(02/2025) AI and a Look Back at 2024 — Applications and Implications
About this Series
This article is part of a series of columns on artificial intelligence originally published throughout 2025 in Mreža magazine. Written from the intersection of psychology, business, and emerging technologies, these reflections aim to provide both practical insights and critical perspectives on the rapid evolution of AI and its broader social, economic, and ethical implications.
Each column captures a moment in time, while contributing to an ongoing conversation about how we understand, implement, and adapt to artificial intelligence — individually, organizationally, and collectively.
The year behind us was anticipated as yet another that would push the boundaries — from further developing the foundations on which technology rests, to the ways existing tools are applied and how frequently they are used. Bill Gates declared a year ago that 2024 would be a turning point. To what extent did that actually happen? My goal is to go through some facts and let each reader draw their own conclusions.
What is certain is that alongside a continued focus on the applications of the technology itself, we have perhaps become even more engaged with its implications.
On Applications and Implementations
As some have noted, the AI market buzzes like an overcrowded club where countless DJs are all playing their own tracks simultaneously. The dance floor? Like a bag of fleas on steroids. Keeping up with the rhythm in the jungle of artificial intelligence is a truly demanding job — something I can confirm from personal experience, as I spend a significant portion of my time just tracking the basic innovations.
On one side, we see data showing that AI investment and its ROI are improving. Global AI investment, which totaled $166 billion in 2023, is projected to grow to $423 billion by 2027. Generative AI (GenAI) is recording annual growth of over 70%, outpacing traditional IT spending. The return on investment is impressive: for every dollar invested, companies are achieving an average return of $3.50, while leading AI adopters are seeing returns of up to $8 per dollar. And this ROI materializes quickly — most implementations take less than 12 months, and returns are realized within 14 months.
Naturally, examples are multiplying every month. Marks & Spencer is one such case. The retail giant achieved a tenfold return on its AI investment, generating significant savings and increased revenue through AI-based personalization. Similarly, a major U.S. e-commerce company boosted engineering team productivity by 10–30%, leading to savings exceeding $100 million.
Versatility and Structural Challenges
AI’s versatility is evident across industries: in biotech, it’s accelerating drug discovery; in retail, it’s optimizing supply chains and personalizing customer experience. However, such data is no longer surprising — it mostly confirms previously announced projects and predictions, now in the hundreds.
What remains problematic, as confirmed by some MIT research, is the lack of strategy and a tendency toward reactive adoption of tools identified as “useful.” This is something I’ve highlighted in recent training sessions for executives of Croatian companies. The key factors for successful AI adoption are well known and relatively simple:
- investing in data quality and infrastructure,
- focusing on use cases that create competitive advantage,
- collaborating with reliable partners and suppliers, and
- applying responsible AI to ensure safety and trust — though for most organizations, what this means in practice remains unclear.
My small contribution to this field is an AI Ethical Code, recently completed and likely to be adopted by the International Project Management Association (IPMA). I expect such documents will undergo frequent revisions and improvements in very short intervals.
Implications: From Tools to Meaning
While there are plenty of interesting examples and challenges in AI application, what I found most fascinating in 2024 were the conversations and debates around AI’s implications.
One emerging theme was the future purpose of economies. In what we might call the “pre-AI tertiary era,” we are witnessing the birth of a new paradigm — the “meaning economy.” This transformation centers on the search for purpose and meaning in a world where automation is redefining the relationship between humans and work.
Albert Einstein once famously said, “Work is what connects a person to reality.” The validity of this idea is evident in everyday life: meaningful work provides people with structure, purpose, and shapes a key part of their identity. However, as automation progresses, this fundamental connection may erode. The consequences are already widespread — a sense of alienation and devaluation is growing, leaving many questions unanswered.
AI is not the cause, but rather the culmination of broader technological trends. Some are beginning to wonder how to find their place in the era of Homo Obsoletus. In a world where humanity risks becoming obsolete, we are confronted with the challenge of preserving human dignity amidst relentless technological progress.
This monumental task demands an intelligent balance between technological efficiency and cultural as well as societal adaptation. As I’ve said many times: “The real challenge isn’t technological change itself — it’s cultural and societal adaptation.”
The key answers may lie in creativity, human relationships, and social engagement, which should take center stage in shaping new values and priorities. A new societal narrative can highlight the intrinsic worth of individuals, offering a sense of belonging and purpose.
I am currently conducting research on this topic, the results of which readers of this column may soon see.
A Final Thought
Does the rise of artificial intelligence not offer a unique opportunity to radically rethink our understanding of work and the very meaning of life?
Some answers — and new questions — may be waiting for us in 2025.
