AI Agents have aided businesses for several years. These modular systems are driven and enabled by Large Language Models (LLMs) and Large Instruction Models (LIMs) for task-specific automation. A Large Instruction Model starts as an LLM but gets fine-tuned with thousands (or millions) of instruction-response examples.
Later, generative AI became positioned to further develop AI Agents and provide the foundation for new developments. These advances included AI agents evolving tool integration, prompt engineering, and reasoning enhancements.
The future state is said to be, by many insiders, Agentic AI systems. In contrast to AI Agents these represent a paradigm shift marked by multi-agent collaboration, dynamic task decomposition, persistent memory, and coordinated autonomy. To what extent will businesses rush to adopt Agentic AI?
In terms of the predictive wave, Digital Journal heard from the software firm Certinia Chief Product & Technology Officer Raju Malhotra. These consider the rise of hybrid human-agent teams and the agentic AI’s role in killing billable hours.
Agentic AI Gives Rise to a Hybrid Human-Agent Model
Looking to the future, Malhotra says: “Agentic AI is freeing services firms from relying strictly on human knowledge workers. Next year, we will see a massive increase in hybrid human-agent teams in the sector. Agents help supercharge their human counterparts, allowing businesses to take on more new business and accelerate their delivery.”
The impact on human resources will be considerable, observes Malhotra: “Almost all human workflows within the services industry will be transformed by this model eventually. In 2026, the biggest impact will be on high-volume, standardized processes that today consume precious time. This includes project planning, resource allocation, contract compliance, time and expense capture, reconciliations. Human workers will be liberated to focus on higher-value advisory and client engagement.”
Yet how can businesses fully take advantage of this emergent technology? Malhotra predicts: “To maximize results from this new model, services organizations will focus on skills arbitrage. In the past, services firms had to determine if they had enough people to take on client work. Now, they need to figure out how to balance human and agent skills. Organizations will do this by prioritizing governance of hybrid teams, cultural adoption of agents, and reskilling consultants toward higher-order problem solving.”
Potential growth areas for Agentic AI deployments include research automation, robotic coordination, and medical decision support.
Bye, Bye Billable Hours: Outcome-Based Billing Takes Hold
Outcome-based billing is a pricing model where customers are charged based on the results or value delivered by a product or service, rather than a fixed fee. In other words, outcome-based monetisation models place the focus on business outcomes, where the customer only pays for a predefined tangible business outcome or value realised from services consumed. This is the focus of Malhotra’s second prediction. The process is an example of an “anything as a service (XaaS)” offering.
Outlining the advantages, Malhotra explains: “Services firms traditionally relied on the billable hour to charge clients, but the billable hour model will become increasingly irrelevant in 2026 thanks to agentic AI. It will be replaced by outcome-based billing. Agentic AI systems don’t just automate tasks. They operate with defined goals, seek and interpret data contextually, and take action dynamically.”
As to the practical applications, Malhotra clarifies: “This allows services firms to eliminate manual work and scope, staff, manage, and deliver projects with a level of precision and predictability that aligns naturally with value-based models. Instead of selling hours, firms will sell outcomes, while AI agents continuously track progress, optimise resourcing, and adapt in real time. This means that services firms will be measured on their business impact rather than their input.”
