Just about any business today, from the smallest startup to the largest conglomerate, is talking about artificial intelligence (AI) and how they are incorporating it into their business. Indeed, the results are promising. A societal economic analysis commissioned by The Confederation of Norwegian Enterprise suggests that digitalisation and AI could contribute up to around €490 billion in additional value creation by 2040 for the Norwegian economy. Generative AI is projected to contribute €175 billion, while other advanced digital technologies and non-generative AI could contribute €315 billion in the same period.
Amid the hype surrounding artificial intelligence, Elin Hauge, a Norway-based AI and business strategist, urges businesses to temper their enthusiasm and avoid creating an “AI gold rush”. She says it can be incredibly tempting for business leaders to jump straight to deploying ChatGPT and hope that it will somehow, magically conjure supernatural value. Instead, they should take their time to learn about AI to gain an understanding of how it really works and how it may affect their business.
“The danger with this is that most business leaders don’t want to get their hands dirty, and they don’t comprehend how their data and processes will be fundamentally reshaped by AI,” she says. “I help leaders to understand how to reap value through the use of the AI toolbox, and how by applying mathematics to large amounts of data and digital processes, they can automize, optimize, and disrupt their business.”
Hauge says that first, businesses must understand what AI is, and what it is not. Part of her job is to demystify AI, which is necessary to help businesses unlock its value. While its popular name is artificial intelligence, she says that AI is not intelligent at all in a human sense. Unlike humans, AI does not have sentience or emotion, and AI programs merely apply the principles of statistics and mathematics to large amounts of data. Most of AI’s fundamental mathematics has been known for decades. Due to developments in computing power, software architecture, copious amounts of data, and sensor technologies, we now have machines that can learn by themselves, hence the name machine learning. Thus, AI is the logical next step of the digital transformation.
Additionally, Hauge highlights that AI has more applications than we can imagine. Generative AI programs, such as ChatGPT for text and Dall-E for images, are currently in the public spotlight. However, the potential value creation by non-generative AI should have a much more predominant role in strategic discussions. Predictive AI, which uses machine learning to identify patterns and make predictions about future events, can be applied in numerous industries, such as medicine, transportation, and agriculture. Examples of this include automated detection of cancer tumors in CT scans, optimisation of route planning for public transportation, and surgical precision in vegetable fertilization.
“I fear that many companies are going to get lost in the rush and invest heavily in an externally created AI strategy that remains disconnected from the business strategy,” Hauge says.
She observes that many boards and C-suite members prioritize diving into the AI space to avoid being left behind by competitors. But, to really reap the benefits, they need to have a deeper understanding of how AI works and redefine how they see the world. Digital transformation has enabled what she calls “digital twins” of business models, and the key to success in embracing AI lies in being able to apply the right tools of the AI toolbox for the right purposes.
“Bottom line, what businesses need to do is to re-examine their business strategy and really understand their business in terms of its digital representation, and then have a look into their AI toolbox and find the right tools and applications for their business. Just like in a physical toolbox, where the hammer and the screwdriver do very different things, leaders should understand the difference between models for interpretation and classification, prediction models, and generation models. I work with companies to understand these fundamentals and allow them to bring out the tools that are the most relevant for their specific strategies.”
