Lack of competence is the biggest barrier to AI adoption
What do people use AI for at work? Which AI solutions are the most widespread? Are they trained on internal company data? And where does AI have the greatest potential?
These questions were at the heart of Forte Digital's breakfast seminar Harder, Better, Faster, Stronger: Getting Started with AI Agents and Assistants. To map out AI use cases, we conducted a very informal survey among the attendees.
Although the survey has low statistical power with only a few respondents (70), it still reveals some interesting numbers that I think are worth sharing.
More than 70 people had made their way to Kunstnernes Hus to attend the breakfast seminar on AI agents.
How is AI used today?
The numbers show that AI is primarily used as a knowledge and productivity tool, especially in text production (56%) and information retrieval (51%), where quick access to information and streamlining writing tasks provide immediate benefits.
Automation (34%) and decision support (37%) are less widespread, likely because many businesses have not yet integrated AI into core processes. The same applies to customer service (20%).
Many also report that AI is used in idea development (47%), while code assistance (25%) is less common.
AI is primarily used as a knowledge and productivity tool.
Chatbots and AI assistants dominate.
Generative AI is primarily used for content production (36%), and chatbots/assistants (36%) are commonly used solutions and tools among the respondents.
The fact that 6% indicate they don't yet have generative AI solutions in place but are considering it suggests that most businesses have already adopted the technology in some form.
Usage is currently most concentrated around simple and accessible tasks, but the challenge going forward will be to leverage AI in more complex areas such as analysis, tailored customer experiences, and advanced automation.
AI is primarily used for content production (36%), and chatbots/assistants (36%)
AI on internal data: An untapped opportunity for many businesses
AI solutions trained on internal data are in an early stage – 22% have implemented them, while 18% are experimenting. At the same time, 15% are considering it but face challenges such as a lack of competence, investments, and data strategy.
However, only 3% report that they have no plans to implement AI solutions based on internal data. Challenges like data quality, infrastructure, and privacy are slowing adoption.
Those who succeed in using internal data will be able to develop more precise, effective, and relevant AI solutions that provide direct business value, such as in customer insights, personalization, or predictive analytics.
Only 22% have AI solutions trained on internal data.
Where does the greatest potential lie?
This is a natural development, as many AI tools have already proven effective in process improvement and cost reduction.
AI for customer insights and analysis (41%) and product development and innovation (41%) follow closely behind, indicating that many businesses view AI as a strategic tool to understand customer needs, identify market opportunities, and drive innovation.
This also reflects a trend where data is becoming an increasingly important competitive factor, and AI enables businesses to extract valuable insights more quickly and accurately.
Generative AI for text, images, video, and code (35%) shows that more and more are considering AI as a tool for creativity and content production. This aligns with developments in marketing, communications, and software development, where AI can contribute to both increased efficiency and new opportunities for content creation.
AI in sales and marketing (28%) is somewhat lower but still an important area. This may be because AI is still in a maturation phase within personalized campaigns and automated customer interactions, but the potential is significant for businesses adopting AI-driven targeting and optimization.
Respondentene ser størst potensial i automatisering og effektivisering (48 %)
What is the biggest barrier?
Lack of competence (32%) is the biggest barrier to AI adoption, making it difficult to identify use cases and ensure successful integration. As a result, it is natural that many take a cautious approach or wait for more insights before fully committing.
Unstructured data (24%) and legal concerns (23%) also create challenges. Costs (21%) are a barrier but less significant than competence and data challenges, suggesting that economics alone is not the main obstacle.
The mention of unclear business value (14%) shows that some are still struggling to see a clear ROI in their AI initiatives.
Lack of competence (32%) is the biggest barrier to AI adoption.
