AI Empowerment for Websites - Essential Knowledge for Today's Digital Landscape
Get ready for the evolution of AI in 2025: Agentic AI is here! If you haven't heard the term yet, don't worry, you're not alone. Think of agentic AI as a super-charged version of AI, like the third iteration. This artificial intelligence technology mimics human thinking in its analysis and processes, relying on large language models, natural language models, and specialized software for writing and training machine learning algorithms.
Differentiating Agentic AI, RPA, and Generative AI
Agentic AI, which is often called "agents," operates autonomously, making decisions, and acting towards complex goals with minimal human supervision. They combine different technologies like large language models and machine learning. In contrast, Robotic Process Automation (RPA) automates repetitive, rule-based tasks using software bots, requiring human intervention for exceptions. Generative AI creates new content such as text, images, or code based on user prompts, leveraging deep learning to identify patterns in data. While Agentic AI is highly autonomous and decision-focused, RPA is task-oriented, and Gen AI excels in generating creative content based on user input.
These three types of AI can collaborate to enhance efficiency and decision-making in business processes. Agentic AI can handle complex decision-making tasks and adapt to dynamic environments, while RPA digital workers excel at automating repetitive, rule-based tasks.
Agents in action
When using agents, humans provide guardrails, outlining the types of activities the agent is authorized to perform. If the agent hits one of these boundaries, it notifies the user it cannot proceed, alerting a human coworker that it needs assistance. As the human works with the agent, those guardrails expand as the agent learns.
Agents take customer engagement to the next level
In customer service applications, agentic AI can help bots make decisions beyond a prescriptive matrix or flowchart. Because they are proactive, agents can "think," reason, and adapt to dynamic environments without the need for human direction. As a result, they improve their own thought processes through every iteration of problem-solving.
Adoption across industries
Early adopters of agentic AI include high-profile companies like IBM, Apple, Siemens, FedEx, Duke Energy, UPS, Tesla, Goldman Sachs, and PayPal. The technology is already being used in customer service applications, medical diagnosis and customized healthcare treatment solutions, process automation in business, supply chain optimization in manufacturing, financial and algorithmic trading, smart grids and energy management, self-driving vehicles, climate modeling and environmental protection, among others.
The impact on our websites (and other businesses)
Agentic AI takes efficiency to the next level, automating more complex tasks and relieving employees of monotonous routine tasks, enabling them to focus on higher-level, strategic, and meaningful work. It is scalable and flexible, allowing organizations to build it into customer-facing applications for a highly-customized experience or sophisticated help desks. Alternatively, it can be embedded behind the scenes in operations.
Ethical concerns and strategic implementation
As agentic AI continuously learns, adapts, and solves complex, dynamic, and changing scenarios, it has the potential to improve real-world problem-solving. However, like other emerging technologies, it requires careful implementation and consideration of ethical concerns beyond job displacement, such as potential misuse, unintended consequences, data bias, transparency, and data governance.
Starting with process identification and determining which specific process or functions are suitable for agentic AI is important in its strategic implementation. Business leaders should also determine organizational value and impact and find ways to evaluate and measure the technology's benefits. Companies may also want to seek external experts to ensure successful implementation. Beyond the technical feasibility, there are other considerations such as data security.
As we move forward with agentic AI, it is crucial for our websites and other business leaders to remain open to how this technology can best serve their organizations. Whether they are ready or not, the exciting world of agentic AI awaits.
- In the evolution of AI by 2025, agentic AI will be a game-changer, favoring a super-charged version of AI, similar to its third iteration.
- Agentic AI, often referred to as 'agents', are autonomous, offering decisions and pursuing complex goals with minimal human supervision, essentially combining technologies like large language models and machine learning.
- While agentic AI excels in decision-making and adapting to dynamic environments, Robotic Process Automation (RPA) focuses on automating repetitive rule-based tasks, requiring human intervention for exceptions.
- Generative AI, on the other hand, creates new content, such as text, images, or code, based on user prompts, utilizing deep learning to identify data patterns.
- Collaboration between these three AI forms can boost efficiency and decision-making in business operations, with agentic AI handling complex tasks and RPA boosting efficiency in repetitive tasks.
- By using agentic AI, businesses can allocate employees to higher-level tasks, such as strategic and meaningful work, and integrate the technology into customer-facing applications or sophisticated help desks.
- As agentic AI is adopted across industries, from customer service and healthcare to finance and technology, ethical concerns, strategic implementation, and data security need to be carefully considered for successful implementation and a positive impact on the business.
