While companies worldwide rush to adopt agentic AI for efficiency, data indicates that nearly half of all deployments will fail or be dropped due to a lack of meaningful return on investment. However, this does not mean that companies should give up; agentic AI, which can automate entire processes and workflows, is indeed becoming critical to keeping pace in our fast-moving world.The bottom line is that those who successfully integrate agentic AI will be far ahead of those who don’t.Crossing this line from failure to success often comes down to thinking about both AI agents and return on investment in a vastly different and new way.Why Building a Single ‘Super Agent’ Often FailsIn many cases, companies deploying agentic AI focus on developing a few advanced agents who can do entire jobs, or significant parts of jobs, seeking to replace human employees. This is too big an ask and often fails. The key is to think smaller.Rather than striving to build an agent that can do an entire job, companies should seek to design agents that can do the rote tasks that bog down their employees: filling out forms, finding relevant info in a manual or contract, taking updated information from an email, and inputting it into an order system. These are the low-hanging fruit. But they are the ones that any company serious about agentic AI should start with, as they increase efficiency in ways that both employees and companies can immediately feel. Once these more basic tasks are automated, companies can then expand into pursuing automation of larger projects or entire processes.Even more importantly, each specific part of the task or process being automated, no matter how simple or complex, should have its own highly tuned agent. These multiple agents then work in a chain to complete the whole task. For example, in preparing a bid for a new project, one agent extracts data; another cross-references that data; a third categorizes it; a fourth writes a summary of the expected costs.Bigger and more complex tasks will require more agents. A real-world example of deploying multiple agents working together is insurance giant AIG, which has recently automated some of its most time-consuming underwriting procedures with AI agents. AIG uses 80 separate agents on each underwriting project, each with its own particular task.Giving one agent too many tasks inevitably results in a loss of transparency and quality control over the automated process, turning it into an unreliable black box that is impossible to reverse engineer, adjust, and understand, and truly integrate into larger workflows. However, when each agent has one focused assignment, knows how to stay in its lane and how to pass on and absorb information from other agents, they can all work together as a productive team while also allowing those humans that manage them to have transparency and control over the process, and override when needed. In other words, the secret sauce is figuring out how agents can work together, not in designing a super agent that aspires to do it all.Move Beyond the Chatbot Paradigm for Agentic AIChatbots based on Large Language Models have emerged as an amazing and helpful AI-powered tool in recent years, able to find, read, and make sense of the scattered, uncategorized data that dominates the business world.In the realm of customer service, especially, their assistance can save human agents massive amounts of time and headaches, contributing to better work environments and lower costs for companies. So it makes sense, to some extent, that many organizations implementing agentic AI think in the paradigm of chat, striving to replace chatbots that can retrieve data and answer questions with AI agents that can be commanded to perform tasks in addition to serving as sources of information.Although it may seem natural, this approach is actually unproductive when it comes to implementing agentic AI. The central value of agentic AI is that it can do things on its own, without constant human command and involvement. Sticking to a paradigm of chat severely limits this potential. Instead, organizations should consider designing agents that communicate with each other to accomplish tasks, thereby freeing up human resources to focus on truly creative endeavors, fostering relationships with other humans that advance the business, and making decisions that machines cannot.Chat will remain a critical tool, but it should not be essential to running agentic AI systems.Thinking Beyond Primary ROI and EfficiencyWhen it comes to automation, most organizations make ROI calculations that are limited to the cost of the new technology and its implementation, and evaluate how much money and time these investments will save them on the processes being automated, considering factors like the potential need for less human labor. In reality, this approach to evaluating ROI and efficiency is short-sighted and overlooks the true potential of agentic AI.When considering agentic AI, organizations need to think about the broader value it will unlock, not just the amount of time, money, and human labor saved. For example, insurers who have successfully automated underwriting with agentic AI are finding that, in addition to cutting costs for that process, they can vet and bring in new business — and more business — faster. With underwriting calculations now taking just a few hours, rather than weeks, insurers can quickly decide whether to take on a client, providing a positive answer much faster than their competitors.Similarly, businesses in heavily regulated but highly innovative consumer sectors, like food, chemicals, and pharmaceuticals, that incorporate agentic AI into their research and development process can deliver products to market faster and more quickly pivot products based on changing consumer demand. This ability to grow a business much faster than one’s competitors is far more valuable than the hours of human labor or the number of dollars that AI can save, and should be a central focus when thinking about the ROI of agentic AI.Agentic AI’s ultimate ability is to enable businesses to grow and their human teams to be more creative. That can only happen when organizations deploy AI agents to do the right things and don’t just measure but truly capitalize on the time, money, and human labor saved — looking not just for a return on investment but a leap forward.The post Stop Building Super Agents; Build Effective AI Teams Instead appeared first on The New Stack.