A staggering 40% of enterprise applications will integrate task-specific AI agents, according to a prediction by Gartner. This marks a key milestone in the history of Agentic AI systems. But as the technology develops, we need innovators to drive it forward and showcase its capabilities.
Ivo Bozukov has been tracking this shift closely. Having spent over 12 years previously leading businesses at Forum Energy Technologies, he saw firsthand how emerging technologies reshape entire industries – and believes AI will do the same for enterprise software.
The Rapid Evolution of Agentic AI Systems
Agentic AI refers to the development of artificial intelligence systems that can perform specific tasks autonomously, leveraging machine learning algorithms to learn from data and improve their decision-making capabilities. The rapid evolution of Agentic AI systems has been driven by advances in computing power, data storage, and the increasing availability of high-quality training data.
Companies like IBM, Microsoft, and Google have been at the forefront of Agentic AI research, investing heavily in the development of AI-powered solutions that can augment human capabilities. For instance, IBM’s Watson platform has been successfully deployed in various industries, such as the medical field and finance, to improve decision-making and drive business outcomes.
How the Relationship Between Humans and Technology is Changing in the Workplace
As Agentic AI becomes more pervasive in the workplace, it’s changing the way humans interact with technology. According to experts in AI adoption, technology delivers only around 20% of an initiative’s value, with the remaining 80% dependent on human factors such as change management, process re-engineering, and skills development. This emphasises the requirement for organisazions to focus on developing a symbiotic relationship between humans and technology, where AI systems augment human capabilities rather than replacing them.
Ivo Bozukov highlighted this in a LinkedIn post last year explaining that “a new generation of AI agents could take that transformation much further.” For example, Accenture’s AI-powered customer service platform has been made to work in unison with human customer support agents, which enables them to provide more personalised support to their customers.
Challenges Organizations Face in Scaling Agentic AI
The potential of Agentic AI is evident, but businesses face big problems in scaling systems that use it. As Ivaylo Bozoukov puts it, “what’s striking isn’t how many companies are using AI, but how few are seeing real returns from it.”
One of the main problems is the need for detailed training data, which can take time to collect, not to mention be expensive. Agentic AI systems require a lot of resources, which is a barrier for businesses who have limited IT infrastructure in place.
The lack of standardisation in Agentic AI development can make it tricky for companies to integrate these systems with their current infrastructure. In order to surpass these issues, companies such as Ernst & Young have created dedicated AI labs. In these labs, they test Agentic AI solutions in a controlled environment, before rolling them out at scale.
The rise of Agentic AI in 2026 is set to continue, which will enable organizations to automate tasks, improve their decision-making, and drive business outcomes. To fully realise the potential of Agentic AI, businesses must confront the obstacles associated with scaling these systems. These include the need for high-quality training data, computational resources, and standardisation in development. By doing so, they can unlock the full value of Agentic AI and create a future where humans and technology work together to drive business success.
