By Jessica Davies  •  December 18, 2024  •

Ivy Liu

For a while, 2025 was shaping up to look like a period where everyone (at least in the business world) took a collective breath and set an AI path that was less driven by hype and threats of “don’t adopt at your peril,” and more anchored to assessing the ROI. 

But it was only a matter of time. 

Digital tech has always been susceptible to buzzwords, and now generative AI has bedded in as mainstream vocabulary, it’s being shoulder barged out of the way by the latest term du jour: “agentic AI.”

So if you’ve seen the term increasingly pop up but aren’t fully sure what it is, or even if you need to know – you’re not alone. Here’s an explainer. 

What exactly is agentic AI, and why is it suddenly being discussed?

It refers to a type of AI that is designed to take independent action, rather than just responding to commands given by humans. It was coined by Andrew Ng, a renowned AI researcher, this June. Big tech companies and vendors have begun to push it more aggressively and the end goal is for these so-called “AI agents” to carry out complex tasks on behalf of humans, but with minimal human oversight. The belief is that it will lead to better organizational efficiency and employee productivity.

How does it dovetail with generative AI? 

To understand it fully you need to think about all types of AI and what they do, at their core.

So-called traditional AI completes tasks based on predefined rules and patterns (set by humans.) It is not adaptable and doesn’t learn or improve from new data (think calculators, logic-based chess programs, or a chatbot with scripted responses.) Then you have machine learning (ML) – a subset of AI – which involves training a model using large datasets to identify patterns and make decisions. It is adaptable and improves as it processes more data. Generative AI is a subfield of ML. Generative AI uses the data from large language models to create new content, such as text, images, music or code. It’s like the more creative kind of AI versus ML’s analytical. 

Now, we have agentic AI, which takes all the components of traditional and generative AI and uses it to anticipate needs autonomously and execute against them. And it can adapt dynamically, learning from experiences and adjusting future actions based on new information. It can also be proactive, rather than waiting purely on human commands like other forms of AI.

Give an example of what an AI agent can do for an HR practitioner.

Like generative AI, agentic is being touted as an employee productivity enhancer by automating dull and time-consuming routine tasks, freeing people up to focus on more strategic-type work. “It’s important HR leaders understand the impact of agentic AI on workforce performance and well-being because it introduces new dynamics to the employee experience and how work is managed and executed,” said Jay Patel, svp and general manager of Webex Customer Experience Solutions at telco giant Cisco.

Be more specific, let’s have an example.

Take learning and development. Agentic AI can benefit career development for employees by offering more individually tailored training and development programs. 

This is how Jill Goldstein, global managing partner for HR and talent transformation at IBM Consulting, sums it up:  Traditional AI will recommend training courses based on an employee’s role and past performance. Generative AI can develop custom learning modules, quizzes, and interact with the content based on the specific needs of the employee agenda. Agentic AI manages that employee’s personal learning plan and continually mines for more information. It adjusts the individual’s learning path based on the new information that’s available and provides real-time performance, data, and feedback that then funnels back into the AI so it helps that employee advance their skills. “So the first one [traditional AI/ML] executes, generative AI helps to customize the content, and the agentic AI builds on both of that and helps me problem solve and actually produces the outcomes associated with it,” she added.

It should also free up HR teams from their own time-consuming administrative tasks, so they can give more attention to more strategic planning around employee engagement, personalized support and productivity. For instance, automating responses to common HR-related inquiries like leave policies, payroll information, or training opportunities. Cisco claims that HR professionals can save an estimated 2.5 hours a week.

To read the full article over on WorkLife click here

https://digiday.com/?p=563729