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How to increase business value with AI agents
To overcome the complexity of deploying agentic AI at scale, organisations must mix and match capabilities, understand key limitations, and take an agile approach to implementation
There is tremendous hype around agentic artificial intelligence (AI), representing the latest wave of solutions pushing the boundaries of what AI can do and how it can transform organisations to operate and compete.
AI agents promise to enhance resource efficiency, automate complex tasks and introduce new business innovations, beyond the capabilities of scripted automation bots and virtual assistants.
What sets it apart is a fundamental power shift – moving decision-making from humans to machines. This lays the foundation for why agentic AI holds business value and why it has the potential to reshape entire industries.
According to a January 2025 Gartner poll of 3,412 webinar attendees, 53% said their organisation is in exploration mode with agentic AI, 25% in piloting mode and only 6% in production mode. In the same poll, 40% plan to pursue initiatives in the next 6 months.
This surge in interest in AI agents also comes with diverse interpretation and unrealistic expectations. As large language models (LLMs) rapidly evolve, many organisations are building AI agents on top of them. While these solutions can gather and synthesise information, interact with applications and complete tasks, they often struggle with enterprise contextualised decision-making.
The gap between hype and operational reality remains substantial. This can blind organisations to the real cost and complexity of deploying AI agents at scale, stalling projects from moving into production.
Organisations must cut through the noise, discerning the truths, myths and implications of AI agents. The focus should be on the essential enterprise components needed to build or use AI agents that increase business value.
Mix and match AI agent capabilities
AI agents are a good option for organisations that need AI solutions capable of understanding user intent, retrieving and processing information from various data sources, and leveraging tools to complete tasks.
Organisations must take a flexible approach, by mixing and matching the right capabilities to suit their use case. This means configuring or building agents based on the data available, the tools and systems they need to interact with, and the LLM capabilities required. This level of customisation brings agents closer to the business context, increasing the value they can deliver.
Understand limitations
Organisations must understand the limitations of AI agents to unlock their full business value. Doing so not only guides an implementation, but manages stakeholder expectations around scope, performance and impact.
One key limitation is the absence of critical components like world models, which is what allows AI agents to build an internal understanding of their environment and predict outcomes.
As human beings, we interpret how this world works via internal or abstract representations. For example, if a child sees apples falling from the trees several times, they will be able to predict how an apple falls next time. When we see something unusual or unexpected, such as a floating apple in the air, we will try to verify and may need to update our mental model or world state.
This active learning process is extremely important for AI agents to “understand” context, and update or improve as necessary. Current memory components of LLM-based AI agents are usually based on chat history and system logs. However, these cannot fully capture and store the dynamics of the agent itself, the environment or the world.
LLM-based AI agents also learn from data distribution, identifying correlations and probabilities rather than causations. This makes them not the most optical AI technique to use. For example, graph-based algorithms still outperform LLMs in areas like route planning, where accuracy and efficiency are critical.
It’s also important to recognise that an AI agent is not the same as an AI model. An agent is a composite AI-enabled system that combines multiple techniques to perceive, reason and act. Capabilities such as predictions or forecasting, planning and optimisation sit outside the strengths of LLMs and are better handled by other AI techniques.
These limitations are critical for organisations to understand as we’re still a long way from being able to entrust LLM-based agents with critical decision-making.
Focus on core enterprise components
Given the high uncertainty, technical complexity and rapid pace of AI agent development, taking an agile approach is essential. It will help organisations minimise latency from input to outcome, build trust and brand loyalty, and stay adaptable as the technology and market continue to evolve.
When building AI agent frameworks or solutions, consider “plug and play” components, technologies or models to avoid vendor lock-in. It isn’t recommended to build extensive frameworks and tools in-house. Instead, prioritise vendor solutions that are open and interoperable, or actively leveraging and contributing to open-source AI agent technology stacks.
Ben Yan is a senior director analyst at Gartner focused on data and analytics
