The challenge of building a successful agentic AI strategy is not about chasing bold promises but about delivering measurable value without exposing the business to unnecessary risk. While firms like Gartner, Accenture, and KPMG highlight the transformative potential of agentic AI, the reality is more complex. Many organisations are discovering that poorly planned initiatives fail quickly, with over 40% of projects expected to be cancelled due to unclear value, rising costs, or weak governance.
One major risk is “agent washing”, where vendors rebrand existing tools as agentic systems. In practice, fewer than a small fraction of solutions deliver true autonomy, leading to unrealistic expectations and failed pilots. At the same time, costs can escalate rapidly. Unlike traditional AI tools, agentic systems operate continuously, consuming large volumes of compute resources through APIs from providers such as OpenAI, Google, and Anthropic. As usage scales, so do expenses, often faster than anticipated.
Another challenge is unpredictability. AI agents are non-deterministic, meaning identical inputs can produce different outputs. This complicates testing, compliance, and reliability, especially in high-stakes environments. When combined with poorly defined prompts or misaligned goals, this unpredictability can lead to cascading failures across systems. Data security is also a concern, as many deployments rely on external cloud-based models, raising questions around privacy, control, and regulatory compliance.
To mitigate these risks, organisations must take a disciplined approach. Start with real business problems rather than ambitious transformation goals. Focus on workflows that are repetitive, high-cost, and predictable, where agentic AI can deliver clear improvements. Implement strong governance from the outset, including human oversight, monitoring, and cost controls.
Scaling should only occur after proving measurable ROI in limited use cases. Metrics such as reduced costs, faster cycle times, and improved accuracy provide tangible evidence of value.
Ultimately, agentic AI is both a powerful opportunity and a strategic risk. Companies that succeed will not be those that move fastest but those that move deliberately, building systems that are controlled, measurable, and aligned with real business outcomes.
Quelle:
https://www.zdnet.com/article/building-an-agentic-ai-strategy-that-pays-off/
