In short
- Building an AI strategy does not start with the tool, but with three foundations: usable data, a clear process and a team that understands why it is using AI.
- There is no universal AI strategy: for an SME, it is about one concrete quick win; for a larger company, it is about a broader framework with ownership and governance.
- AI agents are not a threat to jobs. They take over repetitive work so people can focus on strategy, relationships and creativity.
AI is everywhere. In meetings, at events, in almost every tool you open today. Everyone keeps repeating the same message: "you have to do something with it." But the question companies rarely ask is the most important one: why should I do something with AI?
Using AI because you feel you have to rarely delivers much value. Using AI because it improves your processes, your people and your customer experience? That is a very different story. The difference between the two? An AI strategy.
An AI strategy answers three questions: where will you use AI? Why there? And what do you need to make it work? Not a standalone chatbot pilot, not "we use Copilot now too". But a deliberate choice about which problem you are solving, for whom, and on what foundation.
Without that strategy, you end up with AI applications that exist next to each other instead of reinforcing one another. With a strategy, AI becomes a lever rather than a loose experiment.
Companies often say they are "ready for AI." Look under the hood and you often see something else. Data is scattered across systems, desktops and servers. Processes mostly live in employees' heads. And teams know they want to do something with AI, but not what or why.
That is where the real obstacles are. AI only creates value when the foundations are in order, and those foundations rest on three pillars.
1. Data: available, centralised and usable
AI can only work with what is available. Step one: make sure your data is accessible to AI tools. Step two: centralise it, so maintenance and monitoring do not become chaotic. Step three: make someone accountable. Data must be complete, correct and up to date. Always, not occasionally.
2. Process: AI only makes sense when the process is clear
Anyone can come up with repetitive work that AI could take over. But even repetitive work starts with an input, generates an output and connects to other tasks in your organisation. So do not build blindly. Look at the user who provides the input and processes the output. Only then decide where AI can have the greatest impact.
The question is never "how do I use the fanciest AI?" The question is: how do we make the process simpler, faster and more pleasant for the user? Sometimes the best solution is no AI at all. That is also a good answer.
3. People: teams that know where they want to go
You do not introduce AI all at once. You start small, with teams that are interested and have a clear benefit. Those quick wins build trust. But in parallel, you need to think about the bigger direction: do you want to use AI to increase internal efficiency? As a competitive advantage? Or do you want to integrate AI into your customer offering?
That strategic framework determines the role AI will play: tool, assistant, colleague, or a complete AI team.
As an SME, you do not have the budget or time for a broad transformation programme — and you do not need one either. An AI strategy for a small business starts with one concrete question: which recurring task currently costs my team the most time while adding the least value?
Choose one process (invoice follow-up, sorting customer questions, preparing quotations, ...) and test AI in that one place. Make sure the data for that process is in order, assign someone to review the output, and measure whether it actually saves time. Only once that first quick win works do you expand further. So an AI strategy for an SME is not a twenty-page document, but growing proof that AI solves your specific bottlenecks.
For larger organisations, it is different: there, an AI strategy quickly becomes about scalability, integration with existing systems and change management, with the IT lead or marketing manager driving the initiative.
The AI hype started with simple features such as summarising and generating. Then came AI agents: tasks carried out as if by a colleague. Now we are going one step further: teams of AI agents working together and managed by one employee. Four levels, four different expectations:
Each level requires a different kind of oversight. The further you move towards digital colleagues, the more important the following questions become.
A digital colleague raises new questions. Who is allowed to build or configure such an AI agent? Who "hires" it for a team? Who reviews the output? And what permissions does it get within your systems?
Organisations therefore need to think not only about where AI should be deployed, but also about how people and AI will work together. Training, guidance and clear agreements are not side issues, they are what make an AI strategy sustainable once the first pilot is over.
This is exactly where a strong digital workplace makes the difference: infrastructure that gives AI agents secure access to the right data without sacrificing control or security.
AI agents are not a threat to jobs. They take away repetitive, time-consuming work so employees can focus on the areas where they make the difference: strategy, relationships, creativity and empathy.
Empathy, nuance, creativity and logic are still areas where people excel. But those who use AI intelligently work faster, more efficiently and more consistently than those who do not. AI is not a threat. It is a lever.
AI will not unleash a Hollywood-style revolution over the next few years. No robots taking over your job. Instead, organisations will gradually work smarter and more smoothly. Teams will be freed from repetitive work. Companies will finally gain insights they previously had to guess at.
That does not start with the coolest tool, but with the question: where can AI improve something today for both our people and our customers? Start there, and AI stops being hype. It becomes something that lasts.
Want to know where AI could already make an impact in your organisation today — on your digital workplace, customer operations or processes? Let us know.
An AI strategy is a deliberate choice about where you use AI, why you use it there, and what you need to make it work. It is based on your data, your processes and your team. Not just another tool, but a plan that connects AI to your business goals.
Start with one recurring task that takes a lot of time for little return. Make sure the data for that task is in order, test AI on that process and measure the result. Only expand your AI approach once it genuinely works for that first task.
An AI tool performs one clearly defined task on request. An AI assistant supports an employee throughout a task, with context. An AI agent carries out a complete process independently, from input to output, without someone having to direct every step.
A digital colleague is a team of AI agents that works together and is managed by one employee, much like that employee would manage a human team. It is the next step beyond standalone AI agents.
No. AI agents take over repetitive and time-consuming work, not the work where people make the difference: strategy, relationships, creativity and empathy. People who use AI intelligently work faster and more consistently. The job may change somewhat, but it does not disappear.
You define that in advance, not afterwards. Determine who is allowed to build or configure an AI agent, who reviews its output and what permissions the agent gets within your systems. Without those agreements, you run risks when it comes to quality, data and security.
Start with the foundations. Not with the tool. Is your data available and centralised? Is your process clear enough to automate? Does your team understand why you are using AI? If those foundations are right, you can start small and scale quickly.
