Beyond the hype: a roadmap to your AI solution

AI will completely transform your business!

Isn’t anyone tired of this headline? We have seen these exclamations for a long time, but where are the examples? Where are the success stories? 

Every decade it seems there is a new technology that will change the world, but it only sometimes leads to disappointment when adopting it becomes too challenging.

Many believe the AI industry is like a modern tulip mania, an overhyped technology that leans too much on theoretical output and unrealistic expectations.

And sadly, it’s a self-fulfilling prophecy; many companies jump on the AI bandwagon with buzzwords and marketing bluffs to overpromise their product or service, destroying the trust in what they ought to sell.

Luckily, these rotten apples are quickly disappearing. 

We have now reached a tipping point with AI. The compute capacity, our connected devices, and our wealth of data can finally create unique competitive advantages by better serving customers, improving processes, enhancing the employee experience, or reducing costs. 

The reasons why AI projects fail or don’t go beyond a proof-of-concept phase are because of some stubborn misconceptions. Mostly it all comes back to these:

  • You need a lot of data to use AI effectively.
  • It is expensive.
  • It will disrupt jobs.


  1. You don’t need a massive volume of data to create an impact with AI.
  2. Modelling an AI model is relatively inexpensive if you know the technical boundaries of AI.
  3. AI is here to help, not to replace. It is a technological tool to assist workers in working more efficiently and faster. But more important, it will also improve the operator’s or employees’ activation and working conditions by eliminating repetitive tasks.
  4. Lastly, AI is not another plug-and-play tool. AI requires a culture shift. Data becomes your most important asset to manage. Using AI means that you need to trust the data and the models. 

It’s about culture, stupid!

However, one of the biggest misconceptions about AI is viewing it as a “plug-and-play technology” that will generate results once deployed. AI cannot be compared with traditional software in which procedures and processes have been hard-coded by humans to be as efficient as possible. AI software will use your data to deliver the predictions and insights that are relevant to you. Therefore it’s much harder to buy a value-generating AI program, but with your data, you should consider creating one.

The disruptive nature of the technology can cause established organisations with rigid structures, strict processes and procedures to struggle to extract all value out of AI systems. Therefore, sustainable AI adoption requires a significant transformation towards more agility. 

Consequently, you will need more than simply investing in AI software tools, data infrastructure, and development skills to incorporate AI into the core activities of your organisation. It must be aligned with the company culture, structure and way of working to achieve its full benefits.

According to McKinsey, “most businesses that aren’t born-digital, traditional mindsets and ways of working run counter to those needed for AI.” Businesses must work towards changing these traditional views to embrace the digital era fully.

For a broad AI adoption, three primary shifts will be crucial:

  • Shifting from expertise & experience-based decision-making by top management to data-based decision-making by first-line employees (shift in responsibilities)
  • Shifting from siloed work to interdisciplinary collaboration. All data is being generated across the organisation but trusted and used by others. Everyone must understand their role in the more extensive play.
  • Given AI models are agile and swift, rigidness and risk-averse ways of working will counter the benefits of the AI model. A shift towards agile, iterative and experimental working methods will be needed.

Explore what is possible with AI and get started

If you are interested in implementing an AI solution into your organisation or business, we wrote an extensive roadmap explaining every step of implementing AI successfully. We will be your sherpa in showing the pitfalls and shortcuts to the top of the mountain.

Let us show a preview of the first and probably the most important step, the idea phase.

Focus on the problem rather than the solution.

The idea phase focuses on the problem and not on the solution. By thoroughly defining the problem and informing yourself, an idea for a possible AI solution is born. Nowadays, instead of the AI technology itself, the idea poses the real challenge: the more powerful the idea, the more effective the AI solution.

In the first step of the idea phase, it is crucial to describe the problem the AI solution should tackle. Wait to be tempted to propose solutions, but define the problem in all its aspects. This is an exercise that is often too easily overlooked. That is why we make it the fundament of our work dynamic.

Why has the problem not been tackled yet?

As the problem is clearly described in step 1 of the idea phase, it is straightforward to look into why the problem has not been tackled before. Maybe a solution exists, but this solution is too expensive. Or perhaps it is simply impossible to solve the problem technically? The problem could be too dynamic, only solvable by an intensive-care solution needing constant monitoring and adaptations. Maybe there are only limited resources to tackle the problem. Identifying these surrounding problems is critical to decide if and how to proceed in the AI solution development process.

How can AI help?

In the last step of the idea phase, criteria for using AI as a solution should be proposed. Perhaps you were inspired by a similar solution elsewhere? Or the problem has a repetitive nature and can easily be automated.

Ask yourself these questions: If you had this perfect AI solution, would it indeed benefit your company? Is AI the way to go? What would you do differently if you had this AI solution?

At the end of this process, you understand the problem and have a realistic image of what AI can offer you. 

Interested to read more? Download our AI roadmap and discover the data, notebook and implementation phase with tips and tricks.



Look at the page below if you are looking for inspiration in some successful AI use cases.

Gilles Deweerdt



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