Forget AI, we need to talk about the next step in digital transformation
Between the massive AI hype, the political and financial noise, and disturbing AI dystopias, there is a pragmatic space where data, mathematics, and software enabled by specialised hardware chips create real opportunities for value. That is, if you apply the right tools in the right sequence for meaningful tasks.
The umbrella term AI covers a wide range of tools and methods, with generative tools currently demanding most of the attention.
There is no doubt that the capabilities of these tools are beyond what most people were even able to imagine three years ago when ChatGPT was launched. However, from a bird's-eye view, all these tools are mathematical algorithms and software applied to large amounts of data. Not magic. Only when we reject the ideas of magic and silver-bullet solutions will we be able to use these tools in pragmatic, realistic and value-creating ways. And no, I'm not talking about better ways of outsourcing thinking to ChatGPT.
Between the massive AI hype, the political and financial noise, and disturbing AI dystopias, there is a pragmatic space where data, mathematics, and software enabled by specialised hardware chips create real opportunities for value. That is, if you apply the right tools in the right sequence for meaningful tasks. The umbrella term AI covers a wide range of tools and methods, with generative tools currently demanding most of the attention. There is no doubt that the capabilities of these tools are beyond what most people were even able to imagine three years ago when ChatGPT was launched. However, from a bird's-eye view, all these tools are mathematical algorithms and software applied to large amounts of data. Not magic. Only when we reject the ideas of magic and silver-bullet solutions will we be able to use these tools in pragmatic, realistic and value-creating ways. And no, I'm not talking about better ways of outsourcing thinking to ChatGPT.
The art of persuasion
The term AI was coined in 1956 by John McCarthy. To establish a distinct, ambitious agenda, separate from contemporaneous labels like “cybernetics” and “automata theory", he named it "Artificial Intelligence." It was, in other words, a marketing trick to attract attention and persuade stakeholders of the uniqueness of this new discipline. And so the biggest snake-oil label of all time was born.
In 2017-2018 I worked for a large tech consulting company. “Digital transformation” was used, abused, and worn out, and we needed new toys in the corporate sales rep suitcase. The term AI was coming back into fashion, and we saw a major commercial opportunity in promoting AI as the new kid on the block. In the beginning, the heads of the analysis teams demanded that we talk about "AI and Advanced Analytics" in the same pitches. Gradually, we got rid of the 'Advanced Analytics' and went all in on AI. Just like every other consulting and tech company on this planet. A move which was both brilliant and disastrous. Brilliant because we got a lot of attention. Disastrous because… well, just look around. The current stage of technological development is a mess in so many ways. At the same time, the value for our societies and businesses is still limited.
Back to the future
A few days ago I facilitated an AI workshop with a small industrial tech solution provider. The team consisted of mostly experienced people with limited knowledge of, and experience with, AI. They had started to test genAI for coding, but only for limited tasks and with ambiguous results. As part of my introduction, I brought up a Gartner illustration from 2012 which describes the development of analytics from hindsight through insight to foresight.
In 2012 I worked in an insurance company. I spent a lot of my time trying to explain to the top brass why it made sense to use mathematics applied to our data (also known as analytics) to not only establish hindsight but also develop insight. As I had then recently graduated with an MSc in operational research, the value of prediction models for developing foresight was obvious to me, but my colleagues were not quite as easily convinced.
Back to 2025, with the current AI paradigm in mind, I took the liberty of amending Gartner's original illustration with genAI. From hindsight and insight, AI now provides tools and methods to create foresight AND to create outputs (aka generative AI). However, looking at the market overall, which consists mainly of small and medium-sized enterprises, most companies are still struggling to establish data governance and analytical mindsets to achieve the bottom-left half of Gartner's illustration.
As I connected the dots of advanced analytics from hindsight to creation, the experienced tech resources I was facilitating this workshop for started to shed some of their fear and confusion and began to lean into how the AI toolbox might actually useful.
Beyond the hype and noise, there lies value for those who have the competence to look
Through my work as speaker and advisor, I meet leaders across a wide variety of industries from all corners of Europe. They all struggle with the same fundamental challenge: how to embrace and adopt AI in a responsible, value-creating, and pragmatic way. The needs I address, whether in the format of a keynote, an inspirational workshop with employees, or boardroom sparring, always revolve around building competence, disentanglement of topics, debunking of hype, removal of noise and distractions, and a pragmatic focus on responsible and real value-creation.
The term AI has become lethally infected with nonsense, hype, fear-mongering dystopias, global-scale power games, and value-destructive quests for "low-hanging fruits". If you spread your mental wings and take a bird's-eye perspective on the AI toolbox as we know it today, and our journey of digital transformation, you realise that applying mathematical algorithms to large amounts of data and digital processes, enabled by specialised hardware chips, is the natural next step of digital transformation. The term AI unfortunately adds a distractive layer of deceptive illusions.
I need to add a substantial warning, though. Shredding the term AI and viewing the technology landscape through the lenses of digital transformation is in no way a simplification in terms of success criteria. On the contrary, if you have read some of my previous articles about the competence requirements for success, you will recognise that real and responsible value-creation means that you, as a leader and decision-maker, will have to get your hands dirty and truly understand your company's data piping and business processes.
The AI snake-oil bubble is likely to burst. Data, mathematical algorithms, and software will prevail and continue to develop.
Food for thought
This week, I only have one question for you to think about: To enable yourself and your company to get out of the AI hype and noise quagmire, which competence do you need to acquire? And perhaps equally important: what are you willing to unlearn to get there?
Because without unlearning the noise, the myths, and the seductive simplicity of the AI label, the real opportunities will remain hidden in plain sight. Real value-creation starts not with more technology, but with clearer thinking. And that begins with you.
This article is the a part of my series Where Do We Go From Here? AI as the Natural Next Step in Digital Transformation. Want the rest delivered straight to your inbox? Subscribe to my newsletter.
About the Author
Elin Hauge is a business and data strategist, pragmatic futurist, and eternal rebel. With a unique background spanning physics, mathematics, business, and law, she brings fresh and thought-provoking insights into artificial intelligence and other data-driven technologies. Her work connects today’s realities with tomorrow’s possibilities, focusing on demystifying AI, harnessing data power, managing algorithmic risk, and guiding leaders through digital transformation.