Boosting Business Value with AI Agents: Strategies for Effective Implementation and Realistic Expectations

How to Increase Business Value with AI Agents

Agentic AI is gaining traction as a new class of AI solutions that can transform how organisations operate and compete. Unlike traditional automation bots or virtual assistants, AI agents shift decision-making from humans to machines, enabling automation of complex tasks and unlocking new business opportunities.

Despite the hype, many companies are still figuring out how to deploy AI agents effectively. A recent Gartner poll showed that over half of organisations are in the exploration phase, with only a small fraction moving into production. This gap between expectation and reality often stems from underestimating the challenges involved in scaling AI agents.

Mix and Match AI Agent Capabilities

AI agents are best suited for tasks that require understanding user intent, gathering data from multiple sources, and interacting with various applications. To get the most out of them, organisations need to adopt a flexible approach—combining different capabilities based on their specific needs.

This means configuring agents to work with the available data, integrating with existing tools and systems, and selecting appropriate large language models (LLMs). Customising agents in this way grounds them in the unique business context, which drives better outcomes.

Understand Limitations

Knowing what AI agents can and cannot do is crucial to avoid overpromising and underdelivering. One major limitation is their lack of a “world model”—an internal understanding of how the environment works that helps predict future outcomes.

Humans use mental models to interpret unexpected events and update their understanding. Current AI agents, mainly based on LLMs, rely on chat history and logs but don’t have this kind of dynamic learning. They identify patterns and probabilities but struggle with causal reasoning.

For example, in tasks like route planning where precision is critical, graph-based algorithms outperform LLMs. Also, AI agents are composite systems combining multiple AI techniques—such as forecasting, planning, and optimisation—that go beyond what LLMs can handle alone.

Given these constraints, it’s premature to rely solely on LLM-based agents for high-stakes decision-making.

Focus on Core Enterprise Components

Deploying AI agents at scale involves technical complexity and uncertainty. An agile approach is essential to quickly iterate, build trust, and stay adaptable as technologies evolve.

When developing AI agent frameworks, prioritise modular, “plug and play” components to avoid vendor lock-in. Instead of building everything in-house, look for vendor solutions that are open, interoperable, and ideally contribute to open-source AI agent ecosystems.

This approach reduces risk and accelerates time to value, allowing organisations to adapt their AI strategies as new capabilities emerge.

For IT professionals looking to deepen their understanding or skill set in AI agents and related technologies, exploring targeted courses and certifications can be a practical next step. Resources like Complete AI Training’s latest AI courses offer focused learning paths on AI tools, prompt engineering, and automation.