Understanding AI: What It Is, How It Works, & Why It Needs Oversight

Artificial Intelligence (AI) is no longer a futuristic concept; it is a reality. It’s already reshaping how we live, work, and interact with technology. From voice assistants and personalized ads to self-driving cars and automated customer support, AI is quietly becoming a core part of our digital environment. But while AI is increasingly common, understanding how it works and how it's evolving isn’t always easy. This article breaks down what AI is, explores its various forms, and introduces a growing area of interest - Agentic AI, a type of AI that can make decisions and act with greater independence. We’ll also examine why organizations need to assess AI risks and how governance frameworks can support this process.

What Is AI?

AI refers to computer systems that perform tasks that typically require human intelligence. These systems can:

• Understand and process language
• Recognize images or voices
• Solve problems
• Learn from experience

At its core, AI is about enabling machines to analyze information, make decisions, and, in some cases, take action. But not all AI systems are built the same. Some are designed to handle specific tasks, while others (still theoretical) aim to mimic broad human intelligence.

Types of AI: Capability and Function

Not all AI systems are alike. Some are built to handle a single, specific task, while others (still theoretical) are imagined to match or exceed human intelligence.

By Capability: How Intelligent Is the AI?

Capability categories define the level of advancement and adaptability of an AI system. Essentially, how broadly it can apply what it knows. These categories help distinguish between basic tools that follow instructions and hypothetical systems that could think and reason like humans.

The three main capability categories are:

• Narrow AI: Designed for one specific task, such as filtering spam or recommending movies.
• General AI: A theoretical system that could learn, reason, and perform any intellectual task a human can.
• Superintelligent AI: A speculative form of AI that could surpass human intelligence in all areas, including logic, creativity, and decision-making.

These categories reflect the scope of an AI system’s intelligence, not its design, but what it can do. They help clarify how capable or adaptable a system is, ranging from simple, task-specific tools to hypothetical models that could exceed human intelligence. The table below summarizes the three main capability categories:

By Functionality: How the AI Operates

AI can also be categorized by its functions, specifically how it processes information, learns, and interacts with its environment:

• Reactive Machines: The most basic type of AI. They respond to current inputs but can’t learn from past experiences. These systems make decisions in real time based on pre-programmed rules. A well-known example is IBM’s Deep Blue, the chess computer that could evaluate possible moves but had no memory of previous games.
• Limited Memory AI: Can learn from historical data to make better decisions over time. These systems are widely used today in technologies like self-driving cars, fraud detection software, and customer service chatbots. Their learning is task-specific and short-term, whereby they can recall recent inputs but don’t build a long-term understanding.
• Theory of Mind AI: Still in the experimental stage. It refers to systems that could interpret human emotions, intentions, and social cues, essentially allowing machines to better understand how people think and feel. This level of AI would be essential for emotionally aware applications, such as robotic caregivers or therapeutic assistants.
• Self-Aware AI: Purely speculative. This category envisions machines that possess consciousness, self-awareness, and a sense of identity. Such systems do not exist, and many experts question whether creating them is possible, or even desirable.

What Is Agentic AI?

Agentic AI is an emerging and important development in the evolution of artificial intelligence. While most current AI systems are reactive, designed to respond to specific commands or inputs, agentic AI introduces a new level of autonomy. These systems are not just programmed to wait for instructions; they can take initiative, set goals, plan how to achieve them, and act independently to reach outcomes.

Think of the difference this way: a traditional AI answers your question when asked. An agentic AI anticipates what you need, develops a strategy to meet that need, and carries it out, often without step-by-step guidance.

Agentic AI systems are designed to operate with a higher degree of independence. They combine decision-making, learning, and goal setting in a way that begins to resemble human-like initiative. Instead of simply executing a task, they manage a process. For example, rather than helping you book a flight when prompted, an agentic AI might plan your entire trip based on your calendar, preferences, and past behavior, and handle changes as they arise.

Core Traits of Agentic AI

Agentic systems typically demonstrate a few key characteristics:

• Goal-oriented behavior: These systems are designed around outcomes. They don’t just complete tasks, but they work toward defined objectives.
• Autonomy: Agentic AI can operate with minimal user involvement, managing decisions and actions on its own.
• Adaptability: It can respond to changing information or conditions and adjust its plans accordingly.
• Outcome-based decision-making: Rather than executing fixed instructions, agentic AI selects actions based on how effectively they support the end goal.

Examples of Agentic AI in Use

Although still emerging, agentic AI is beginning to appear in consumer and enterprise tools:

• Travel planning assistants that not only suggest destinations, but also book flights, hotels, and activities based on budget, schedule, and preferences.
• Smart virtual assistants that manage calendars, prioritize meetings, send follow-up emails, and reschedule when conflicts arise, without direct prompts for each action.
• End-to-end customer service bots that diagnose a problem, take steps to resolve it, and confirm resolution, all without escalating to a human agent.

Why Agentic AI Matters

The rise of agentic AI signals a shift in how we interact with technology and the extent to which we delegate control to it. These systems blur the line between tool and teammate, automating not just tasks but entire processes.

For everyday users, this means greater convenience:

• Routine tasks can be fully offloaded
• Tools become proactive rather than reactive
• Systems start to anticipate needs rather than wait for instructions

However, greater autonomy also brings new challenges.

• Privacy: Because agentic systems often need access to a wide range of personal or contextual data to function well, privacy concerns grow more complex.
• Trust: Users must decide how much decision-making power they’re comfortable handing over. Transparency in how agentic AI reaches conclusions is essential.
• Bias and Safety: Like all AI, agentic systems are only as good as the data they’re trained on. Poorly trained or monitored systems may produce biased or harmful outcomes, especially when left to act without oversight.

In an environment where AI is evolving faster than regulation, adopting a proactive, structured approach to AI governance isn't just good practice. It's essential for long-term success and trust.

AI Risk and Governance Frameworks: What Organizations Should Know

As artificial intelligence becomes increasingly embedded in business operations, organizations must look beyond performance and innovation to address the risks that accompany it. Whether you're developing AI solutions internally or relying on vendors that use AI to store, process, or analyze sensitive data, you need a clear framework to evaluate and govern how those systems are used.

AI presents unique challenges that differ from traditional IT systems. These challenges are often complex and evolving, including:

• Data privacy and security: AI systems frequently rely on large volumes of personal or proprietary data, increasing the potential impact of a breach or misuse.
• Bias and fairness: Without careful design and oversight, AI can perpetuate or even amplify discrimination and bias embedded in training data.
• Transparency and accountability: Many AI systems operate as "black boxes," making it difficult to explain how decisions are made or who is responsible for their outcomes.
• Regulatory compliance: As governments introduce new AI-specific laws and data regulations, businesses must prepare to demonstrate responsible use and documentation.

To address these risks, organizations are turning to AI governance frameworks, which are structured models that provide practical tools, guiding principles, and assessment criteria. These frameworks help ensure that AI systems are not only technically effective but also aligned with ethical standards, legal obligations, and organizational values.

Using a governance framework allows your team to:

• Establish policies for responsible AI development and deployment
• Evaluate both internal and third-party AI systems before adoption
• Build trust with customers, partners, and regulators by demonstrating accountability
• Stay ahead of emerging compliance requirements and avoid reputational damage

Examples of Common Frameworks

NIST AI Risk Management Framework (AI RMF)

• Developed by the U.S. National Institute of Standards and Technology
• Focuses on trustworthiness, safety, and accountability
• Applies to both in-house AI and third-party tools

OECD AI Principles

• Global principles focusing on human rights, transparency, and responsible use
• Useful for policy-level and ethics-based evaluations

ISO/IEC 42001 (In Development)

• An upcoming international standard for AI management
• Emphasizes governance, documentation, and risk control

EU AI Ethics Guidelines and Regional Guidance

• Many regions offer local guidance focused on fairness, non-discrimination, and explainability
• These may soon influence laws and regulations

How to Use These Frameworks

Organizations can apply these tools to:

• Evaluate internal AI systems for safety, fairness, and transparency
• Vet third-party vendors that handle sensitive customer or company data
• Create internal policies for AI use and oversight
• Prepare for upcoming audits or compliance requirements

Final Remarks

AI is no longer a concept to prepare for; it is here. It’s already part of how we work, live, and make decisions. Most systems in use today are still narrow and task-specific, but we’re seeing a shift. Agentic AI, with its ability to act independently and manage goals, represents a new level of capability. That shift brings new opportunities, but also new responsibilities.

For individuals, knowing the basics (what different types of AI do, how they function, and how agentic AI fits into the picture) can help you make better use of these tools and understand where they may fall short. For organizations, the responsibility is greater. It’s not just about keeping up with new technology; it’s about using it responsibly, in ways that are safe, fair, and consistent with your values and legal responsibilities.

That’s where governance frameworks come in. They provide a structured approach to assessing risk, establishing clear policies, and ensuring accountability, whether you're developing AI internally or relying on external tools. This is especially important when third-party vendors are involved, particularly those that store, process, or transmit your organization’s sensitive or mission-critical data. In these cases, their practices directly impact your risk exposure. Governance frameworks help you evaluate not only your own systems but also those of your suppliers, ensuring that everyone handling your data meets the same standards. These frameworks aren’t just about meeting compliance requirements; they’re key to building trust with customers, regulators, and your own team.

As AI becomes more autonomous and deeply embedded in systems we rely on, thoughtful oversight isn’t optional. It’s a necessary part of managing both risk and impact. Understanding how AI works, where it’s headed, and how to govern its use isn’t just useful, it’s essential.