Why AI-scaling feels hard and what to do about it.

It seems as if the tech sector has been the best AI adopter so far. Other industries, and especially smaller enterprises, seem to find it an uphill battle for different reasons. Internal resistance to adoption not being the least of company bosses’ headaches. 

At Davos, at the World Economic Forum, some of the companies at the forefront of AI adoption in early 2026 discussed how they are scaling AI beyond pilots—because everybody wants to know more about scaling AI.

It is clear and worth repeating, that to use AI successfully in your business, you need 

• good structured data,
• AI literate staff, 
• cyber defense strategies, 
• leaders who lead by example by using it themselves. 

Roy Jakobs, CEO of Royal Philips, during the World Economic Forum  said:

“Data and technology are important, of course, but a good rule of thumb is to spend at least as much time thinking about adoption as tech development.” —In other words, considering how AI will be used in practice by people throughout the organisation. 

He went on to say “Adoption is ultimately where success is measured. You need to design that from the get-go. And that is much less about technology, much more about understanding the practice that it will actually serve.”

On the issue of self-regulation he emphasised its importance: 

“Especially since regulators also struggle to keep up with the speed of technology development. We need to have our own rules – how we test, how we validate, what level of rigour we apply, what kind of practices, but also biases that we take into account.”

During another session, Meta's Chief Global Affairs Officer, Joel Kaplan, also stressed:

 “It's important to think deeply about how AI is going to change the workflows of the organisation and also the nature of its work. Every organisation is going to have to think about this over the next couple of years,” he added. “And the ones that [will] succeed are the ones who start thinking now.”

Advice curated from people in the field of AI-adoption:

• Adoption in the workforce will not happen automatically just because you have the tools and applications. 
• It is not a magic wand that can fix all problems by itself. 
• It is not a one size fits all solution. The best practice for everybody in the organisation is to play around with it to see how it will work for you or not.
• Your staff needs to understand that they will have to change the way they used to do the job.
• Focusing on AI uses and users will create feedback and show the tangible benefits of AI.
• The expertise and judgments of frontline workers and experts is the kind of information often left as unstructured data, losing valuable insights.
• You need input from staff, but equally staff need support and training from their leaders. 
• Transparency and human accountability are also important principles.
• Processes that were once dependent on an employee’s intuition become repeatable and teachable.
• The people in your workforce can focus on nuanced issues that require empathy and complex reasoning.
• Companies that have invested in data very early, are surging ahead in how they're using it.
• We believe that, at this stage, AI can advise, but a human is still in command.
• The human role should be to provide judgment, oversight, creativity and spot exceptions, while AI can handle execution, optimisation, prediction and pattern synthesis. This shift is already starting in some organisations. 

One final word:

Steve Norman, MBA Corporate Leadership Expert, Management Consultant, and Leadership Coach said in an article: “We need massive retraining programs, social safety nets for displaced workers, and education systems that prepare people for human-robot collaboration. Most importantly, we need leaders who understand that technology should serve humanity, not replace it entirely.” 

To summarise: A few key words in successful AI adoption: 

• clear data, 
• clear benchmarks,
• employee buy-in, 
• leadership support and example, 
• keep the human in the loop.