AI.”
This approach was put into practice in the development of a business case for an agentic AI application at Amazon Business, designed to automate supplier onboarding within enterprise procurement systems.
The objective was to reduce integration time from several months to just a few days. What changed as a result of the program was above all the level of rigour with which Matthias assessed the trade-offs between internal development and collaboration with external partners, together with the construction of the cost model.
“I did not limit myself to demonstrating that the technology could work. I quantified precisely where automation would generate economic value, building an argument capable of withstanding rigorous scrutiny.”
Reducing integration time from months to days was therefore not simply an operational result, but the starting point for demonstrating the economic value of automation and supporting the decision with verifiable data.
This method derives directly from the frameworks explored during the Master. The project also helped shape the direction of Matthias’s next professional step, towards a role entirely focused on agentic AI applied to enterprise procurement.
The same focus on economic value also informs the advice Matthias gives to professionals who want to integrate artificial intelligence into their work but do not know where to begin.
“You need to stop looking for a generic artificial intelligence use case and start instead with the worst process in your organisation. Every business function has at least one workflow that everyone considers slow, manual, and expensive. That is where you should begin.”
Before developing a pilot project, Matthias believes it is necessary to explain in a single sentence which cost or which amount of time will be removed through automation.
“Without that answer, you do not yet have a business case: you only have a technology demonstration. The economic argument should come before the pilot.”
Building the business case for artificial intelligence
Matthias’s experience highlights one of the central issues in the adoption of artificial intelligence within companies: the gap between technological possibility and sustainable application.
Advanced models and agentic systems can transform complex activities, but their use requires an approach that brings together technology, strategy, organisation, and finance. The question is not only whether a process can be automated, but also what benefit it can generate, which resources it requires, and how it fits within business priorities.
From this perspective, an interdisciplinary approach makes it possible to examine innovation from different viewpoints, connecting theory and practice, academic expertise and managerial experience. This is particularly relevant in a rapidly evolving field such as artificial intelligence applied to business.
Matthias’s journey shows how education can influence not only the acquisition of new knowledge, but also the way a decision is structured: defining the problem, measuring expected value, evaluating alternatives, and building a proposal capable of standing up to scrutiny based on data, costs, and financial objectives.
For an artificial intelligence project to become part of a company’s strategy, technology must be accompanied by a precise question: what tangible outcome can it generate for the business?
