It is not getting any easier to make the right, forward-looking decisions for your own company, in the face of AI quantum leaps, the need for digitalisation and increasing global complexity. If a company's business strategy suddenly changes, this inevitably has an impact on its digital strategy. Finding the right digital path under these rapidly changing conditions is a challenge for any company and a headache for many IT decision makers. A Data & AI Maturity Model can offer decision makers a flexible tool to go through the transformation process towards a data-driven company.
How the Data & AI Maturity Model systematically supports this process
The Data & AI Maturity Model is designed to meet the needs of organisations to plan and monitor success in the process of data and AI transformation. This is because getting started using data and AI in an organisation is difficult. According to a 2021 Harvard Business Review survey, 99% of Fortune 1000 companies are investing in data and AI capabilities. Yet only 29.2% of them said they are achieving transformative business results and only 24% say their company is data-driven (Source: Harvard Business Review (2021)). Why is digitisation proving so difficult for organisations?
Why digitisation efforts often fail and what it takes to succeed
One reason is that successfully harnessing the potential of data requires new organisational capabilities.
In order for an organisation to become data-driven, specific conditions must be met. The human factor is often underestimated. Decision-makers in companies tend to focus purely on the technical side of AI and data. However, both components are needed: Technology and human skills.
In addition to the development of use cases aligned with the data strategy, the business vision, human skills, an appropriate leadership strategy and an examination of ethical issues are the most important. Many data and AI transformations fail because of a lack of strategic vision, leadership support and a strong focus on generating value in the context of digitalisation.
It is essential in this context to first internalise that there is no artificial intelligence without data. This means that AI applications cannot work without an underlying data structure. Every journey towards a data-driven organisation starts with an analysis of the current state. It must be clearly defined where the company is currently on the data/AI journey and what it needs to achieve the set goal.
Four essential steps on the digitisation journey:
- Discovery phase: the company becomes aware of the potential of data and AI through ad hoc actions, while management recognises the need for a more systematic approach and targeted investment in developing the relevant capabilities.
- Build-up phase: the data strategy is more clearly articulated. The organisation has a better understanding and knows where it wants to go. At this stage, investments are focused on prioritised initiatives, but the organisation is not yet generating value from data on a larger scale.
- Development: seeing the first data use cases being realised in practice and making money: The first evidence that efforts are starting to pay off. This can lead to further initiatives being taken to make the company data-driven.
- Leading companies have already built capacities that enable them to develop and deploy data use cases continuously and reliably. They are able to track progress and value, as well as monitor and improve algorithms. When needed, they invest in all relevant elements of data and AI transformation. Data has become a competitive advantage for these companies.
It remains to be said that, in addition to the necessary technology, new organisational skills are needed at management level and among employees in order to successfully master the data and AI transformation and to generate a competitive advantage from it.
Don't wanna miss more insights and updates about the AI landscape? Subscribe for our LinkedIn Newsletter!