Artificial intelligence is evolving rapidly, and one of the most significant developments on the horizon is the rise of autonomous AI agents. Unlike traditional software that follows a predefined workflow, these systems can make independent decisions, execute multi-step processes, and collaborate with other tools to achieve outcomes. The potential applications span everything from IT operations to business strategy, yet very few companies have successfully adopted them at scale.
The gap between promise and reality isn’t only about technical limitations. The deeper issue is organizational readiness. Most businesses are not yet structured, equipped, or disciplined enough to support AI agents in a way that delivers real value.
Why Many Companies Aren’t Ready Yet
Early experiments have highlighted recurring challenges. Data quality is often unreliable, which undermines the ability of agents to act with confidence. Business processes are another stumbling block; inefficient workflows cannot simply be “fixed” by layering advanced technology on top. If the underlying system is flawed, AI agents will only accelerate the inefficiency.
This reality reinforces an important point: AI agents are not silver bullets. They can only perform well when they are supported by clean data, well-designed processes, and organizational structures that allow for transparency and oversight.
Building a Foundation for Agentic AI
To prepare for adoption, businesses need to focus first on understanding their current state. This involves three critical areas:
- Processes – Tools like process mining help organizations identify how work is really being done, uncovering inefficiencies and deviations that need to be addressed before automation can succeed.
- Technology – A clear inventory of applications and their dependencies is essential. Agents will rely on this infrastructure to function effectively, so gaps and redundancies must be eliminated.
- People – Employees need more than training; they need visibility into how systems are used, where bottlenecks occur, and how agents will interact with them. Digital adoption platforms can bridge this gap by both tracking usage and easing the transition to new workflows.
Even with highly capable AI systems, human oversight will remain critical. Agents can take on tasks, but people must guide, correct, and approve decisions, especially where context or judgment is required.
Managing AI Agents as Part of the Business
Once agents are introduced, they become part of the operational landscape. Companies will need new practices for monitoring and managing them, including:
- Maintaining an agent inventory that details their functions, the processes they serve, and the data they use.
- Identifying high-value use cases through ongoing analysis, recognizing that opportunities will evolve as the technology matures.
- Measuring effectiveness to ensure agents deliver measurable improvements in efficiency, customer outcomes, or revenue.
- Adapting workforce skills as certain functions are automated, shifting recruitment, training, and role design to complement the strengths of AI.
Steps to Take Now
Preparing for agentic AI doesn’t have to wait until the technology matures further. Organizations can begin today by:
- Cataloguing their IT systems and mapping business processes to create visibility.
- Establishing governance rules for how agents are built, approved, and monitored.
- Investing in AI literacy so employees understand the potential and limitations of the tools they will soon work alongside.
Looking Ahead
The excitement surrounding agentic AI is well-deserved, but so is the caution. These systems are powerful, but without preparation they can amplify weaknesses instead of strengths. Companies that take the time now to refine their processes, modernize their technology stack, and build governance structures will be better positioned to unlock the real benefits when the tools are ready for broader deployment.
Agentic AI is coming—and while its capabilities will grow over time, the foundation for success must be laid today.
