AI in Business - Artificial Intelligence promises to redefine how value is created, captured, and delivered across all sectors—from commercial enterprises to public institutions. While investments in AI technology continue to grow, the real test lies in integrating AI into organizational strategies and cultures. Leaders must address questions of how to effectively deploy and integrate AI, how to adapt workforce skill sets, and how to balance efficiency with critical considerations like data privacy and equity.

The Silver Economy - Over the coming decade, countries such as Norway are projected to become “super-aged” societies, with more than 20% of their populations aged 65 or older. This demographic shift carries significant economic and social implications. Employers face potential workforce shortages, while demand soars for products and services tailored to senior citizens, ranging from healthcare innovations to financial solutions. At the same time, responsibility for aging is increasingly shifting from government bodies to individuals, putting pressure on households and businesses to devise sustainable strategies for eldercare, employment, and societal well-being.

A Business Perspective - Neither of these challenges—AI disruption nor demographic transformation—can be addressed purely through technological or policy lenses. They demand expertise in strategy, economics, and organizational behavior, as well as leadership and ethical considerations. Ultimately, the key to addressing these grand challenges lies in recognizing that they are deeply rooted in how businesses and the economy function.

DIG as a consortium is skilled and motivated to help solve these challenges through research and practical cases from our partners.

Business Intelligence

DIG aims to become Norway’s leading group for capturing the economic values of AI/ML technology in organizations’ downstream activities. We transform business challenges into data-driven solutions by converting qualitative requirements into quantitative analyses, leveraging advanced AI and machine learning methodologies to capture economic value and drive sustainable growth.

Principal Investigator: Ivan Belik. 
Research team: Nhat Quan Lie, Einar Breivik

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  AI and data-driven methodologies are transforming business decision-making, optimizing strategic processes, and generating economic value. Through collaboration between academic and industry partners, interdisciplinary approaches are developed to translate business challenges into data problems solvable through advanced analytics. Leveraging artificial intelligence, large language models (LLMs), and machine learning (ML), business intelligence is refined to transform complex organizational data into actionable insights.

  State-of-the-art AI tools, natural language processing (NLP) techniques, and high-performance computing enable the analysis of large-scale business data. By detecting complex patterns and refining data-driven strategies, organizations can enhance efficiency, productivity, and competitive advantages across industries. Advanced AI infrastructure, including Norway’s first deep-learning-capable AI server, facilitates computationally intensive tasks, further driving innovation in business intelligence research.

  As businesses navigate an increasingly digital landscape, AI and ML serve as essential tools for sustainable growth. From improving data collection and integration to optimizing strategic decision-making, AI-powered business intelligence provides robust solutions that help organizations unlock their full potential.

  Key questions include:

  • What methodologies can effectively translate business challenges into data science/analytics problems?
  • How can qualitative business requirements be seamlessly integrated with quantitative data analyses?
  • What are the challenges and solutions associated with big data, including prediction accuracy, data quality, GDPR compliance, and limited data scenarios?
  • How can AI and large language models (LLMs) improve the accuracy and efficiency of data analysis in business intelligence?

Consumer Behavior and Technology Adoption

We investigate how consumer behavior evolves within digitally transforming markets shaped by AI and demographic shifts, providing actionable insights that enable both the private and public sectors to design, implement, and manage innovative digital services.

Principal Investigator: Helge Thorbjørnsen.
Research team: Jareef Bin Martuza, Aruna Divya Tatavarthy

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  Understanding human behavior is key to digital innovation. New digital services are of little value unless they are adopted and used by organizations, clients and consumers. As the majority of new products and services in fact fail, it is crucial for both commercial firms and government institutions to understand the drivers and barriers of new service adoption, as well as how to change consumer behavior in digital environments. 
  DIG research focuses on how organizations can increase commercial success by lowering consumer adoption barriers, removing uncertainty and ‘nudging’ consumers to change their behavior in digital environments and complex service systems. Together with industry partners, DIG offers new perspectives and tools for understanding and influencing how consumers think and act in such complex decision contexts.

  Projects:

  • Consumer trust and honesty: DIG focuses on how organizations can build and maintain trust when customer interactions are primarily digital and when interactions allow for cheating and dishonest behavior.
  • Happiness and well-being: DIG focuses on consumer outcomes beyond marketing-related constructs such as satisfaction, purchase and loyalty. For consumers, end-results such as happiness, meaning and psychological richness are arguably even more important.

Creating and capturing sustainable value

How can firms integrate strategic technologies, adapt organizational structures, and innovate their business models to create and capture sustainable value in response to two transformative global challenges — AI's disruptive impact and the rise of the Silver Economy.

Principal Investigators: Lasse B. Lien, Tina Saebi, Magne Angelshaug, Tor W. Andreassen, Bram Timmermans
Research team: Eirik Sjåholm Knudsen, Björn Schmeisser, Vidya Oruganti, Frank Elter, Kristina Heinonen, Seidali Kurtmollaiev, Line Lervik-Olsen

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  In an era of rapid technological change, remaining competitive requires more than simply adopting new digital tools. True value emerges when these technologies are strategically integrated into business models, organizational structures, and innovation processes. While emerging technologies offer opportunities for efficiency gains and market differentiation, their widespread availability makes it essential to combine them with complementary assets, capabilities, and innovations to achieve sustained competitive advantages. However, many organizations face challenges in aligning technological adoption with broader business transformation, limiting their ability to fully capture long-term value.

  Competitive advantages through strategic technology adoption can be achieved through three key pathways:

  • Strategic integration – Embedding digital technologies within operations to enhance efficiency, innovation, and value creation.
  • Technology differentiation – Gaining a competitive edge by developing or accessing proprietary technologies, unique data sources, or specialized expertise.
  • Hybrid advantages – Combining technological advancements with organizational capabilities and complementary assets to build long-lasting competitive advantages.

  Key research questions

  • How can firms effectively integrate digital technologies with complementary assets, capabilities, and innovations to enhance their business models and organizational infrastructure?
  • What strategies enable firms to develop or access superior digital technologies that provide a competitive edge?
  • How can firms combine technological advancements with complementary assets and capabilities to create hybrid competitive advantages?
  • How can firms leverage digital technologies to address sustainability challenges while remaining competitive? 

Governance, Leadership and Change

We investigate how governance and top-level leadership foster necessary transformation and build long-term capacity for change and innovation in the face of AI’s disruptive potential and the opportunities in the emerging Silver Economy.

Principal Investigator: Inger G. Stensaker
Research team:
At NHH: Therese Egeland, Jonas Hammerschmidt, Dorotea Rossi Kriscak, Lasse Lien, Bram Timmermans, Rune Bjerke
At SNF/AFF: Researchers: Synnøve Nesse, Thora Lou, Marius Jones

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  Responding to shifting societal and technological realities (such as AI and the aging population) invariably implies business transformation among firms and industries. These macro-level developments create opportunities for new and emerging entrepreneurs and industries while requiring strategic change, innovation and reorientation among existing firms and industries. These types of strategic change have proven particularly challenging for well-established firms with a history of success, as they tend to become constrained by structural and cultural inertia (often referred to as the success paradox). In this stream of research, we investigate how governance and top-level leadership foster necessary transformation and build long-term capacity for change and innovation in the face of AI’s disruptive potential and the opportunities in the emerging Silver Economy.

  Key questions include:

  • How do governance structures—encompassing boards, chairs, and CEOs—shape strategic transformation and innovation for sustainable growth in the face of shifting societal and technological realities?
  • How can top-level executives navigate internal and external pressures, sustain momentum for change, and foster organization-wide engagement amid shifting societal and technological realities?
  • In what ways does ownership structure (e.g., private, public, non-profit, private equity) influence decision-making processes and the success of transformative efforts?
  • How can organizations effectively align multiple transformative changes while adapting their strategies, structures, and behaviours to capitalize on emerging demographic and market opportunities?

Future of work

We investigate how AI-driven transformation  and the Silver Economy combine to reshape workforce dynamics, skill demands, and labour market structures from organizational, individual, and policy perspectives.

Principal Investigator: Therese Egeland
Research team: Karen Modesta Olsen, Alexander Madsen Sandvik, Vidar Schei, Bård Fyhn and Christer Andre Flatøy

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  The research group aims to explore how AI-driven transformation and the silver economy are reshaping the future of work, with a focus on aging populations and technological advancements. By examining strategies to empower senior employees, leveraging emerging technologies to enhance quality of life, and adapting HR systems to demographic and technological shifts, the research seeks to foster inclusive, productive, and meaningful work environments. Ultimately, the goal is to ensure that innovations like AI and flexible work arrangements align with the evolving needs of older individuals while promoting health, happiness, and organizational performance.

  Key questions:

  • How can evidence-based strategies be developed and implemented to empower senior employees to actively shape their careers, promoting happiness, health, and productivity in their later career stages 
  • How do flexible work arrangements, including remote work, digital solutions, and flexible employment relations, impact the length and quality of working lives from an individual perspective, particularly in terms of health, work-life balance, productivity, and job satisfaction?
  • How are the needs and preferences of individuals aged 65 and older evolving, and in what ways can emerging technologies be leveraged to support these needs, enhance quality of life, and extend the period of independent living for this demographic?
  • AI as a team member - What practical strategies can managers implement to effectively leverage AI in order to enhance team dynamics and improve overall organizational performance?
  • How can HR-systems adapt to technological advancement and challenges of an aging population?