From AI experiment to scalable value
Many organizations are currently experimenting with AI. It often starts small. An employee tries a new tool, builds an agent, or explores how AI can support part of their work. But when does something become a real experiment? And how do you ensure successful experiments eventually create value at scale?
An experiment is typically something that takes place in a smaller setting, where the outcome is still uncertain. A good example comes from an occupational health services provider, where a company doctor started experimenting with AI. The idea was simple: could AI listen to consultations, automatically create summaries, build a case file, and even draft follow-up letters?
Technically, the answer was yes. But that is only the beginning.
Start by defining the value you’re looking for
Many organizations focus on the technology before they define the outcome they want to achieve.
We’ve all been there: trying out a new AI tool, generating images, or testing whether a tool can build a website on its own. It’s interesting, but what exactly have you tested?
An experiment becomes meaningful once it is linked to a business process and a clear objective. Only then can you determine what “good enough” looks like.
Take meeting summaries as an example. A summary used for personal note-taking has very different requirements from a letter that will be sent to an employer or employee. The same AI capability can support multiple use cases, each with its own quality standards.
Saving time is not the same as creating value
Many AI initiatives start with a focus on efficiency: reducing administrative work, eliminating manual tasks, and saving time.
But what happens with the time that is saved?
That is often a far more interesting discussion than the time savings themselves. A CFO may immediately think about cost reduction or additional revenue. Other professionals may see an opportunity to spend more time with customers, clients, or patients.
The reality is often more nuanced. Suppose a professional saves three minutes of administration during a thirty-minute consultation. Can those three minutes actually be utilized? Does it create room for an additional appointment? Or does it primarily improve the quality of the interaction?
These are questions that many organizations cannot fully answer when they begin their first AI experiments.
Experimentation requires an initial investment
One common misconception is that AI should deliver immediate returns.
In practice, experimentation often costs extra time at first. Employees need to learn how to use new tools, adapt their ways of working, and discover which applications provide real value. As a result, the initial investment can feel larger than the initial benefit.
That is perfectly normal. Every new technology comes with a learning curve. The time invested upfront should eventually pay off as organizations gain experience and identify the most valuable use cases.
Moving from one experiment to one hundred
As organizations launch more AI initiatives, a new challenge emerges: how do you scale the successful ones?
Technology plays a critical role here. Something that works on one employee’s laptop does not automatically work across the entire organization. That is why organizations should think early about the AI platform and infrastructure required to support broader adoption.
This is often where organizations get stuck. There is no shortage of ideas, but the foundation needed to deploy them at scale is frequently missing.
Once dozens or even hundreds of experiments are underway, organizations must make choices. Which initiatives deliver the most value? Which ones align with strategic priorities? Which should become part of the wider portfolio?
In many cases, a distinction emerges between solutions that employees can build and use themselves, and solutions that require central development and governance because they support broader organizational processes.
Not everything needs AI
The focus of AI is continuously evolving. The conversation started with AI-generated text, then shifted towards AI-generated video, and now increasingly towards AI agents.
At the same time, many people are starting to experience a certain degree of AI fatigue. Think about the flood of LinkedIn messages and automated sales emails that all sound remarkably similar. Once everyone uses the same tools, differentiation becomes more difficult.
This raises a broader question: do we really want AI everywhere?
A useful comparison is artificial grass. Artificial grass is practical, requires little maintenance, and works well in certain environments. But placing it in front of an old farmhouse in the middle of the countryside can feel strangely out of place.
The same applies to AI. There are plenty of processes where the human element is relatively unimportant and where AI can create significant value. At the same time, there are many situations, both professionally and personally, where human interaction remains essential.
The human factor continues to matter
Consulting is also changing as AI becomes more capable. Creating documents, analyses, and presentations is becoming increasingly supported by AI tools.
Yet the real value of consulting rarely lies in the document itself. It comes from creating alignment, guiding organizational change, and helping solutions take root within a business.
As AI becomes better at generating content, these human capabilities may become even more important.
Keep experimenting, but do it with purpose
Organizations should absolutely continue experimenting with AI. Experimentation is how people discover new possibilities and learn where technology can create value.
The organizations that ultimately gain the most from AI are not necessarily those running the highest number of experiments. They are the organizations that understand the value they are trying to achieve, make deliberate choices about what to scale, and remain critical about where AI truly adds something meaningful.
Because in the end, success is not about AI itself. It is about what you achieve with it.
