AI Adoption Goes Beyond Technology And Is More Of A Strategic And Cultural Change

This article is part one of “Opportunities and Pitfalls of Generative AI from an SME Perspective” by senior reacher Janne Kauttonen and RDI Communications Specialist Martti Asikainen, originally published in Finnish in Haaga-Helia University of Applied Sciences’ eSignals Pro

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Janne Kauttonen & Martti Asikainen, 31.1.2025

In 2023, generative AI (GenAI) broke free from the exclusive domain of researchers and tech giants, becoming accessible to organisations of all sizes. This watershed moment sparked an explosion of innovation, giving rise to countless AI-powered startups and services.

For small and medium-sized enterprises (SMEs), this shift has irreversibly transformed the landscape of knowledge work and creative industries. Integrating generative AI into business operations has become not merely advantageous, but essential for firms hoping to maintain their competitive edge and secure future success.

In this article and its predecessor, we examine the distinctive features of generative AI, contrasting its operational characteristics and use cases with those of ‘traditional’ AI systems, which rely on predetermined rules and models. We specifically address the implementation challenges from an SME perspective, offering practical insights into technical considerations these businesses must navigate. 

For these businesses, the message is stark: incorporating generative AI into their operations is no longer optional. As market pressures intensify, it has become a fundamental requirement for survival and success in an increasingly competitive landscape.

A Strategic And Cultural Shift In The Workplace

Unlike traditional AI with its more predictable and limited functions – such as classification and clustering – Generative AI can create new content, interpret and generate complex data, and interact with users in near real-time. This capability stems from massive and comprehensively trained neural network models, known as foundation models, such as Claude, GPT-4, Gemini, and Dall-E, which require no additional training from the end user.

Thanks to foundation models, deploying generative AI no longer necessarily demands the extensive data collection and refinement typically associated with the initial phases of AI training – previously a prerequisite for creating and utilising custom AI models. Foundation models can be implemented directly, provided one understands the basics of prompt engineering and exercises appropriate caution regarding sensitive information.

AI has become part of everyday working life through user-friendly web applications and software-integrated AI components (plugins). From an organisational perspective, this means that implementing generative AI isn’t merely a technical upgrade – it requires strategic rethinking and cultural transformation throughout the organisation. For instance, in a software company, language models like ChatGPT can enhance the work of programmers, communication professionals, and managers alike – despite their vastly different roles and responsibilities.

Gaining A Competitive Advantage Requires Agility

Generative AI is shifting the focus of AI utilisation from automation towards innovation and creation. Currently, its impact is most pronounced in rapidly digitalising markets such as customer service, where it enables the production of various customised solutions for clients. The most typical examples include intelligent chat services or chatbots and various virtual assistants that enhance customer experience by providing round-the-clock support. Cloud-based, user-friendly tools such as ChatGPT (OpenAI), Claude (Anthropic), Poe (Quora Inc), Copilot (Microsoft), and Firefly (Adobe) can be directly applied to business operations.

Typically, challenges emerge only when organisations seek to gain competitive advantage from generative AI and customise models to their unique needs, such as leveraging proprietary data and intellectual capital. In achieving competitive advantage, organisational agility and innovation capability play crucial roles. Finland and the world at large are currently experiencing a hype phase surrounding generative AI. A characteristic feature of this phase is the tendency to reframe all problems and tasks as AI problems – the first question invariably being whether AI could solve problem X. This has partly led to an explosive increase in startups (Iansiti & Lakhani 2020).

In small and medium-sized enterprises, this hype manifests as a significant increase in AI products and services applicable to their business operations.However, companies would do well to maintain a level head regarding AI products and services, as many seemingly impressive AI services are often built upon the same foundation models (typically GPT-3.5 or GPT-4), with no guarantees regarding their security levels or long-term continuity. This concern emphasises the importance of familiar partner networks. Therefore, companies should first explore whether new GenAI solutions could be developed with trusted, proven IT partners.”

Focusing On Knowledge Work, Content Creation And Innovative Design

AI usage is currently drawing significant research interest. According to surveys, in 2023, one-third of all companies regularly used generative AI applications for at least one function, and up to 60% of businesses that had previously utilised AI in some capacity reported also using generative AI. Currently, company size appears to remain a significant factor in the adoption of new technologies.

Generative AI adoption is particularly low among small business owners with fewer than 51 employees. Surveys indicate that only 38% of small business owners have experimented with generative AI, with just 11% actively using it in their operations (Bhattacharyya 2023). The disparity is especially notable in the Nordic countries, where up to 77% of large companies reported using generative AI during 2023. According to Europe’s largest private AI laboratory, Silo.ai, this usage was almost entirely (71%) through large language models (Silo AI 2024).

Research suggests that approximately 75% of generative AI’s value comes from four key areas: customer service operations, marketing and sales, software development, and R&D activities (Chui et al. 2023). Its role, particularly in content creation, is gaining strong momentum among SMEs, as an increasing number of companies employ AI to support their communications and visual content to save on marginal costs. A typical example is personalised content for customers, especially used in sales and marketing.

According to German-based Statista, adoption has accelerated so rapidly that in the United States, only 17% of marketing professionals reported not using generative AI tools in their work (Dencheva 2023). Statistics show that knowledge workers whose roles involve text and/or images currently benefit most from generative AI. From a knowledge worker’s perspective, several AI-integrated office software options are available, such as Microsoft’s Copilot 365 for MS Office products and Bard for Google’s equivalent products.

AI In Product Design: Opportunities and Realities

Generative AI differs from traditional, predictive AI models particularly in that traditional models are developed for specific, narrowly defined purposes, which consequently limits their user groups. A prime example of this is financial administration automation, which extensively utilises automatic invoice recognition models (see, for instance, the Snowfox.AI case; Ruohonen et al. 2022).

However, this limitation doesn’t apply to generative AI. For example, the highly popular ChatGPT adapts to various text creation and modification tasks, with its benefits varying significantly depending on the task at hand. Thus, one can say that generative AI is reshaping the world of product design, enabling entirely new ways of working and even more innovative approaches to design and modelling.

"However, despite all the hype, it's worth remembering that traditional, non-generative AI still accounts for approximately 80% of all AI and advanced analytics' economic impact."

With the generative AI breakthrough, engineers and other designers can input predetermined design objectives with parameters (such as product performance, space requirements, materials, manufacturing methods, and cost constraints) directly into generative design software, which then maps possible solutions while creating alternative plans for achieving these objectives.

However, despite all the hype, it’s worth remembering that traditional, non-generative AI still accounts for approximately 80% of all AI and advanced analytics’ economic impact (Chui et al. 2023). In practical terms, this means that the world still runs on traditional AI, and it’s unlikely that generative AI will replace the need for more traditional AI in businesses, at least in the near future.


Our Recommendations

We offer the following essential guidance for small and medium-sized enterprises. These five points warrant careful consideration, as following them will help your business leverage AI effectively and responsibly (adapted from Koupanou 2023; Iansiti & Lakhani 2020):

1. Stay Informed and Invest in Learning

Actively monitor AI developments to stay abreast of applications and methods relevant to your sector. Social media platforms (Facebook, LinkedIn, and X), newsletters, and forums offer efficient ways to keep current. Take advantage of both free and paid AI training courses to update your team’s practical skills with minimal barriers to entry.

2. Experiment with Apps and Learn Their Limitations

Familiarise yourself with AI services through hands-on experimentation, particularly with prompts. Many free or low-cost GenAI services are available for testing without significant investment. Begin with widely-used tools like ChatGPT or Claude. Test various inputs and tasks to understand how AI responds to different requests. The key is gaining practical understanding of these tools’ strengths and limitations.

3. Foster Collaboration and Build Networks

Form learning groups with colleagues for shared experimentation and discovery. Identify AI developers in your sector and enquire about their offerings. Build connections with experts and businesses. Share experiences both within and beyond your organisation. Explore application possibilities collectively. Engage with local technology firms and startup communities, and participate in industry events and training sessions for networking opportunities.

4. Make your Business more Data-Driven

Eliminate data and process silos whilst centralising your IT architecture. A genuinely data-centric company can implement both GenAI and traditional AI more swiftly and effectively. Fragmented systems impede AI adoption, while unified IT architecture enables smooth data flow and efficient AI integration. Though this requires strategic planning and investment, it enhances operational efficiency and competitive advantage.

5. Prepare for Change

Given generative AI’s rapid evolution, today’s impossibilities may become tomorrow’s realities within months. Monitor your industry’s developments actively and prepare for significant disruption in coming years. Readiness for change is paramount. Develop your staff’s capabilities and ensure organisational adaptability. Conduct sector-specific scenario analyses of AI impacts and develop alternative strategies for your business.

Contact

Janne Kauttonen

Janne Kauttonen

Senior Researcher
+358 294471397
janne.kauttonen@haaga-helia.fi

Martti Asikainen

Martti Asikainen

RDI Communications Specialist
+358 44 920 7374
martti.asikainen@haaga-helia.fi

References

Bhattacharyya, S. 2023. Generative AI and Adoption Readiness of different size Businesses. Medium. Accessed 25.1.2024.

Chui, M., Hazan, E., Roberts, R., Singla, A., Smaje, K., Sukharevsky, A., Yee, L. & Zemmel, R. 2023. The economic potential of generative AI: The next productivity frontier. McKinsey & Company. Accessed 25.1.2024.

Dencheva, V. 2023. Popularity of generative AI in marketing in the U.S. Statista. Accessed 21.2.2024.

Iansiti, M., & Lakhani, K. R. 2020. Competing in the age of AI: Strategy and leadership when algorithms and networks run the world. Harvard Business Press.

Koupanou, N. 2023. The AI Boom: Practical Guide to Generative AI for Small Businesses. Towards AI. Accessed 22.1.2024.

Ruohonen A., Kauttonen J., San Miguel E. 2022. Hiilineutraalius: taloushallinnon tekoälysovellus apuna kohti globaalia tavoitetta. eSignals Pro. Haaga-Helia. Accessed 22.1.2024.

Silo AI. 2024. The Nordic State of AI. Accessed 24.1.2024.

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