From experimentation to full-scale production
Over the past year, the application of AI, particularly generative AI and large language models (LLMs), has gained significant traction, resulting in a surge of AI experimentation and a search for business use cases among companies in virtually all industries. However, successful AI adoption requires careful planning, a digital core, and a long-term commitment to development.
The journey from AI experimentation to full adoption is complex yet crucial for companies to stay competitive and relevant. Fully integrating AI into the core of a company's products, services, or processes, and thus creating lasting value and competitive edge, is a challenging and evolving area requiring increased investment.
In this five-part blog series, we will dive into the central observations of the Nordic State of AI report, an annual report examining AI adoption across leading Nordic multinational companies by surveying their current use of AI. In this second part, we will explore the challenges and opportunities companies are facing related to AI adoption.
The current state of AI is characterized by growing interest and experimentation. However, the Nordic State of AI report highlights a pivotal challenge in AI's future: the transition from experimentation to production.
80% of survey respondents have started experimenting with ChatGPT-type large language model (LLM) products. Nearly 70% of surveyed companies have AI projects in development, and 86% expect these projects to move into production within the following year. Nonetheless, the vast majority are only scratching the surface with experimentation and minor proof-of-concepts while still looking for the right plan to reach tangible business value.
From scattered projects to shared practices
Companies are extensively experimenting with various LLM products and have numerous ideas for using AI.
The result is characterized by an ongoing experimental phase, with projects scattered throughout the organization and lacking the necessary scale to make a significant impact. As a result, customer satisfaction and perceived product value may be compromised while financially lucrative opportunities remain unexplored. AI projects and initiatives are mainly decentralized within companies, and there is a lack of shared practices around data.
“AI adoption is going forward but not really moving forward.”
Niko Vuokko, PhD
CTO, Silo AI
Value creation – When AI becomes truly attractive
As the report's central observations show, successful AI adoption plays a vital role in creating value with AI. AI has the power to fundamentally transform products, services, and processes, making them more efficient and increasing the value provided to end customers.
How and where AI is deployed significantly influences the return on investment. Maximizing the value of AI requires careful and considerate integration – a process that demands time and strategic planning. The majority of companies are using AI in their products or services (74,3 %), production or manufacturing processes (65,7 %), or customer experience (60%). Those are the main areas where AI should be explored in depth.
It's not merely about internal optimization but rather about leveraging AI to offer superior value to customers. This long-term strategic approach ensures sustained competitive advantage, prioritizing value creation over short-term efficiency gains.
Key challenges in AI adoption
While the focus in many companies is shifting, it is worth noting that many still prioritize short-term efficiency gains. Immediate efficiency improvements often overshadow the deeper integration of AI into core business processes and products. However, clearly defined business strategies are essential for leveraging AI's potential to enhance competitive advantage significantly.
Despite being aware of their goals and objectives, companies encounter numerous challenges. These include difficulties in managing and integrating large volumes of data, a lack of necessary AI skills among employees, and resistance to change within organizational cultures.
Overcoming these barriers requires strategic planning and practical measures such as enhancing employee training programs, investing in scalable AI technologies, and fostering a culture of innovation.
The imperative for strategy and roadmaps
Ultimately, the spotlight shifts away from technology. Over a third (37%) of respondents (up from 17% last year) believe that an unclear business strategy or roadmap is the main obstacle to expanding the use of AI.
Small technical issues can be fixed, but the real change will come from increased budgets and the implementation of long-term plans.
Even though there is expertise in creating strategies and plans, there is a shortage of individuals with the skills to implement them, marking the most significant talent gap. There is a clear disconnect between planning and implementation.
Investing in AI
Despite the popularity of LLM-based products, there is a forecast of limited investment in AI, suggesting a cautious approach toward full-scale adoption. Only 20% of respondents anticipate investing over €10 million in AI by 2024. More companies are prioritizing training and competence development over recruiting additional AI talent. This shift is primarily driven by the macroeconomic situation rather than changes to underlying strategies.
However, for those aiming to derive substantial value from AI, significant investments are essential – not just financially but also in terms of resources and strategic focus. These investments go beyond mere capital expenditure to encompass data quality and management, which are foundational to effective AI implementation. Integrating and operating AI models effectively also requires upgrades in technological infrastructures and the development of new skill sets among employees.
Investment in AI also includes fostering a culture of innovation within organizations, where AI can be seamlessly adopted and utilized. It is about creating an environment where AI tools are not just used for isolated tasks through various productivity tools, but building a digital core that can be elevated with AI through each development interaction.
“Getting significant value from AI is a long-term investment that should be made with care and consideration.”
Niko Vuokko, PhD
CTO, Silo AI
The bigger picture: AI's transformative potential
While generative AI and LLMs have caught the attention of most companies, this is merely a glimpse of what can be achieved with AI.
It's easy to draw parallels with the advent of electricity and the internet, suggesting that we are at a similar juncture with AI. The real challenge lies not only in adopting new technologies but in fundamentally rethinking business models and processes in an AI-driven era. The goal of integrating AI into products and services to create significant customer value is widely shared yet difficult to achieve.
Scaling AI has proven to be a surprisingly complex undertaking, a challenge that companies need to be aware of and prepared for. This complexity is partly due to the rapid progress of technology, but it is also influenced by the fact that company culture, including decision-making processes and leadership, does not change overnight.
Proper AI infrastructure, including shared data practices, is crucial for scaling AI use throughout a company or organization. Although progress is being made, substantial advancement is still lacking.
“AI is going to change how the planet works”
AI's journey from a novel technology to a core business innovation and efficiency driver is complex and challenging.
As businesses continue to explore AI, the transition from experimentation to full-scale production emphasizes the need for a strategic and clear roadmap. The Nordic State of AI report highlights the importance of integrating AI into core business functions, not just for efficiency but as a transformative force. The growing curiosity and recognition of AI's potential are promising, indicating a positive direction in corporate perspectives.
However, a significant challenge persists in the scarcity of skilled talent necessary to drive AI initiatives forward. To truly harness AI's potential, companies must invest in a culture that supports continuous learning and adaptability. Strategic integration, robust infrastructure, and visionary talent are crucial to unlocking AI's transformative power in redefining competitive landscapes. Ultimately, mastering AI integration means committing to an ongoing journey of innovation and value creation.
The next part of our Nordic State of AI blog series delves deeper into the key findings of the report concerning the increasingly growing bottleneck of AI talent.
Stay tuned, and in the meanwhile, download our report for more insights.
The annual Nordic State of AI report provides an overview of the use of artificial intelligence in the Nordic region, with the goal of offering business leaders, academics, policymakers, as well as anyone interested, a comprehensive view of the latest changes and developments in Nordic AI.
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