AI is Not a Project – It Is a Strategic Decision
Artificial Intelligence was one of the most dominant topics at Arendalsuka 2025. More than 500 debates covered opportunities and challenges, but what is most critical for success was largely absent from the conversations: the focus on data.
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AI is not magic. In a time when technological development is accelerating, it is easy to get carried away in fear of being left behind. In meetings with companies, I often hear about AI strategies that, at their core, are about adopting new tools. But AI is not something you can just “plug into” your existing business model and simply expect immediate returns from. To truly succeed with AI requires a strategic decision about how organizations handle their data. AI without data is like a fund without capital.
Those who succeed with AI do not necessarily have the most advanced models – but they have control of their data. They have invested in architecture, governance, flow, and security. They have broken down silos, defined ownership, and ensured quality so that data can be used and shared. Ensuring this does not, strictly speaking, start as an IT project – it starts as a strategic leadership responsibility.
Data as a Strategic Resource
The government’s roadmap for technology-driven business also highlights data as the key to value creation. Norway has strong preconditions, but lags far behind our Nordic neighbors in digital value creation and venture capital investments in the ICT sector. Abelia’s transformation barometer shows the same picture: we have the competence, but it is concentrated in specific sectors, and we lack the ability to scale technology broadly.
This gap is not due to lack of potential, but because we too rarely treat data as a strategic resource. If business – and society at large – is to succeed, we must prioritize data on the same level as capital and competence. This requires targeted investments and a new approach to building architectures with data at the center.
What Should Leaders Bring to Their Next Meeting?
The next time the executive team gathers, one question should be on the agenda:
What concrete measures are we taking to ensure that our data can actually be used to create value with AI?
It is not about being the first to adopt new technology. It is about being ready when the technology is mature – and that requires a solid foundation.
Data quality, governance, and flow are not support functions. They are business-critical.
Data first. Always.
