Today, we’re thrilled to sit down with Aisha Amaira, a MarTech expert with a deep passion for integrating cutting-edge technology into business operations. With her extensive background in CRM marketing technology and customer data platforms, Aisha has a unique perspective on how innovations like Agentic AI are transforming the way companies operate, streamline processes, and uncover critical insights. In this conversation, we dive into the differences between traditional automation and Agentic AI, exploring their capabilities, the value they bring to businesses, and how leaders can navigate the shift to more autonomous systems.
How would you describe traditional automation in a business context, and what are some everyday examples companies rely on?
Traditional automation is all about using technology to handle repetitive, predictable tasks without much human involvement. It’s built on rules and predefined steps, so it excels at consistency. Think of things like Robotic Process Automation, or RPA, where bots take care of data entry or invoice processing. There are also workflow tools that automatically route approvals or send notifications based on specific triggers. Even simple scripts or macros that run the same task over and over—like generating reports—fall into this category. These tools save time and cut down on errors, but they’re pretty rigid since they can only do exactly what they’re programmed for.
What are the biggest strengths of traditional automation when it comes to saving time or reducing costs for businesses?
The real power of traditional automation lies in its ability to speed up routine work and minimize mistakes. Tasks that used to take hours, like processing forms or updating records, can be done in minutes with almost no errors. This directly cuts operational costs since you need fewer people tied up in manual work. It also delivers predictable results, which makes it easier to measure efficiency gains. For businesses, this means faster turnaround times and more resources freed up for other priorities.
How does Agentic AI stand out from traditional automation in the way it operates?
Agentic AI takes things to a whole new level by shifting from just following instructions to actually understanding goals and working toward them. Unlike traditional automation, which sticks to a script, Agentic AI can plan out multi-step processes, adapt to unexpected changes, and even interact with different systems or people on its own. It’s less about “do this specific task” and more about “achieve this outcome, figure out how.” That adaptability and autonomy make it a game-changer for handling complex or dynamic situations.
Can you share an example of a task where traditional automation hits a wall, but Agentic AI shines?
Sure, let’s look at customer support. With traditional automation, you might have a chatbot that answers basic FAQs using canned responses. Anything outside its script—like a nuanced complaint—gets escalated to a human agent. Agentic AI, on the other hand, can triage tickets, dig into customer history, resolve common issues independently, update the CRM, and even suggest follow-up actions. It’s not just reacting; it’s thinking through the problem and acting proactively, which cuts down delays and improves the customer experience.
In terms of adaptability, why is Agentic AI such a big leap forward compared to traditional automation?
Adaptability is where Agentic AI really pulls ahead. Traditional automation is static—it can’t learn or adjust unless someone reprograms it. If something unexpected happens, it just stops or fails. Agentic AI, however, learns from data and feedback over time, so it gets better at what it does. It can tweak its approach on the fly if conditions change, whether that’s a new policy, a system glitch, or an unusual customer request. This means it can keep delivering results even in messy, real-world scenarios where traditional tools would need constant human fixes.
What does autonomy mean when we talk about Agentic AI, and how does that compare to traditional automation?
Autonomy in Agentic AI means it can make decisions and take actions without someone holding its hand. It evaluates its environment, figures out what needs to be done to meet a goal, and executes those steps—only looping in humans when something exceeds its set limits. Traditional automation has zero autonomy; it’s entirely dependent on explicit instructions. If there’s a deviation or exception, it’s stuck until a person steps in. Agentic AI turns automation into something like a self-driving system, handling complexity and interdependencies on its own.
How does Agentic AI create value for businesses beyond just efficiency or cost savings?
While traditional automation focuses on speeding things up and cutting costs, Agentic AI amplifies impact across the board. It optimizes entire processes from start to finish, spotting and fixing bottlenecks before they slow things down. It’s also scalable, so it can handle growing complexity without needing more staff. On top of that, it provides strategic insights by analyzing trends and predicting outcomes, which helps with better decision-making. And it frees up employees to focus on creative or high-value work, like innovation or building customer relationships, rather than mundane tasks.
What’s a real-world scenario where Agentic AI clearly outperforms traditional automation?
Let’s take IT operations as an example. With traditional automation, you might have scripts that restart a server or send an alert when a threshold is crossed. That’s helpful, but it’s purely reactive. Agentic AI goes further—it analyzes system logs in real time, predicts potential outages before they happen, applies fixes based on past solutions, and documents everything automatically. If there’s an odd issue it can’t resolve, it escalates with detailed context. This proactive approach prevents downtime and saves a ton of headaches compared to waiting for something to break.
For business leaders curious about adopting Agentic AI, what’s the best way to start exploring this technology?
The first step is to pinpoint processes in your organization that have high impact but also high friction—think bottlenecks, repetitive approvals, or multi-step workflows prone to errors. From there, I’d recommend starting small with pilot projects. Test Agentic AI in a controlled way, maybe running it in parallel with human decisions to see how it performs. Set clear goals and boundaries, like policies or compliance rules, to keep things in check. It’s also key to track results—look at cycle times, error rates, and even employee feedback. This gradual approach lets you build confidence in the tech without overcommitting upfront.
Looking ahead, what’s your forecast for the role of Agentic AI in shaping the future of business operations?
I think Agentic AI is going to redefine how businesses operate over the next decade. It’s not just an upgrade—it’s a complete shift toward self-managing systems that can scale without piling on resources. Companies that adopt it will break through growth barriers, end persistent bottlenecks, and gain real-time intelligence from every process. Employees will spend less time on grunt work and more on strategic, creative contributions. On the flip side, businesses that stick only to traditional automation might hit a ceiling—fast and reliable, but capped in what they can achieve. I see Agentic AI becoming the backbone of smarter, more agile operations across industries.