Beyond the hype and political noise, where do we stand with AI?
The hype and political noise surrounding generative artificial intelligence has over the last three years intensified on a weekly basis. Indications of a financial bubble are becoming increasingly apparent, and the market is growing weary of unfulfilled promises and the science fiction-like pursuit of artificial general intelligence (AGI). Leaders and decision-makers struggle to grasp the real value. That is, if there is any real value to reap from artificial intelligence at all.
During WWII, the British armed forces applied statistical methods and mathematical modelling to optimise battlefield and supply chain logistics. This particular discipline is known as Operational Research, and it includes a wide range of mathematical methods for supply chain optimisation, rule-based systems, and analytical decision-making. The underlying mathematics are to a large extent similar to those used in current artificial intelligence. The main difference, or rather expansion, is that artificial intelligence as we know it today mostly relies on machine learning and deep neural networks, which is only possible due to the availability of unprecedented computing power and massive amounts of digital data for the training of the algorithms, both in the format of structured data, text, sound, and images.
From Big Data to a Decade of Digital Transformation
15-20 years ago many corporations spent big money on big data and data warehouse projects. We didn't really reap the value of those investments as fast as the consulting industry suggested, but they did lay down the foundation for the more recent decade of digital transformation which we have all been part of. Over the last decade or so, digital transformation has been a topic on every leader's agenda. If you look around where you are right now, you probably quickly realise that the effects and implications of this digital transformation are ubiquitous. Today’s teenagers can not even imagine a world without smartphones, they don’t understand how linear TV works, and they have never seen a compact camera. Personally, I haven’t visited a bank branch in 25 years, I get annoyed when companies send me paper invoices, and my tax reports are completed as swift verifications of automated reports.
Looking back at what we have achieved in terms of digital transformation, we are now at a stage where we have digital data, computing power, digital processes, and mathematical tools and methods. It makes sense to use mathematical algorithms and software engineering to better understand and shape our data and digital processes, and to innovate, optimize, and automate to achieve better products and services. This also opens up entirely new ways of working with and understanding digital representations of any asset, including the generation of outputs through generative AI.
Why AI Feels Like Magic—But Isn’t
However, to most people, the role of algorithms, particularly when applied to languages, is beyond comprehension. And when something is beyond comprehension, we attribute it to some kind of divine force. Humans have always turned to divinity as the explanatory model whenever the peculiarities of the world have been beyond our human comprehension, and we are doing it again with artificial intelligence.
By cutting through the hype and embracing AI for what it truly is: a powerful evolution of digital transformation rooted in data and mathematics, leaders can unlock immense opportunities. Leaders who take the disciplined route through learning about the real capabilities and limitations of mathematics, software, and data engineering will be able to unlock the long-term strategic disruption. Those who succeed in taking the next step in digital transformation will be able to strategically optimise operations, create new value, and build a more intelligent and sustainable future, far beyond the fleeting promises of quick-win fads.
How Leaders Can Move Beyond the Hype
As you consider the true potential of AI within your context and navigate both opportunities and risks, ask yourself:
How is your organization currently distinguishing between AI hype and its pragmatic problem-solving applications?
Beyond specific generative technologies like ChatGPT, Copilot, or Gemini, what foundational understanding of data, digital processes and mathematical tools does your leadership team need to have for strong long-term success?
Are you viewing AI as a short-term trend or as the natural next step of digital transformation demanding strategic disciplined investment?
Successfully moving from hype to meaningful long-term change requires more than just experimenting with the latest tools. It requires a willingness to engage with AI as part of the broader digital transformation, with all its opportunities and risks. If this resonates with you, I can help translate that vision into practice. Whether by guiding your leadership team in shaping pragmatic AI plans or by sparking new perspectives through keynotes and workshops, I bring clarity and structure to what often feels like an overwhelming landscape. Together, we can make AI a strategic enabler rather than just the passing trend.
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A quick disclaimer: I am pretty sure that both data scientists, operational researchers, and AI experts of all kinds will jump on me for this simplification, as they all have their own territories to defend. However, from a leadership perspective, I believe that those turf wars are not relevant. What matters is that you understand that artificial intelligence is about mathematics applied to large amounts of data and digital processes, that this is not magic, and that it is the natural next step of digital transformation.
This article is the first in 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.