New developments in artificial intelligence (AI) are constantly popping up. Just last year, generative AI took the world by storm. Now, agentic AI is the latest trend.
This has piqued the interest of the majority of business leaders across industries — likely due to the increasing pressure they feel from management to be more productive than ever.
Agentic AI vs. Automation Explained
- Automation: Automation is a program that executes pre-defined rule-based tasks automatically. It’s a fit for simple “if-this-then-that” business tasks, such as sending an email after a customer fills out a form.
- Agentic AI: Agentic AI is designed to perform non-deterministic tasks autonomously with very minimal human oversight. It can adapt to new variables and simulate human reasoning. It’s best used for open-ended tasks that require flexibility and reasoning.
While I’m all for technological advancements that optimize operational efficiency, there’s one glaring issue: Most executives don’t fully understand what agentic AI is, how it differs from other solutions on the market and which cases it’s better suited for. Even worse, many of the trending agentic AI solutions sold today are promising revolutionary outcomes and are actually just automation disguised as AI.
Adopting AI Doesn’t Always Deliver on ROI
Better productivity means better profit, and getting the right tools in your arsenal is key for achieving both. The tools you and your team use, however, need to give your organization good ROI in order to be worthwhile.
Nearly 75 percent of companies are struggling to show tangible ROI for AI investments. Some may argue that measuring ROI on AI is difficult, but this disconnect is more likely due to the result of choosing the wrong solution for your organization’s needs.
While there’s a lot of excitement behind emerging AI solutions, it doesn’t mean that the tools will automatically drive desirable business outcomes. In fact, it can cause unnecessary spending with not much to show in terms of return. This is why fully understanding the use cases, benefits and drawbacks of the technology you’re adopting and how it can impact your workforce is critical.
Agentic AI vs. Automation: What’s the Difference?
Agentic AI and automation are very different in terms of their nature, application, use cases — and many people conflate the two. Both can be used to enhance productivity, efficiency, and ROI; understanding their differences is the critical first step in any organization’s AI journey.
Automation
Automation has been around far longer than generative and agentic applications. At its most basic level, automation is a program that executes pre-defined rule-based tasks automatically.
This is powerful in circumstances where fast, reliable outcomes are necessary to move the needle on business goals. “If this, then that” scenarios are ideal for automation — meaning that if a specific stimulus happens, a specific action follows. For instance, a customer fills out a lead form on your website, and they are automatically sent an email confirming that you’ve received their information and will contact them soon.
For example, as a lead moves through your sales pipeline in your CRM, there are certain things that need to happen as their status changes: notifying the right team members, sending a document, following up via email, etc. In these situations, you can use automation to do these tasks for you and, in turn, reduce manual tasks, save time and improve consistency. AI could cause unnecessary work and inefficiencies in these situations because it’s natural-language based, which means it’s more likely to make a mistake and has less traceability. Automation is easier to use and trace the outcomes of a business event that triggered an automation back to the source.
It’s important to recognize that automation tools are limited to the tasks they have been programmed to perform. It’s not ideal for adapting to new scenarios or managing more complex responsibilities. For instance, while it may notify a company about a new lead, automation tools are not able to take this a step further by interacting with the lead and answering questions they might have or automatically updating company documents with this information.
Agentic AI
AI agents vastly differ from automation: they are designed to perform non-deterministic tasks autonomously with very minimal human oversight. Unlike automation, AI agents can adapt to new variables, simulate human-like behavior and reasoning and continue to progress and become more sophisticated as these models interact with humans.
I would describe agentic AI as having four core characteristics: complex, autonomous, flexible and natural language proficiency. It can have a multi-step nature of reasoning, making it beneficial for complex situations where more flexibility and open-ended reasoning is needed. For instance, following prompts submitted by a customer to help them troubleshoot a problem, following instructions and following prompts that compound on previous prompts.
I like to think of AI more as augmented intelligence. As humans we’re used to clicking buttons and working in a structured manner, but this process can be improved with intelligence. For example, if you need to send an email to a lead who submitted a form on your website, AI can help improve that email by adding personalization, proofreading and editing for clarity/tone.
As with any other solution, there are downsides to AI agents that executives need to be cognizant of. Unlike automation which is highly reliable, AI agents tend to be more unpredictable and require extensive training to avoid undesirable outcomes or hallucinations. For instance, in the example of updating information on leads, if not trained properly, AI agents can share inaccurate information with the team.
Automations are prescriptive, whereas AI agents are assistive and, in the future, predictive.
How to Determine the Right Approach
Agentic AI is a powerful tool, but it doesn’t mean that it's right for every organization and use case. In fact, a lot of executives are struggling to show AI ROI because they’re taking very simple processes that are ideal for automation and are instead using agentic solutions. This takes away valuable company resources for training and maintaining these agentic solutions for use cases that aren’t really moving the productivity needle.
For many businesses, the need for automation is simply about increasing efficiency in specific tasks — such as document generation, contract management or e-Signatures — where the process doesn’t require the complexity or adaptability of AI. If your team’s primary bottleneck is administrative overhead, automation can provide immediate ROI, without the need for an AI solution.
That’s why executives need to ask the right questions to determine if their use case is a fit for agentic AI, automation, or both. There are two key questions you need to ask when considering AI versus automation:
- Does this process need human-like intelligence and reasoning to interact with customers, or does it fall into the “if-this-then-that” category? AI is ideal for the former and automation for the latter.
- What is the priority and ideal outcome? AI is great for saving time and increasing speed, and automation is great for cases where speed/time, compliance, data quality, and employee productivity/satisfaction are the priority.
AI is in the midst of a hype cycle, and it can be difficult for organizations to determine what makes sense for their business. It’s important to understand that ROI can and should be clear when choosing the right solution for your company.
Before making the investment, organizations must carefully consider what use cases are ideal for AI versus automation, and how these solutions will impact them internally and externally to determine which approach will help to achieve their specific goals.
