Artificial intelligence (AI) has become one of the most discussed themes in both financial markets and broader public debate. The pace and scope of development suggest that AI may represent the most significant technological shift affecting business and society since the industrial revolution.

This article highlights three questions that investors and observers should follow closely:

Will AI actually enable a major productivity leap?
What does a new data-driven economy look like?
What will be the role of human labour?

Let’s dive in!

1. Will AI actually deliver a major productivity leap?

AI is often described as the most significant step forward in labour productivity since the industrial revolution. The industrial revolution shifted mechanical work from humans to machines. AI extends this shift to cognitive tasks such as reasoning and decision-making.

Used appropriately, AI can support decision-making that is faster, more accurate and more consistent than what humans can achieve. In data-intensive sectors where competitive advantage depends on decision quality, AI has the potential to increase productivity and strengthen competitiveness to levels that were previously difficult to reach.

Three trends illustrate how AI-driven productivity gains may unfold:

Trend 1:From automation to intelligent decision-making

Automation in business is nothing new. What changes with AI, however, is the scope of decisions that can be automated. Traditional automation systems rely on linear rules and structured data. AI can process unstructured data, such as text and images, and incorporate context and uncertainty into decisions.

This moves AI-supported decisions closer to human judgment, but without human constraints such as limited processing capacity or reaction time.

The implication is that AI is no longer only a cost-reduction tool. It can augment individual employees to the point where one AI-enabled worker may match the productivity of an entire team.

Trend 2:Self-improving systems

A key feature of AI systems is their ability to learn. Traditional software is static and requires manual updates. Many AI models improve automatically as they process new data or receive feedback from users.Each interaction can refine the system, and every new process that uses AI generates additional data. This can create a reinforcing loop where increased use improves the model, and improved performance drives further adoption.

Trend 3:The AI winners and losers

Given the scale of potential productivity benefits, success or failure in harnessing AI may become a major differentiator among companies.

Early indications suggest that firms are separating into three categories:

AI-native: Businesses where AI is central to product value (e.g., OpenAI, Anthropic, Palantir).
AI-assisted: Businesses that integrate AI to support value creation, for example through copilots or analytics.
AI-laggards: Businesses that fail to recognise or effectively capitalize on AI-enabled opportunities.

For investors, this framework can help evaluate strategic positioning. If the pace of AI advancement continues, firms that embed AI close to the core of their value creation may enjoy a structural advantage.

2. A new era for the data economy?

Data has long been important in digital business, but AI increases its strategic relevance and redefines how competitive advantage is built. Three perspectives are particularly relevant.

Data as a strategic resource

As AI models increasingly become sources of competitive advantage, the data used to train them becomes a strategic asset.

In the early stages of machine learning, the volume of data was often considered the dominant factor. As models have improved, data quality factors such as diversity, balance and representativeness have become critical. These help systems understand context, nuance and anomalies. The basis of competitive advantage is also shifting. Whereas access to technology (such as patents) once played a central role, exclusivity in access to data may become equally important. This has led to discussion on the concept of data gravity: organisations with large, rich datasets attract developers and partners, strengthening their position further.

Gatekeepers of the new data economy

A data-driven economy creates opportunities for companies that enable the AI ecosystem or control access to it:

Semiconductor manufacturers provide the physical components required for AI. Nvidia’s rise illustrates how critical this segment is.
Cloud service providers such as AWS, Microsoft and Google operate the infrastructure on which AI runs and influence which firms can scale their models.
Data governance and security providers offer essential capabilities in privacy, compliance and protection. Any firm relying on data needs partners that can support these requirements.

Data network effects

Network effects are familiar in technology. Many of the Mag 7 companies built dominance through user-driven feedback loops. AI introduces a new type of network effect driven by data rather than users. Better data improves model performance, which attracts more users and integrations, generating even more data. Unlike social networks, where new users must join for effects to grow, AI network effects can emerge rapidly. Every interaction, click or sensor input produces data continuously. Firms that manage their data networks effectively may build competitive advantages faster than in previous technology cycles.

The role of synthetic data

As real-world data becomes more valuable, access to it is becoming more constrained due to privacy, regulation and competitive protection. This increases the role of synthetic data—artificially generated data used to train AI models. Synthetic data management may become a meaningful business segment. It offers a way for firms with limited real-world data to develop AI capabilities and participate in network effects. If real data is the “oil” of the data economy, synthetic data may represent renewable energy sources.

3. What will be the role of human labour?

Concerns about AI replacing human decision-making are common. A more realistic outcome is that AI increases the effectiveness of human thinking rather than replaces it. Just as the industrial revolution amplified physical labour, the AI era may amplify cognitive labour.

From automation to cognitive collaboration

Technology has long been used to outsource physical tasks. AI expands this to certain cognitive tasks, allowing humans to focus on areas where human judgment, creativity and understanding of context add value. Examples already in use include:

Copilots like ChatGPT, GitHub Copilot and Microsoft 365 Copilot, which assist with writing, coding, analysis and communication.
Decision-support systems in finance, healthcare and logistics, where AI processes data and outlines scenarios.
Creative tools such as Midjourney and Sora, which convert human ideas into production-ready outputs.

AI accelerates the path from concept to execution, enabling humans to spend more time on higher-order thinking.

A new generation of knowledge workers

As AI becomes a core work tool, the requirements for knowledge work evolve. Human responsibility shifts from performing tasks to overseeing and coordinating AI-driven workflows. Examples include:

Knowledge workers directing multiple AI agents.
Creative professionals focusing more on strategy and narrative.
Technical professionals managing prompts and fine-tuning models rather than producing every output manually.

This shift does not necessarily imply workforce reduction. Productivity may rise, and job satisfaction may improve as routine tasks are delegated to AI.

Humane attributes as differentiators

As AI assumes more cognitive load, humane attributes may gain importance. Creativity, empathy and ethical judgment are areas where human contribution remains essential.

Creativity enables asking the right questions in a world where AI can deliver most of the answers.
Empathy becomes valuable as interpersonal interactions differentiate human services from automated ones.
Ethical reasoning and contextual judgement remain necessary in areas such as governance, legal systems and leadership.

Ironically, the rise of AI may increase the value of distinctly human traits within organisations.

Conclusion

This article outlined three central questions for the AI era. The answers evolve rapidly as adoption increases. Regardless of the exact trajectory, AI is clearly one of the decade’s defining structural shifts, with implications for business, markets and work.

We will continue following the development of the AI boom and its impact on financial markets closely and will keep you up to date. If you want to stay updated on investing megatrends and the latest market movements, subscribe to our Kvarn Pulse newsletter, follow us on social media and explore the educational articles on our website.