WKO Future Journey London: What Companies Need to Understand About Agentic AI Now

Agentic AI is currently one of the most exciting developments in the AI landscape. That is exactly why we took part in the WKO Future Journey to London. The focus was on current trends, concrete application areas, and the question of how AI can be used productively in companies. The goal of the trip was not just to talk about new models and tools, but to understand how AI is already being integrated into real processes, data flows, and systems today.

Together with a delegation from the Austrian Federal Economic Chamber and representatives from companies such as AVL, TTTech, Gebrüder Weiss, PwC, and ICMPD, one thing quickly became clear: the key question is no longer just which model is being used, but whether AI is embedded into processes in a way that creates real outcomes.

One observation ran through many conversations, sessions, and company visits: the focus is shifting.

Models → Agents → Outcomes

Models remain the foundation. Tools are becoming more accessible and, in many areas, more interchangeable. What matters are the outcomes. This is where agentic AI becomes especially compelling: not at the level of a single function, but in its ability to coordinate multiple steps toward a goal and be embedded into operational workflows.

For companies, this also changes the benchmark for evaluation. The question is no longer just whether a model delivers good answers. The more important question is whether an AI system can meaningfully support existing processes, prepare decisions, or reliably execute operational steps.

Systems instead of tools

A particularly clear pattern from London was this: the real value is not created by the individual tool, but by the system behind it.

A functioning agent does not consist of just one model. It requires multiple components, clearly defined roles, orchestrated workflows, and above all, deep integration into the existing IT and process landscape. That is exactly why testing new AI tools or piloting individual functions is not enough. Productive value only emerges where systems work together.

This also shifts where effort is required in projects. The challenge is not in choosing a model or writing a prompt. The greater leverage lies in,

• Understand processes clearly
• Make data usable
• Build interfaces
• Connect systems with one another

So most of the work happens outside the model.

Why standard solutions reach their limits

Standard solutions can be a sensible starting point. They help map simple tasks, implement initial automations, and make potential visible.

Their limits become apparent quickly, however, wherever multiple systems are involved, decisions depend on context, or processes are not standardized. The closer a company gets to real value creation and operational relevance, the less generic approaches alone are sufficient.

Standard solutions can therefore be a starting point, but they are rarely the target architecture. And that is exactly where the real value of agentic AI begins: in its ability to effectively support complex, company-specific workflows.

Why relevant use cases are almost never generic

Another recurring impression was that the most valuable use cases are almost never generic. On an abstract level, topics such as customer service, sales, IT support, or document processing may seem similar. In practice, however, they differ significantly.

This is due to:

• specific processes
• available data
• internal rules and logic
• evolved system landscapes

Precisely because agentic AI works toward specific goals, this difference becomes immediately relevant. Truly valuable use cases are therefore almost always individual.

In the UK, people think in terms of production

What stood out in London was how consistently AI is being approached with productive application in mind. AI is not only discussed there, but implemented at high speed. AI-first startups emerge quickly, new business models reach the market in a short time, and existing industries are being actively challenged.

AI is being used deliberately to:

• rethink processes
• reshape markets
• build competitive advantages

That is exactly what makes the current development so relevant. Anyone who sees agentic AI merely as a new tool underestimates its strategic impact.

Governance is part of the solution

By contrast, a different dynamic often emerges in German-speaking markets. Regulation is quickly used as an argument for restraint. On site, however, one thing became clear: implementation is happening even in regulated environments.

Governance is not the blocker here, but part of the solution.

The real question is less: Are we allowed to do this? And more: How do we implement it in a sensible way?

This perspective matters. It is not the mere use of AI that becomes a competitive factor, but the ability to integrate it into real business processes in a controlled, meaningful, and sufficiently fast way.

What companies should do now

The most effective entry point into agentic AI does not begin with the tool, but with the process. Particularly relevant are workflows where friction occurs today, for example:

• recurring processes with clear patterns
• media discontinuities between applications
• manual coordination between teams or systems
• decisions based on data from multiple sources

It is in precisely these kinds of environments that agentic workflows deliver practical value, because they do not just provide information, but can also structure operational steps toward a clear goal.

A sensible entry into agentic AI usually follows this sequence:

1. Identify processes with friction losses
2. Check where data from multiple sources, manual handovers, or recurring decisions play a role
3. Analyze relevant data, systems, rules, and approvals
4. Assess where integration creates the greatest operational value
5. Only then decide on agent architecture, platform, and model

This sequence is strategically important: those who start with the process build robust use cases. Those who start with the tool often produce nothing more than better demos.

Agentic AI is not a product, but an integrated system

Perhaps the most important takeaway from London is this: the real value of agentic AI does not come from AI alone, but from its integration into real processes, data, and systems.

The most exciting development is not just that systems can generate content, but that they are increasingly able to understand, prepare decisions, and act along processes. That is exactly why it is not enough to view agentic AI as a new product or tool. It only works sustainably where integration, governance, and process understanding come together.

And perhaps even more importantly: the greatest risk for companies right now is not getting something wrong, but waiting too long.