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The precise impact of this new generation of AI agents will vary from business to business. In some cases, it will mean building specialized agents for specific tasks within larger processes, such as making a journal entry, drafting an RFP, or responding to customer inquiries. Meanwhile in others, it may involve deploying fully autonomous agents capable of orchestrating an entire process from end to end. An example here is in the areas of compliance, where agents could identify data needed to review compliance against a set of standards, identify gaps, report on them and even remediate issues without any required human involvement to complete the task.
Yet, what’s true across the board is that these agentic systems will be transformative, freeing up workers from repetitive tasks, rapidly assessing market risks and opportunities, and giving leaders connected and real-time data to sharpen their decision-making. And they can do it all with limited or no direct human intervention.
Start to end game
It won’t just happen though. Successfully integrating agentic AI into operations requires a fundamental shift in organizations’ overall strategies and mindsets. Whereas with generative AI (GenAI) tools many leaders have focused on individual task augmentation within areas like customer service and product development, agentic AI gives them the chance to be far more revolutionary. Rather than looking at a process and asking, “Where can I use AI to improve it?” they can achieve an even greater impact by completely redesigning the process using agentic AI from start to finish.
Customer churn analysis and prevention is a great example — not the least because it’s an issue that almost every organization can relate to. Traditionally, this is a process requiring multiple teams across multiple areas of the business, including data engineers, data scientists, marketing analysts and executives. It also involves various steps — from data exercises like identifying at-risk customers and analyzing any trends to business and marketing programs like designing intervention strategies, executing targeted campaigns and monitoring effectiveness.
With agentic AI, the whole process could be executed by just five agents, with each responsible for a different step and all working autonomously yet cohesively together. This not only improves efficiency but ensures real-time, data-driven decision-making. And it’s completely replicable on an ongoing basis too.