Why the Major Push for AI May Prove Revolutionary
AI agents are moving from idea to practice, powered by reasoning language models capable of planning, remembering, and managing ever‑longer chains of tasks. Automation is shifting from deterministic rules to probabilistic judgment. This pushes human value creation toward clearer goal‑setting and smarter process design. At the same time, the threshold for building tailor‑made software is collapsing. So, what does this mean for organisations that want to keep up?
In just a few months, the discussion about AI agents has exploded. From a niche technical concept in early 2024 to record‑high search interest in autumn 2025, many now believe AI agents represent the next major leap in the implementation of artificial intelligence. But what is really driving this shift? And more importantly: what does it mean for organisations and their work with foresight, strategy, and environmental scanning?
The answer lies in the convergence of several powerful trends: more—and better—language models, models that can execute tasks in multiple steps, reasoning AI that makes fewer mistakes, and output that increasingly matches expert‑level competence. Combined, these factors open the door to a new era of automation in which tasks that once required manual effort—such as advanced research—can now be delegated.
From Algorithms to Autonomy
Digital automation is not new. Historically, however, it has been limited to deterministic IF‑THEN logic—excellent for repetitive workflows but demanding to maintain and too rigid when needs change. With the rise of language models, a paradigm shift is underway: automation is moving from static rules to probabilistic judgment.
Today, an AI agent can not only read an inquiry sent to an organisation’s general inbox—it can also assess whether the issue requires human involvement or can be handled automatically. It can search policy documents, compare with similar previous cases, and even know when to ask for help. This is fundamentally different from earlier automation solutions.
Three Insights That Change the Game
1. Reasoning Models Break the Snowball Effect
Earlier AI models tended to make mistakes early in a dialogue—mistakes that snowballed as the model continued generating responses based on earlier errors. Reasoning models such as GPT‑5 or Claude Opus 4.1 break this cycle by processing each step methodically and independently. The result? GPT‑5 can complete more than 2,100 consecutive steps, while non‑reasoning models fail after only four in similar tasks.
2. Good Ideas Are Now the Bottleneck—Not Programming
The threshold for building customised software has collapsed. Startups like Swedish Lovable demonstrate this vividly by letting users create full web apps simply by describing what they want—no coding required. Meanwhile, tech giants like OpenAI are releasing tools to automate entire workflows. It used to be easy to have an idea but hard to build it. Today it’s the opposite: building is easy; identifying a valuable idea in the noise is harder.
3. Ett generationsgap öppnas upp
In Kairos Future’s study The Talent Hunt, 53% of Generation Z (born 1995–2009) reported regular AI use, compared with only 22% of Generation X (born 1965–1984). This gap is more than a digital divide—it signals who will drive the next wave of innovation and productivity. Younger employees who already prefer “ChatGPT‑ing” over Googling are developing work practices that are native to the new technology.
Numbers That Speak for Themselves
Two data points in the report are especially revealing:
Nearly on Par with Experts: The latest model Claude Opus 4.1 was compared with industry experts averaging 14 years of experience. In realistic tasks (4–7 hours of work), its results were judged equal to or better than the experts’ in 48% of cases—a win rate approaching the 50% threshold at which AI performs consistently on expert level.
Exponential Progress Continues: The length (and complexity) of tasks AI can complete has doubled every seven months since 2019. If this trend continues, the most advanced AI systems will soon be able to carry out month‑long projects independently.
Three Conclusions for Future‑Oriented Organisations
From Doing to Reviewing
As more tasks become automatable, the role of knowledge workers shifts. The “doing” used to be the most resource‑intensive part of a process. Now planning, process design, and quality review matter more. This requires a mindset shift—from being a task performer to acting more like a manager of AI agents doing the execution.
AI‑Native Companies Have a Structural Advantage
Organisations built today with AI at their core will enjoy the same type of structural advantage that “internet‑native” companies held over incumbents during the past two decades. The rules for service‑based industries have changed dramatically. Those who experiment with discipline, standardise without locking themselves in, and secure quality along the way stand to gain the most.
Inspiration Is the New Scarcity
In our analysis of AI maturity in a European organisation, inspiration and insight into what AI can accomplish ranked among the most important levers for increasing AI adoption. In many cases, we are more limited by imagination than by technical constraints. What is needed is not only technical competence but also the ability to see new possibilities—and the courage to test them.
The Age of Agents Requires New Perspectives
The overall trend points toward the industrialisation of software—agents increasingly handle execution in the background, while strategic craftsmanship steps forward. Just as the industrial revolution moved production from artisans to factories, AI agents are now automating more and more digital work. Routine tasks such as data analysis, document review, and information search, once handled manually, can now be delegated to agents. This frees up space for strategic craft: asking the right questions, interpreting context, and making sound judgments.
For organisations working with foresight and strategy, this shift represents both challenge and opportunity. The best agents will be the ones we barely notice—the ones seamlessly integrated into our workflows, freeing time for what genuinely requires human judgment.
At Kairos Future, we have spent 30 years helping organisations understand and shape their future. With our expertise in trend analysis, foresight, innovation, and strategy, we can support you in navigating the AI era—from identifying where AI creates real value to designing processes that balance automation with human judgment. Contact us to explore how your organisation can take the next step into the world of agents.