Although several technology providers claim to have agentic AI capabilities in their products, the term “agentic” AI is still ill-defined. That’s why, aside from a grounding definition, leaders need to distinguish between flexible (agentic) and rigid (predefined) workflows, being aware of the early stage of agentic AI and the importance of personal experimentation to understand the difference. Peter Gostev, Head of AI at Moonpig, joined me on “What’s the BUZZ?” to discuss a pragmatic approach with agentic AI.
Comparing Agentic AI and Traditional Workflows
It’s easy to get swept away by the latest buzzwords. One of the most pressing distinctions to understand is the difference between truly agentic AI and systems that merely follow predefined workflows. Simply said, Agentic AI can make autonomous decisions and flexibly respond to varying contexts while traditional workflows automate tasks but lack the intelligence to self-adjust.
Vendors may advertise their solutions as "agentic," but upon closer inspection, they often reveal predictable patterns similar to yesterday's tools. This is crucial for leaders to assess: Does the solution simply replicate existing workflows, or does it offer genuine agency? Take a moment to engage with the technology. Are the AI systems genuinely autonomous in their decision-making? By actively evaluating these differences, you can avoid falling for inflated claims and focus on solutions that truly deliver value.
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Participating in the Early Stage of Agentic AI Development
Agentic AI is still in its formative stages. While terms like "agentic" are quickly becoming buzzwords in the tech industry, many companies are just starting to innovate with these tools. As a leader, you should approach the topic with a healthy dose of skepticism. You might hear promises about the capabilities of the latest AI innovations, but remember that many are in their early maturity stages and adoption is ramping up.
» [Is it a use case agentic?] Is the use case defined within one specific workflow with just small deviations or maybe there's like three ways you can go. And does it have freedom to go and do things in the more open-ended space. «
— Peter Gostev
Instead of rushing to adopt the newest technologies, look for established use cases demonstrating real success, such as research or certain coding tasks. By grounding your expectations in reality, you’ll make more informed decisions that will pay off in the long run. Don’t hesitate to explore these developments and weigh the benefits of a deliberate approach versus just being part of the hype.
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A foundational understanding of the technology and concepts and hands-on experimentation are important. While information overload is common, relying solely on third-party reports or vendor claims can lead you astray. Instead, make it a priority to test these AI systems personally. This approach not only builds your understanding but also allows you to develop your intuition about what works and what doesn't. (Vendors like n8n or Make.com offer platforms that you can even use without coding.)
When you experiment, you gain direct experience with the technologies actively shaping your industry. This personal engagement helps demystify AI, revealing capabilities beyond the headlines.
By incorporating experimentation into your decision-making processes, you also position yourself to be an informed leader who can effectively sift through the noise. Ultimately, the only way to truly grasp the potential of agentic AI is to dive in and engage with it directly.
Summary
To avoid being misled by AI's promises, understand the difference between true agentic AI and streamlined workflows, recognize the early development stage of agentic AI applications, and engage with the technology first-hand. By doing so, you'll enhance your discernment and become an effective leader in AI adoption, ultimately paving the way for informed choices. Once you process this information, take the next step by identifying AI solutions in your organization that lend themselves to experimentation.
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