Show Me the Money: The Real Business Value of AI for Enterprises

Artificial intelligence has moved beyond the experimental stage. According to the latest NTT DATA Business Solutions Transformation Study 2026, 60% of organisations now see AI as the primary driver of their transformation initiatives. At the same time, 76% have already implemented AI systematically within transformation programmes. Yet this is where a fundamental tension emerges: while AI is increasingly viewed as a catalyst for innovation, data quality, governance and transparency remain among the biggest challenges organisations face.

The latest study, SAP Value of AI: Oxford Economics 2026, paints a similar picture. Organisations increasingly expect measurable returns on AI investments. However, 67% are still unconvinced that their AI implementations are delivering their full ROI potential. At the same time, only 3% feel fully prepared for the next stage of AI maturity: the adoption of agentic AI.

This is where the contradiction becomes apparent. While businesses recognise AI as a strategic technology for the future, many initiatives remain limited to individual applications. Chatbots are introduced, assistants are piloted and selected processes are automated. The resulting business value often remains localised and incremental.

During SYBIT's Expert Talk, “Show Me the Money – The Real Business Value of AI”, Dominik Rotter, Senior Director Digital Products at MULTIVAC, Ron Boes, Director Innovation & Portfolio at SYBIT, and Jonas Degener, Business Consultant at SYBIT, explored exactly this challenge: why the real business value of AI does not emerge from isolated use cases, but from the ability to scale AI across data, processes and systems.

AI Has Moved Beyond the Experimental Phase

“Where do organisations currently stand when it comes to AI?”

Ron Boes answered this question with an observation that reflects many industries today: levels of maturity vary dramatically.

While some organisations have already established comprehensive data and AI architectures and are running productive solutions, others are still searching for direction or addressing their first governance and use case questions. At the same time, the technology is evolving at a pace many companies have never experienced before.

This creates the risk of a growing divide.

Organisations that begin building scalable foundations today are creating advantages that will be difficult to catch up on later through isolated AI tools alone.

Successful AI Projects Do Not Start with AI

One of the most important messages from the discussion came from Dominik Rotter.

Many organisations still follow a familiar pattern:

"We have a technology. Now let's find a problem for it."

This often leads to AI being implemented without creating genuine business value.

Instead, every project should begin with a different question:

"What problem are we actually trying to solve?"

Only once the problem has been clearly defined should a use case be developed and the right technological solution discussed.

Where AI Is Already Creating Measurable Business Value Today

A wide range of use cases demonstrates that AI has already moved far beyond experimentation.

In service, requests are automatically classified, prioritised and processed. In sales, AI supports customer meeting preparation and the consolidation of relevant customer information. In commerce, entirely new opportunities are emerging around digital advisory services, personalisation, cross-selling and upselling.

The practical examples presented by MULTIVAC were particularly compelling.

Rethinking Knowledge Management

MULTIVAC now operates an internal AI assistant that makes knowledge from different sources such as Confluence, SharePoint, OneDrive and other systems accessible. Employees can ask questions about sales processes, product information or internal procedures and receive immediate answers based on existing company knowledge.

The key insight is that the value was not created by building entirely new knowledge repositories. Instead, existing content was unlocked and made intelligently accessible.

Predictive Maintenance as a Competitive Differentiator

A second example comes from the service environment.

Using AI, large volumes of machine, sensor and service data are analysed to identify potential issues before they occur. Service technicians are proactively alerted to critical machines, enabling them to focus on genuinely relevant cases instead of manually monitoring hundreds of assets.

For MULTIVAC, the ability to proactively notify customers about potential issues and prevent downtime is becoming a genuine competitive differentiator.

AI Must Deliver More Than Efficiency Gains

Many AI initiatives begin with productivity, automation and cost reduction. Processes become faster, information is made available more quickly and employees are relieved of routine work.

However, the Expert Talk made it clear that this perspective is no longer sufficient.

Dominik Rotter identified three strategic objectives for AI initiatives: increasing efficiency, differentiating from competitors and creating entirely new revenue opportunities. While the first two objectives are already visible in many organisations today, the third is becoming increasingly important.

The key question is no longer:

"Where can we save costs?"

but:

"Where can we create new business with AI?"

From Efficiency to Monetisation

One of the most interesting parts of the discussion focused on emerging digital business models.

Dominik Rotter used knowledge as an example. Traditionally, organisations have sold training courses, consultancy services and educational programmes. AI now creates new opportunities to deliver knowledge itself as a service. Customers can access expert knowledge precisely when they need it rather than through fixed training formats. Knowledge itself becomes a digital product.

Data-driven services can also be reimagined through AI. Machine and production data analysis, for example, has traditionally been labour-intensive. AI now enables large parts of these analyses to be generated automatically and delivered at scale, creating services that are more economical to provide while reaching a broader customer base.

Customers Do Not Pay for AI

Another important takeaway emerged during the discussion:

Customers do not pay for AI.

Customers pay for tangible value.

If AI helps prevent machine downtime, accelerate access to knowledge, simplify purchasing processes or improve decision-making, it creates benefits customers are willing to pay for.

The technology itself often remains invisible.

The Next Phase of AI Will Be Defined by Growth

This may well define the next stage of AI adoption.

The first wave of AI was primarily focused on efficiency, automation and productivity. The next wave will focus far more heavily on new services, digital products and additional revenue streams.

Put differently:

The first wave of AI asked how work could become more efficient. The next wave will ask how businesses can grow with AI.

Data Remains Critical, but the Rules Are Changing

The Transformation Study 2026 clearly shows that data quality remains one of the most important success factors in transformation initiatives. At the same time, data quality issues continue to be one of the most common challenges organisations encounter.

Yet the discussion revealed an important shift.

Historically, organisations invested enormous effort in structuring data. Information had to be categorised, tagged and stored in the right systems.

Modern AI changes that logic.

Ron Boes and Dominik Rotter explained how today's systems can combine information from multiple sources and use it contextually. Whether content sits in Confluence, SharePoint or elsewhere is becoming less important. What matters is whether the information exists, is accessible and can be trusted.

In short: data quality is becoming more important, while traditional data structuring is becoming less important.

This represents one of the fundamental changes AI is driving across organisations today.

AI Is Not Just Changing Applications. It Is Changing the Architecture Behind Them.

One of the most fascinating aspects of the discussion centred on what is changing beneath the surface.

Many organisations still see AI as a standalone feature, chatbot or assistant.

In reality, a new generation of enterprise architectures is emerging.

Jonas Degener described the growing importance of Data Layers and scalable Enterprise Architectures. The goal is to make data from different systems centrally available and reusable across a wide range of applications.

Instead of building every AI use case separately, organisations are creating shared foundations that support:

• AI assistants
• Customer portals
• Analytics platforms
• Mobile applications
• Digital services
• Automation processes

The discussion also explored new concepts such as the Model Context Protocol (MCP). The idea behind MCP is to make company data and business capabilities available to AI systems through standardised interfaces. This will allow AI assistants to interact directly with products, knowledge bases, services and even sales systems.

The implications are significant.

AI is increasingly evolving from an isolated feature into a design principle for modern applications. Organisations are no longer simply adding AI capabilities. They are fundamentally rethinking data, processes and digital products.

From Individual Use Cases to Scalable AI Adoption

How can organisations achieve this in practice?

Jonas Degener describes the approach using a simple formula:

"Start vertically, scale horizontally."

Companies begin with a specific use case and implement it end-to-end, from data sources and model logic through to the final application.

At the same time, they establish a technical foundation that can support future use cases.

Step by step, isolated AI projects evolve into scalable platforms.

The real investment is therefore not in the first chatbot or assistant, but in creating a foundation for future innovation.

Conclusion: The Real Business Value Begins After the First Use Case

The discussion made one thing abundantly clear: many organisations are still focusing on the wrong question.

The real business value of AI emerges when organisations understand their most important challenges, make their data usable and create a foundation that allows AI to scale across multiple processes and functions over time.

That is where the next level of competition will be won.

And that is precisely where the gap is beginning to widen between organisations that already view AI as a strategic capability and those that are still experimenting with isolated tools.