What Norwegian companies reveal when asked where AI can create real value

Norwegian companies are no longer questioning the relevance of AI. They are asking where it can create measurable value, and what it will take to turn experimentation into scalable business impact.

This article draws on observations from Capgemini’s AI Accelerator: a series of workshops conducted with more than 12 Norwegian companies across banking, manufacturing, maritime/offshore, industrial services and supply chain technology during 2025-2026. The findings generated from more than 500 AI use cases represent recurring workshop observations, not a statistically representative survey.

One pattern stands out: Norwegian companies are not short on ideas. Use case lists grow quickly. The harder conversation begins when leaders must decide which ideas are worth pursuing, who should own them, what data and governance are required, and how the business must adapt for AI to create lasting value.

Key finding 1: AI opportunities are practical and surprisingly similar

Although the companies operate in different sectors, many share similar underlying needs. AI value often begins where organizational friction is greatest. The strongest use cases are typically not futuristic or disruptive, but practical responses to familiar productivity barriers: finding the right document, reusing previous work, interpreting requirements, compiling status reports, preparing meeting follow-up or waiting for specialist input. This suggests that the first scalable wave of AI value for most companies will come not from the most spectacular use cases, but from solving recurring, high-volume problems employees already recognize.

The most valuable ideas are not always the most advanced. Many strong candidates are practical and familiar: search, summarization, reporting, document control, contract review, meeting follow-up and internal support.

Key finding 2: Many AI ideas reveal a more fundamental business problem

Across the workshops, promising “AI opportunities” often pointed to deeper challenges in data quality, process ownership, information architecture and governance. In many cases, these issues may need to be addressed through simplification, clearer ownership, architectural improvements or conventional automation before AI is introduced.

A recurring insight is that AI opportunities are rarely purely technical. They often sit at the intersection of business processes, data availability, organizational responsibility, risk management and user adoption. When teams discuss AI use cases, they frequently uncover bottlenecks such as fragmented data, manual handovers, inconsistent documentation, limited process visibility and unclear accountability.

For leaders, this means AI strategy should start with a clear understanding of where time, knowledge, quality and decision-making are constrained today, to identify where AI can improve the flow of work.

Key finding 3: The real divide is between experimentation and execution

For the companies in our workshops, competitiveness is not only about new AI-powered products. It is about faster access to knowledge, shorter reporting and documentation cycles, stronger project and cost control, fewer manual errors, better compliance readiness, more effective use of scarce expertise, improved customer and employee experiences, and faster learning across teams.

As AI becomes more accessible, advantage will not come from access to the technology alone. It will come from applying it faster, safer and more effectively than competitors with clear ownership, trusted data, responsible governance and adoption designed in from the start.

But many organizations are now entering a more demanding phase. They are no longer asking only: “What can AI do?”

They are asking: “What will create measurable value?”, “What can we implement safely?” and “What needs to change in our data, processes, governance and ways of working?”

These are the questions that separate AI experimentation from AI-enabled competitiveness.

Structured activities (such as AI Accelerator) can provide a practical foundation for this transition, by bringing together leadership, business functions, technical resources and operational teams to prioritize AI opportunities and connect business needs with feasible, scalable solutions. The aim is not AI for its own sake, but a practical path from business need to feasible solution and scalable value.

AI maturity is rarely a straight line

One of the clearest workshop observations is that AI maturity varies not only between companies, but within them. Enthusiastic individuals may already be experimenting, while colleagues in the same organization have yet to incorporate AI into their work. At the same time, governance, training and approved ways of working may not have caught up. Giving employees access to an AI tool is therefore only a starting point. Companies also need shared competence, clear expectations, safe practices and leadership that connects adoption to business priorities.

Five questions every leadership team should ask before scaling AI

Before scaling AI, leadership teams should align on a few practical questions that test whether ideas are connected to real business value, feasible implementation and responsible adoption.

  1. Which AI opportunities are most clearly connected to business challenges, goals and priorities?
  2. Which use cases combine high value, acceptable risk and realistic feasibility?
  3. Do we have the necessary data, systems and process foundations?
  4. Who owns the business change and value realization, not just the technology?
  5. How will leadership build competence, adoption and measurable results across the organization?

These questions help shift the conversation from inspiration to implementation, ensuring that investments in AI are directed toward opportunities with the strongest foundation for scalability.

From pilots to industrialized AI value

Many Norwegian companies are not short on AI enthusiasm. The challenge now is stronger execution.

Competitive advantage will come from choosing the right problems, building the foundations to solve them, and embedding AI into how work is done. Companies that make this shift can move beyond isolated pilots and turn AI into a trusted, measurable part of operations.

The next phase of AI will not be defined by the number of ideas generated, but by the ability to turn the right ones into scalable business value.

This is the conversation Capgemini will continue at Techpoint 16-17 September, where our leading experts in AI will be exploring how AI can strengthen competitiveness, productivity and innovation.