As artificial intelligence (AI) becomes more common in business, companies are starting to question whether every task truly needs AI or if they should focus on areas where AI can make the most difference. Gregg Aldana, Senior Vice President and Head of Global Solutions Consulting at Appian, suggests that businesses should begin by identifying the specific problems they need to solve and the areas with the most inefficiencies, rather than trying to find places where AI can be used. A key consideration is whether a task follows predictable rules with a clear outcome. In such cases, traditional automation or business rules may be more effective and less expensive than AI. AI is more valuable when tasks require adaptive reasoning, understanding complex situations, or dealing with unpredictable inputs. For example, an AI system could analyze customer claims from web forms, mobile apps, and emails to determine which department should handle each request, combining structured data with unstructured content. However, AI also comes with hidden costs beyond the initial development of the model. These include expenses related to data collection, system integration, ongoing monitoring, governance, security, and managing unexpected situations. Operational infrastructure is necessary to ensure AI models function safely and effectively. Businesses must also consider accountability—understanding why a decision was made, being able to trace what happened if something goes wrong, and having a clear way to handle situations an AI can't confidently manage. In high-stakes areas, such as finance, insurance, healthcare, and government, human judgment remains essential. AI can speed up processes like customer onboarding or clinical trials, but decisions with serious consequences should involve human oversight. The future of AI in business is likely to involve a mix of people, AI agents, and traditional automation, with each component handling the parts of a process it is best suited for. Introducing AI into processes that were once handled by simple rules can sometimes add unnecessary delays, uncertainty, and administrative burden without providing much benefit. Companies should be selective about where they use AI, ensuring it genuinely improves the process. Before implementing AI, businesses should focus on identifying the problems they need to solve, rather than just looking for places to use AI. They should pinpoint the biggest challenges, understand which decisions can be improved, and determine where human oversight is needed. When AI systems move from making recommendations to actually making decisions, it becomes crucial to define clear boundaries for their actions. AI agents should be allowed to operate within specific guidelines and risk parameters. Human oversight is still important for handling exceptions, unusual cases, and high-risk decisions. Ultimately, businesses should measure the success of AI by the results it delivers—such as faster processes, lower costs, or better decision-making—rather than just counting how many processes have been "AI-enabled." A successful AI strategy focuses on making processes measurably better, rather than trying to apply AI everywhere.