The data & AI journey typically consists of different phases including:

- Exploration. In this phase, you will explore a few quick & dirty (business) opportunities to assess whether you want to embark on the journey. This phase usually takes from several months up to a year and requires little investment. Once you are convinced that there is value, you will move to the next phase.

- Transformation. In this phase, you start to organize yourself to become more data-driven. You will perform an analysis to find the most interesting use cases (applications) to start with, considering value as well as feasibility. You need to break silos (technological, departmental, vendors) and collect data across the enterprise into a coordinated platform. You will need to hire a CDO and set up a data team, closely collaborating with IT and HR. You will need to work on a ‘single version of the truth’ so that the whole enterprise understands data in the same way without ambiguity. This phase will require significant investment and may take between three and five years. Be prepared to have some patience as an organization.

- Data-driven. In this phase, you will start to enjoy the results of your endurance in the first years. You will be able to use data in a consistent way to inform the big decisions you have to make related to your core business. Moreover, new data-driven products and services will see the light. Depending on your sector, you might be able to externally monetize your data and insights to other sectors in a B2B business model.

- AI-empowered. In this phase, you will use Machine Learning and other AI technologies to scale the value of data throughout your organization. Given the massive scale of use, mastering data privacy and AI ethics become essential to create and maintain trust with your stakeholders.

In each of those phases, you will have to make many decisions that will determine how fast or slow you will progress on your journey. Those decisions are different in nature and relate to various aspects of the organization such as business & finance, technology, people, and responsibility.

- On the organisational side, you have to think about where to place the Chief Data Officer, how to measure data maturity and what will the relation be between the data and the IT department.

- On the business and finance side, you need to make decisions about how to select the best use cases, how to measure economic impact, and how to finance the whole data journey, which can take years. There is no single right answer for the question of what business function to start with. In the beginning, it is important to choose a business function that matters for core business but that is also feasible from a practical perspective. Otherwise, it will take too long to provide the first results. Having said this, many organisations start with marketing.

- On the technology side, important decisions include whether you want to work in the cloud or on-premise, whether you need a unified data model, and you need to define a data collection strategy including planning and budgeting.

- On the people side, it is important to create a team with the right skills and expertise. Many organisations choose a mix between hiring expert personnel and training existing personnel. Sometimes they outsource the first initiatives to a third party to kickstart the activity with the objective to later internalise the knowledge. Other decisions related to people include how to democratise all the data initiatives, how to win over skeptics, and how to make people enthusiastic about data through appropriate communication.

- Finally, on the responsibility side, you need to understand the social and ethical challenges of AI and Big Data; to define AI principles and implement them in your organisation; and work out how you can use data as a force for good, to improve society and fight its challenges.