Agentic AI vs. Traditional AI - KYP.ai | Productivity Intelligence

Agentic AI: What Business Leaders Need to Know

Agentic Artificial Intelligence (AI) is transforming the business landscape. Many of us are trying to understand agentic AI for business cases that bring value. Essentially, we are trying to fly the plane while building it simultaneously.

Time is ticking, though.

Gartner shares, “By 2028, 33% of enterprise software applications will include agentic AI, enabling 15% of day-to-day work decisions to be made autonomously.”

Agentic AI systems can make their own decisions. In contrast, traditional AI is best for analyzing large amounts of data, recognizing patterns, and detecting anomalies.

Below, we share key indicators to help you decide whether to deploy agentic AI or use traditional AI. Let’s review the steps to consider to ensure that you are driving toward the most compatible AI for each enterprise use case.

Agentic AI vs. Traditional AI: What’s the difference?

Agentic AI is artificial intelligence that can think, plan, and act autonomously to achieve goals. It makes decisions independently (without human intervention) and dynamically adapts to changing conditions. It actively engages with and learns from experiences, allowing it to improve its methods continuously.
Most people are familiar with traditional AI, which follows specific rules to analyze large datasets. It uses historical data to make decisions or predictions based on patterns learned from past information.

Visual of agentic vs traditional AI differences.

Machine Learning, a subset of AI, helps computers learn from data, helping AI improve tasks without explicit programming. It instantly processes vast amounts of data, recognizes patterns, and makes predictions or decisions (e.g., a streaming service recommending shows to watch using shows you’ve watched previously). Conventional AI systems rely heavily on large datasets and predefined algorithms. They perform specific tasks but often lack flexibility in dynamic situations. Traditional AI still relies on people to make the final decisions.

When to use Agentic AI

Agentic AI systems operate autonomously, making independent decisions based on real-time data analysis. Unlike conventional AI models requiring specific prompts or instructions, agentic AI can initiate actions and adapt to changing conditions without explicit human input. This autonomy means AI Agents can address dynamic challenges and proactively execute tasks without continuous prompting.

For example, a Process Optimization Agent powered by agentic AI would analyze real-time process data to identify inefficiencies, discover workflow improvements, and automatically implement optimizations as they are identified. This differs from conventional AI, where humans must still decide what and when to implement any uncovered optimization.

Another scenario for agentic AI is a Budget Management Agent, which assists companies in developing, overseeing, and modifying budgets. This AI agent automates expense tracking, forecasts future financial trends, and generates financial reports. Continuously adding new data points helps build a deeper understanding over time. This data can work with other AI tools to automate invoice reconciliation and accounts payable processes, improving operational efficiency throughout the business.

In contrast, conventional AI is currently used for budget management tasks like data entry, report generation, and forecast prediction. These tasks require human input for prompting, model training and retraining, and providing additional context, data, or goals to ensure accurate outputs.

Conventional AI is also well-suited for big data analysis, particularly in processing large, structured datasets. It excels at tasks where users perform repetitive activities with structured data.

Agentic AI excels in scenarios that require problem-solving without predefined rules or specific prompts.

Tools like KYP.ai identify traditional AI and agentic AI use cases. For example, they can use Intelligent Document Processing (IDP) to extract invoice data and Robotic Process Automation (RPA) for order fulfillment. An AI Agent can help with RFP preparation, and Generative AI can assist with email responses, among other tasks.

Agentic AI in Manufacturing

Manufacturing is at the forefront of Agentic AI use cases. For decades, the industry has been integrating different forms of automation and AI, from robots that perform manual labor to computer vision systems that identify defects. As Agentic AI continues to evolve, several key differences emerge between conventional and Agentic AI.

With traditional AI, a process engineer uses AI to determine the root cause of a defect or system failure. To pinpoint patterns, the engineer prompts AI to analyze large datasets from production processes, equipment documentation, and event logs. Ultimately, the process engineer decides on the necessary action to address (and ideally prevent) the problem—whether to keep the production line running, knowing it will break, prioritize product output, or shut down immediately to fix the issue.

In contrast, agentic AI can make decisions autonomously based on its analyses, such as adjusting machine settings or initiating maintenance requests in real-time. This level of autonomy reduces the reliance on human decision-making and accelerates response times to emerging issues. By integrating agentic AI into manufacturing maintenance, companies can achieve more responsive, data-driven operations, which improves productivity and lowers operational costs.

Agentic AI in Banking and Finance

AI has long been utilized in banking to detect fraud. A machine learning model uses past transaction data to find unusual patterns. These patterns can include large transaction amounts, new vendors, or many small charges. Such signs may suggest fraudulent activity.
Traditional AI systems flag suspicious transactions based on predefined thresholds and historical patterns. When new fraud indicators arise, humans need to manually update the model or dataset to ensure it remains current. Once a transaction is flagged, a bank employee and possibly the customer are contacted to address the situation.
In contrast, an agentic AI system continuously monitors transactions in real-time, not only detecting fraud patterns but also acting autonomously searching for new fraud techniques to stay ahead of new threats. Instead of just flagging suspicious transactions like traditional AI, agentic AI investigates, reasons, and acts in real-time. Here, agentic AI autonomously mitigates risks, adapts strategies, and optimizes prevention measures.
Rather than following fixed actions—such as “If X happens (e.g., a transaction greater than $1,000), do Y (automatically approve or decline a phone call to the cardholder)”—agentic AI evaluates multiple potential strategies and selects the most effective one. For example, imagine a transaction that appears to be fraudulent; agentic AI quickly simulates various intervention strategies (such as blocking the transaction, implementing multi-factor authentication, or delaying settlement) and predicts the potential outcomes before taking action. Using contextual reasoning, agentic AI can determine the appropriate course of action.

Agentic AI represents a significant advancement from traditional AI systems. Its transformative potential enables businesses to leverage AI for more than routine tasks, sparking a new wave of innovation and efficiency in various industries. It signifies a broader shift towards more intelligent, autonomous systems that address complex problems with minimal human intervention.

Agentic AI in other industries

Retail

Retailers benefit from AI-driven personalization strategies. By customizing customer experiences, agentic AI enhances engagement and boosts sales. These tailored interactions lead to higher consumer satisfaction and better retention rates. In healthcare, AI solutions are being used for diagnosis and treatment planning.

Healthcare

Agentic AI helps interpret medical data quickly and accurately, improving patient outcomes. These advancements showcase AI’s potential in life-saving applications.

Logistics

In logistics, agentic AI optimizes route planning and supply chain management. It predicts delivery delays, automates warehouse operations, and dynamically adjusts transportation schedules. This proactive approach significantly improves efficiency, cost savings, and on-time delivery performance.

How do you find a valuable AI business case?

Enterprises are increasingly exploring valuable use cases for agentic AI. Similar to the recent boom in generative AI, companies are looking to identify specific, data-driven scenarios that extend beyond generic applications, such as AI customer service agents.
Agentic AI can play a crucial role in discovering these valuable use cases. Companies like KYP.ai help identify compatible patterns for implementing agentic AI effectively. Processes that involve multiple steps or decision points are particularly well-suited for this type of AI, especially in dynamic situations that change over time.
For example, AI can respond to real-time changes in supply chain optimization. It can handle inventory updates, shipping delays, or changes in demand, allowing the process to adjust automatically. By coordinating workflows, agentic AI ensures seamless execution and maintains context awareness without requiring constant human oversight.

What are the challenges?

Implementing agentic AI presents several challenges despite its advantages. Firstly, it is a relatively new concept; agentic AI emerged around 2024, so we are still exploring its capabilities and limitations. There is no straightforward, plug-and-play solution, as a successful application requires hyper-personalization and vast amounts of data.

Like other types of AI, agentic AI raises ethical concerns and potential biases. Since it makes autonomous decisions, it can be challenging to understand the reasoning behind those decisions. Ensuring these decisions are ethical, fair, and unbiased is a big challenge, especially with autonomous actions.

Accountability is another critical concern, particularly if something goes wrong or if the system makes an unjustifiable decision. Data security and integration with legacy technology systems are also significant issues, especially in regulated industries. Agentic AI uses sensitive data, raising concerns among leadership even within secure, closed-loop systems.

Organizations are creating AI boards to mitigate these concerns. To successfully deploy agentic AI, organizations should integrate change management best practices. They must also understand the technical requirements and support necessary for this process. While there are challenges and risks, the overall promise of agentic AI makes it a valuable investment. PWC predicts AI will boost the global economy by $15.7 trillion by 2030. This growth will mainly come from productivity gains driven by AI innovation.

Despite the challenges, investing in agentic AI can provide significant benefits that enhance efficiency, foster innovation, and create a competitive advantage. By adopting agentic AI, organizations can uncover new opportunities to improve their operations, products, and services, positioning themselves for long-term success.

Agentic AI also excels in scenarios that require problem-solving without predefined rules or prompts. For example, it can add an extra layer of cybersecurity by detecting unusual network behavior, identifying potential threats, and autonomously determining a response. Some models can even learning new tactics to prevent future breaches.

When aiming for deep personalization beyond basic rule-based automation, an HR agent can autonomously guide new employees through the onboarding process. This includes customizing training modules, answering questions in real-time, and adjusting the learning path based on each employee’s progress and specific role requirements. The applications of agentic AI differ from those of typical automation or conventional AI, which often focus on repetitive tasks—such as end-to-end payments and order processing—or content creation, like presentation decks or SEO writing.

These traditional automation approaches tend to be rule-based, ensuring that standard operating procedures (SOPs) are documented, tracked, and compliant. Large language models (LLMs) are commonly used for straightforward data-driven responses. At the same time, generative AI (GenAI) analyzes various sources (both open and proprietary) to provide contextual insights across a wide range of prompts and scenarios.

How to implement agentic AI

Agentic AI is a catalyst for business innovation, enabling previously unattainable breakthroughs. Implementing agentic AI requires a well-thought-out approach combining the right technology, data, and governance. Begin by clearly defining the goals you wish to achieve, such as improving efficiency, reducing errors, or enhancing customer experiences.

Next, find possible use cases by creating a pilot project or proof of concept. This will help you test agentic AI in a controlled setting. This step allows you to refine your approach and demonstrate value before a full-scale rollout.

The rollout of agentic AI should involve diverse stakeholders, each playing a critical role in ensuring that the system is developed, deployed, and maintained effectively. The collaboration of executive leadership for strategic alignment and operational teams for real-world implementation is essential to maximize the success and impact of agentic AI within your organization.

KYP.ai helps enterprises identify agentic AI (or other automation and AI) use cases by capturing and mining operational data to find tasks compatible for automation.

Their algorithms identify precise use cases based on proprietary data collected about the workforce, business processes, and systems. Unlike OpenAI sources, KYP.ai secures data in a closed network that is accessible only to your employees. This approach ensures that your model is trained solely on your data, protecting all proprietary and sensitive information or trade secrets.

Next steps

Agentic AI marks a significant advancement over traditional AI systems, offering transformative potential for businesses. It enables autonomous decision-making, continuous learning, and dynamic adaptation to changing conditions, which enhances efficiency, fosters innovation, and creates a competitive advantage.

The potential benefits of agentic AI, such as managing challenges around biases and ethics, make it a valuable investment. Organizations can uncover new opportunities to improve their operations, products, and services by adopting agentic AI, ultimately positioning themselves for long-term success.