{
  "id": 15876406,
  "title": "Stop choosing the wrong Agentic AI use cases",
  "url": "https://urgent.news/2026/10/21/stop-choosing-the-wrong-agentic-ai-use-cases",
  "topic": "ai",
  "section": "AI",
  "published": "2026-10-21T10:19:41.000Z",
  "source": {
    "name": "TechRadar",
    "slug": "techradar",
    "url": "https://www.techradar.com/pro/stop-choosing-the-wrong-agentic-ai-use-cases"
  },
  "original_language": "en",
  "account": "The allure of agentic AI is akin to using a single hammer for every task, leading to a proliferation of potential use cases. However, this enthusiasm can result in misguided strategies. It's crucial to focus on the value before the agent. In the aftermath of ChatGPT, many enterprises hastily deployed AI tools without a clear purpose or desired outcome. This led to employees working differently without knowing if any improvements were made. McKinsey's 2025 survey revealed that only 39% of organizations reported any financial impact from their AI investments, despite widespread adoption. Organizations must establish clear metrics to measure both adoption and outcomes. While adoption indicates people are using AI, outcome measures the tangible results such as revenue, productivity, or efficiency gains. However, the true measure of success lies in the behavioral layer, where the integration of AI changes how people perform their jobs. Understanding the workflow is crucial as it determines the baseline measures and ensures the agent can perform the task correctly. Not every problem requires agentic AI; instead, simpler solutions like Robotic Process Automation (RPA) or process changes might be more suitable. The economics of deploying agentic AI are still evolving, but a strong business case is essential. Proving the value before building the AI system can save time and resources. Building a portfolio of use cases allows organizations to prioritize based on factors such as financial impact, feasibility, and risk. This approach creates a strategic plan rather than a linear queue of tasks. Budgeting for a \"transition mortgage\" is equally important, as it involves additional resources for training and change management. Centralized oversight of use cases can prevent duplication of effort and optimize resources. Ultimately, the success of agentic AI lies in identifying the right problems and proving the value through measurable outcomes.",
  "summary": "Why businesses must rethink agentic AI before scaling costly, ineffective use cases.",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}