The story of the relationship between man and machine is often told through the prism of a single event: Garry Kasparov’s loss to supercomputer Deep Blue in 1997. In the popular narrative, this was the moment of the symbolic passing of the baton, the beginning of the dominance of silicon over protein. But from a business and strategic perspective, it is much more interesting what happened afterwards. Chess did not disappear. On the contrary, it has evolved into a model of so-called ‘centaur chess’, where teams made up of a human and an algorithm achieve results that are unattainable by either a standalone grandmaster or a standalone computing engine.
Today, almost three decades later, the same mechanism is beginning to shape the global economy. We are at a turning point that analysts are increasingly comparing to 1999 and the Cloud Computing revolution. Back then, software distribution and infrastructure scalability were at stake.
What is now at stake is redefining the very nature of operational work through the implementation of so-called Agentic AI – artificial intelligence based on autonomous agents.
The key challenge has ceased to be an existential question (“Will AI replace us?”) and has become an architectural question: how do we design an organisation to avoid “digital friction” and effectively integrate silicon agents with human capital?
Cognitive dissonance: Smart Home, Legacy Office
The current technological landscape in large organisations is characterised by a specific paradox. The end user – who is also an employee of the corporation – is experiencing unprecedented digital fluidity in his or her private life.
Consumer applications, supported by advanced algorithms, predict intent, integrate payments, logistics and communication in real time. The experience is holistic and immediate.
Meanwhile, once logged into company systems, the same user is confronted with the reality of distributed applications, data silos and manual processes. CRM, ERP or HRIS systems often do not communicate seamlessly with each other, forcing a human to play the role of a ‘human API’ that manually moves data from one window to another.
It is in this gap – between the expectations set by the consumer market and the realities of the enterprise environment – that the demand for a new generation of solutions is born. Agentic AI is no longer just an analytical tool or a text generator. It is an attempt to transfer this consumer fluency and decision-making into the complex bloodstream of the enterprise.
The 95 per cent trap and the “Agent Gap”
Enthusiasm for generative artificial intelligence (GenAI) has led to thousands of pilot projects being launched in recent years. However, a cool analysis of the data – corroborated by, among other things, MIT studies or reports from consultancies – indicates a worrying trend. It is estimated that up to 95% of these initiatives do not get beyond the Proof of Concept (PoC) phase and into production environments.
The reason for this is rarely due to the inadequacy of the language models (LLMs) themselves. These models are ‘intelligent’ enough to understand commands. The structural problem is a lack of integration, a phenomenon referred to as the ‘Agentic Gap’.
Artificial intelligence in isolation is glamorous, but not very business effective. In order for an AI agent to do real work – for example, to handle a return of goods on its own, change parameters in the supply chain or prepare a personalised B2B offer – it must have access to:
- Trusted real-time data (Data Layer).
- Business logic and compliance rules.
- Possibilities for calling actions in other systems (Action Layer).
The failure of most implementations stems from trying to overlay modern AI on top of an outdated, unstructured data infrastructure. Without a solid integration foundation, agents remain ‘hallucinatory advisors’ instead of becoming trusted task performers.
From automation to autonomy: The Agentic Model
The difference between legacy automation (RPA) and Agentic AI is fundamental. Traditional automation follows a rigid scenario (if/then). Agentic AI has the ability to reason, plan sequences of actions and adapt to changing conditions, while maintaining human-designated safety barriers.
Implementing the agentic paradigm means moving from a ‘man operates a tool’ model to an orchestration model, where a human manages a swarm of agents. In this new working architecture:
- Agents take on repetitive tasks, requiring analysis of large data sets and rapid, low-level decision-making.
- People are migrating towards high-value-added tasks: exception management, strategy, human relations and ethical oversight.
This is not a zero-sum game where the machine’s gain is the human’s loss. IDC analysis suggests that by 2030, AI-driven digital work will generate a global economic impact of trillions of dollars. This value will not arise from cost reductions (job replacement), but from the reallocation of resources.
Freeing specialists from the administrative burden allows them to explore business areas that were previously neglected for lack of time or capacity.
Integration as a new innovation
The lessons from the current stage of AI development are clear. The time of isolated experiments is coming to an end. Competitive advantage is being built by organisations that can systemically integrate AI into the core of their business.
The Agentic AI implementation strategy should be based on three pillars:
1. getting the data layer right: Agents are only as good as the data they work on. Without a unified view of the customer (Customer 360) and the product, implementing AI will only multiply the chaos.
2. Platformisation: Instead of building your own models from scratch, it is proving more efficient to use platforms that offer a ready-made framework for agents (‘Agentforce’), while ensuring security and regulatory compliance.
3 The evolution of leadership: the new reality requires the management of hybrid teams. The ability to define goals for agents, audit their work and design processes in which machine and human delegate tasks seamlessly becomes a key competence.
Garry Kasparov did not turn his back on technology after his defeat. He realised that the chess engine is not a game killer, but a powerful analytical tool that elevates the game.
In business, we are seeing an analogous process. The question being asked at board meetings today has evolved. It no longer reads: “Should we implement AI?”. It is, “How do we make it so that technology realistically extends our capabilities?”. The answer lies in smart integration and the understanding that in the economy of the future, the winners are not those who have the best AI, but those who can best work with it. Agentic AI is not the end of human work – it is the beginning of higher-value work.
