For centuries, the idea of replicating human intelligence belonged to the realm of science fiction. Today, it’s reality. Artificial intelligence (AI) is advancing at breakneck speed. While some of the core scientific concepts have been around for nearly 70 years, only recent breakthroughs in computing power, fuelled by American innovation and the mass production of advanced chips in Asia, have made it possible. Thanks to cloud technology, that power is now virtually unlimited and within reach for everyone.
The unprecedented pace at which AI has developed has taken many by surprise. Citizens, scientists and businesses alike have seen a flood of AI applications emerge in just a few short years. Those able to harness these capabilities are already reaping productivity gains, cost savings and a strong competitive edge.
Crucially, companies don’t need to be Silicon Valley-style innovators to benefit from AI. There is a common perception that AI success is reserved for American or Asian firms, but that is not true. The high standards upheld by European companies – their careful, efficient and high-quality approach to production – could position Europe as an ideal frontrunner in AI adoption, boosting productivity and building a significant competitive advantage over other regions.
How can AI value be realised?
Yet many European organisations still struggle with the same core question: how can the value promised by AI be realised? One thing is clear: releasing AI into a business environment without proper control is not a recipe for success. Without human oversight, undesirable outcomes emerge – using personal data introduces new risks that must be carefully managed. Regulation is also constantly evolving and the very nature of the knowledge economy is shifting – these considerations have to be carefully managed. Tasks that were once impossible are now handled effortlessly by AI, while tasks still beyond AI’s reach demand fresh insights and unprecedented forms of smart collaboration between humans and machines.
Two ways organisations tend to use AI
In practice, there are two main ways organisations tend to deploy AI. The most accessible is as a personal assistant. This includes writing emails, summarising documents, or answering questions. Products like Microsoft Copilot, Google Gemini, and ChatGPT can be quickly installed and provide immediate support to employees. This helps staff build AI skills quickly and work more efficiently. However, the large-scale productivity leap often promised by AI rarely materialises in this scenario – these tools enhance individual performance but seldom transform entire business processes and meet the whole AI-promise. Far from that. That said, the cost-benefit ratio is still often highly positive, particularly for Western European knowledge-based companies paying salaries far above the global average.
The second, more ambitious approach is to place AI at the heart of the business model to fundamentally redesign processes. This requires a larger, transformative vision to fully unlock AI’s potential for significant value creation. Many organisations begin this journey but fail to follow through. They might start (or have already started) with an AI roadmap and target the so-called ‘low-hanging fruit’: small proof-of-concepts that deliver measurable results and pay back the initial investment. However, because these projects typically improve only a fragment of complex process chains, the overall impact remains limited. Challenges such as data quality, risk management and compliance are often identified during these pilots and are then treated as obstacles. Without addressing them, proof-of-concepts cannot be scaled into production. The result: enthusiasm wanes, organisations retreat from large-scale rollout, and the hoped-for (and ultimately potential) value remains untapped. Rationally, this is understandable. But equally – and most would agree – those who invest only minimally and lack realism can expect only minimal returns.
Another common concern is that current AI use still largely revolves around ‘prompting’ – formulating commands for an AI model and maintaining an interactive dialogue with it. In large-scale business processes, this is neither scalable nor efficient. This is where Agentic AI comes in: AI that can generate the right prompts itself, orchestrating task-executing agents (pre-programmed modules) capable of carrying out entire digital processes under human supervision. Where Agentic AI was, until recently, an unfamiliar concept, it is now gaining attention as a key enabler of large-scale process optimisation with AI.
Successfully integrating AI requires more than installing off-the-shelf tools or running a few proof-of-concepts. In fact, it needs a new operating model. It demands vision, strategic decision-making and the willingness to fundamentally transform processes. Organisations that can weave AI into their core, from strategy through daily operations, will create a lasting competitive advantage. The shift from shock to strategy is not easy, but those who start now will set the pace for the future.
Five essential tips to move from AI shock to AI strategy:
- Identify high-potential processes: organisations with complex operations and many digital handovers are prime candidates for significant productivity gains with AI. An intrinsic drive to achieve substantial efficiency improvements is key.
- Set clear goals and requirements: without ambitious yet realistic targets, breakthrough results are unlikely. Support these goals with strong evidence, a clear plan, and a critical eye on the ‘how,’ the ‘when’ and the ‘with what’. Don’t accept a quick ‘No, it can’t be done.’
- Be realistic about the journey: significant transformation will require multi-year investment. Returns can be both rapid and proven if the entire organisation embraces AI adoption. This includes bringing employees along with you, so they understand and trust the mission. Effective communication and expectation management is paramount.
- Raise AI literacy across all levels: employees, managers and executives don’t need to become AI specialists, but they must understand how AI will change their work. This takes time – you can’t go from 0 to 100 in a few weeks. But you can in 12–18 months.
- Build the right human expertise and capacity: assemble teams with the ability to execute transformation, combining deep domain knowledge, operational expertise, technical skills, and change management capabilities. Experience is critical.
The message is simple: AI will not transform your organisation by accident, nor will it do it overnight. And a piecemeal approach involving small, pilot projects – although useful – won’t get you to where you want to be either. But with clarity, commitment, and the right capabilities, the move from AI shock to AI strategy can help you achieve your organisational goals around efficiency and productivity and also define your competitive position for years to come.
Want to know more?
If you want to know how Valcon can help your organisation shape its AI strategy, please reach out to Koko Visser at [email protected].
