Data Science & AI; the new electricity. But how do I start?

Think of AI as the 21st century equivalent of introducing electricity into business processes. After all, who wants to work in the dark? AI as a fuel for new business models. Equivalent to electricity, AI really has dozens of possible applications.

When properly implemented, AI delivers real business benefits: Microsoft found that organizations already on the AI ​​journey outperform organizations that are not, with 5% on productivity, performance and business results. According to Gartner, by 2021, 15% of all customer service interactions worldwide will be fully handled by AI, an increase of 400% from 2017.

Part of the problem is that companies have a hard time understanding how to apply this transformational technology to their own organization in a way that delivers meaningful impact. With the demand for AI increasing, the discussion is no longer “why should I implement?”, but “how do I start?”.

AI has the ability to influence every part of the business – from production and supply chain to user experience – and fundamentally disrupt operations. In addition, there is no one-size-fits-all approach to AI.

AI solutions are tailor-made to solve specific problems within an organization. This means that management must be actively involved in identifying those problems – and in understanding the possibilities and limitations of AI in addressing them. In short, when it comes to embracing AI, management must have a personal interest. Leadership is needed to convince people to value the importance of data and AI and its potential to generate value in the company.

Ultimately, this means rethinking traditional corporate structures, operational models, role definitions, individual success measures and career advancement. Short-term successful AI solutions depend on asking the right questions about the data, so encourage teams to ask questions, challenge them and get actively involved in the introduction of data and AI into the business.

A good starting point is to identify the value proposition of the company and to ask: what kind of business are we in? What makes us successful? Is it R&D? Marketing? Customer service? Operational excellence?

The next step is to gain insight into the possibilities of AI and to align it with the value proposition of the company: for example, AI can help with forecasts (such as predicting budget overruns or periods during which the staff will be sick). In sectors such as retail, project management or healthcare, where the value proposition has a strong customer focus, AI can help visualize and improve planning and optimize resources in different scenarios.

Where a company’s value proposition is based on innovation and R&D, AI-enabled search engines can quickly reveal the competencies, knowledge base and ideas of the entire workforce and then use them for relevant data. This helps build teams with extensive know-how equipped with the depth of knowledge and data to generate new insights and achieve greater impact in a shorter period of time.

The disruptive, silo-devastating impact of AI means that when it comes to AI experiments, management must assemble multidisciplinary teams that can pool their respective areas of expertise to maximize the impact and success of any AI project. The exact combination of skills these teams need depends on the business problem or the range of problems the company is trying to solve with AI. The team likely needs data scientists, data engineers, designers, and a project manager (i.e. the person who “owns” the business case). If the problem is very domain specific, a domain specialist is needed.


In summary, the following approach is recommended:

  1. Make yourself personally aware of the potential and impact
  2. Be open to a mind shift (think differently)
  3. Take a problem or question that matters within the current market context. Finding problems worth solving is at the heart of successful innovation as it shifts focus from theory to practice and from talking to doing.
  4. Assemble multi-functional team to address problem
  5. Test current state of AI, build a data lake / data warehouse and start experimenting
  6. Start small, think big and start without delay
  7. Persist in applicable solutions
  8. I believe that Data Science & AI is in every heart of a successful business model, ergo is the basis of a successful business model. Data science & AI are going to make the “competitive” difference. The D of Digitization of Data is standard in the marketing mix of Porter and is the 5th P!

Are you also looking for the right competences to help your organization with data and to develop data driven insights and services? Then look at IXT, People multiplying your Business; or mail to