From Experiments to Real Systems: Five Market Shifts Reshaping MainlyAI’s World of Enterprise AI

Artificial intelligence is no longer a question of if enterprises will use it, but how they will make it work in practice.

Across industries, most large organizations now have access to powerful AI models. Many have run pilot projects. Some have built impressive demonstrations. Yet far fewer have succeeded in turning those experiments into reliable, secure, and repeatable systems that work inside real business processes.

This gap between promise and practice is where several important market shifts are now taking shape. Below, we outline five trends reshaping enterprise AI and explain why this part of the AI market is becoming increasingly important, including for companies such as MainlyAI that operate between AI experimentation and real-world operations.

1. The bottleneck has shifted from model access to production

For the past few years, access to advanced AI models was the main limitation. That limitation has largely disappeared.

Today, the harder problem is productionization. This means turning promising prototypes into systems that can be maintained, governed, and improved over time. Many organizations are learning that notebooks do not scale across teams, demos often fail when organizations change, and experimental setups break under real-world complexity.

As a result, value is shifting toward platforms that make it easier to build and operate AI inside organizations, especially where many teams, long time horizons, and changing requirements are involved.

What to watch

• Enterprises reducing the number of AI tools they use and moving toward more integrated platforms

• Greater focus on systems that are easy to maintain, reuse, and share across teams

2. AI is moving closer to real execution

A clear next phase for enterprise AI is the rise of so-called agentic workflows. These are AI systems that do not only generate answers, but also plan tasks and carry out actions across different tools and systems.

Instead of living mainly in chat interfaces, AI is increasingly built into everyday workflows. It can trigger actions, work with live data, and operate inside internal systems. As AI moves closer to execution, responsibility for outcomes can no longer be unclear or spread out. It must be clearly owned, understood, and governed inside real systems.

This changes what good AI infrastructure looks like. Transparency, control, and the ability to trace decisions become just as important as technical performance.

What to watch

• AI agents moving from experiments into small but frequent business processes

• Growing demand for tools that allow monitoring, approvals, and oversight of AI actions

3. Governance is becoming part of the buying decision

As AI systems move into daily operations, governance can no longer be added later.

Regulation is becoming stricter, especially in Europe. At the same time, governance expectations are increasingly built into purchasing decisions. Security, risk, and compliance teams are now often involved when AI tools are selected, together with engineering and product teams.

This favors platforms that make AI easier to understand for many stakeholders, easier to review, and easier to standardize across teams. In practice, governance often helps organizations scale AI safely, rather than slowing them down.

What to watch

• Governance requirements influencing tool selection at an earlier stage

• Preference for platforms that make documentation and traceability part of everyday work

4. Integration with existing systems is where value is created or lost

Most enterprise value sits inside internal systems that were not built with AI in mind.

Integrating AI into these environments is not only a technical challenge. It is also an organizational one. Knowledge is spread across teams, system behavior is limited by permissions and processes, and responsibility is often unclear. Solutions that support existing teams, rather than replacing them with a small group of specialists, are increasingly preferred.

The ability to turn existing knowledge into safe and understandable AI systems is becoming a key advantage in enterprise AI.

What to watch

• Tools that help internal developers work with AI without major reorganization

• Increased focus on keeping institutional knowledge inside the organization

5. Reliability, cost control, and discipline are becoming decisive

As AI moves into production, organizations pay more attention to practical issues. These include unpredictable costs, system downtime, dependence on external providers, and the need to monitor and evaluate AI behavior over time. Data quality and data origin also become more important.

Once the initial excitement fades, operational discipline becomes the main differentiator. Platforms that support modular design, reuse, and clear system structure help organizations control cost and risk while keeping performance stable.

What to watch

• Stronger requirements for monitoring and evaluation of AI systems

• Competition shifting toward transparency, reliability, and operational control

Why this part of the market matters

Taken together, these trends point to a clear need for a specific type of infrastructure.

When AI systems are expected to carry out tasks, interact with core systems, and operate under real governance rules, organizations need platforms that make workflows clear, responsibilities visible, and system behavior easy to understand for both technical and non-technical teams. At this stage, success depends less on the AI model itself and more on ownership, integration, and long-term operation.

This is the context in which MainlyAI operates. The company focuses on the layer between powerful AI services and the internal systems, processes, and teams that are responsible for how AI behaves in real use.

For us at Katalysen, investments in this area are not about short-term technology trends. They are about where long-term value is created as AI moves from experimentation into core infrastructure. This part of the market is operational and often not very visible. It requires clear ownership rather than abstract innovation, which is why it is often under-served early and only later recognized as strategically important.

In practice, this is where experiments turn into lasting value, and where AI becomes part of everyday infrastructure rather than a series of isolated tests.

This article is the first in a series where we explore market trends around our core investments. The goal is not to promote individual companies, but to help our shareholders and partners better understand why certain parts of the market matter now.

What this perspective assumes

This market view is based on a few assumptions that we believe are reasonable, but not guaranteed:

• Enterprises increasingly prioritize reliability, governance, and transparency as AI systems move into daily operations

• AI systems that can take action continue to move from experiments into focused, high-impact business processes

• Regulation and internal governance increasingly influence how AI tools are selected, especially in Europe